Reviewerly/exHarmony huggingface dataset:
Helping Data Science Students Develop Task Modularity

This paper explores the skills needed to be a data scientist.Specifically, we report on a mixed method study of a project-based data science class, where we evaluated student effectiveness with respect to dividing a project into appropriately sized modular tasks, which we termed task modularity.Our results suggest that while data science students can appreciate the value of task modularity, they struggle to achieve effective task modularity.As a first step, based our study, we identified six task decomposition best practices.However, these best practices do not fully address this gap of how to enable data science students to effectively use task modularity.We note that while computer science/information system programs typically teach modularity (e.g., the decomposition process and abstraction), and there remains a need identify a corresponding model to that used for computer science / information system students, to teach modularity to data science students.
Generating Optimal Privacy-Protection Mechanisms via Machine Learning

We consider the problem of obfuscating sensitive information while preserving utility. Given that an analytical solution is often not feasible because of un-scalability and because the background knowledge may be too complicated to determine, we propose an approach based on machine learning, inspired by the GANs (Generative Adversarial Networks) paradigm. The idea is to set up two nets: the generator, that tries to produce an optimal obfuscation mechanism to protect the data, and the classifier, that tries to de-obfuscate the data. By letting the two nets compete against each other, the mechanism improves its degree of protection, until an equilibrium is reached. We apply our method to the case of location privacy, and we perform experiments on synthetic data and on real data from the Gowalla dataset. We evaluate the privacy of the mechanism not only by its capacity to defeat the classificator, but also in terms of the Bayes error, which represents the strongest possible adversary. We compare the privacy-utility tradeoff of our method with that of the planar Laplace mechanism used in geo-indistinguishability, showing favorable results.
Mobile Phone Usage Among Senior High and Technical School Students in Ghana and Its Impact on Academic Outcomes – A Case Study

There are currently over 7.6 billion mobile phone connections in the world, which is approximately equivalent to the world’s population; as if to say that everyone in the world is hooked up to mobile telephony. By the next decade, it is estimated that the number of mobile phone subscribers would increase by a billion with 90% of this growth coming from developing countries. This exponential growth coupled with its ubiquity highlights its essentiality in all spheres of human endeavour. Despite the fact that many sectors in developing countries worldwide are embracing its usage as an effective tool, the Ghana Education Service (GES) has placed a ban on the use of mobile phones in senior high schools, technical and vocational institutions. Bearing in mind that most of these institutions provide boarding facilities for their students for long periods of time – spanning over several months; the teaching and learning experience is denied access to mobile phones over this long period. This study administered a survey to determine whether this denial has any impact on educational outcomes and what the impact would be if done otherwise. Using variations of selective and random sampling methods on a sample size of 150 respondents from the Nkoranza municipality, results from the study showed that the use of mobile phones in the Ghana educational system will have a positive impact on the teaching and learning process. Teachers and students would have easy access to up-to-date educational materials. However, the study recommends that for this to be effective and sustainable, proper usage supervision must be provided especially for students in order to avoid negative influence that stem from some social media.
Narrative Analysis of Institutional Traps of Education and Science in Russia

For last 30 years Russian science and education system has been experiencing constant, profound institutional transformation in order to increase the quality of education, it's access ability and with a point to encrease the effectiveness of science. This article is devoted to the attempt to analysis the results of undertaken reforms and evaluating the quality of economic institutions which coordinate the order of market agents of researching field. The analysis of narratives approach was chosen with the intention for estimating the quality of reforms undertaken. Narratives are defined to be stories in direct speech of actors themselves of researching field: professors, students, Master’s and graduated students of all forms of education, as soon as scientists and researchers of universities and research institutes and different levels of responsibility and competencies administrators. The Integrum database taken as the main source of information for searching for narratives. This informational base consists of 1250 Russian newspapers (federal and regional) and 500 journals which help us to select 250 narratives (stories in direct speech) of people who are directly involved in transformational process of education system and science being studied. The obtained results pointed out the number of stable ineffective rules and practices which have a negative impact on our researching area functioning and also made it possible to classify out a few groups of institutional traps such as: metric's trap, trap of raising level of bureacracy, trap of shortage of finansing, electronization and digitalization trap and trap of staff capacity. Each determined trap reflected in the article is also pointed out and argued with selected narratives.
Time Series Simulation by Conditional Generative Adversarial Net

Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper
Developing a Predictive Model on Assessing Children in Conflict with the Law and Children at Risk: A Case in the Philippines

Lawful protection and the right to live is a common term used to most of the citizen in a society. Protecting children is a major area in the society needed to execute. So, this study aims to utilize data mining techniques in extracting hidden patterns that can be used to craft a policy that will lessen the children in conflict with the law and children at risk and enforce the preventive measure. This study aims to develop a model is using the dataset provided by the social welfare and check the predictive performance of the different algorithm, like a Decision tree, Naïve Bayes, General Linear model, and Logistic Regression. Using RapidMiner as a tool to cross-validate and measure the performance of each model and Tableau for the data visualization of the data. This study found out that the Naïve Bayes algorithm is the appropriate model for prediction for having a 92.65% accuracy result and 7.35% classification error. However, the Naïve Bayes algorithm garners 2.001 seconds in model building time. Further, this study found that at the age 15-17 years also children committed a heinous crime and at the age of 12 - 17 year old many are victims of maltreatment.
Digital Doctors and Robot Receptionists

Advances in artificial intelligence offer the promise of accessibility, precision, and personalized care in health settings. However, growth in technology has not translated to commensurate growth in automation of healthcare facilities. To gain a better understanding of user psychology behind the acceptance of automation in clinics, a 3 (Role: Receptionist, Nurse, Doctor) x 3 (Digital Agent Representation: Human, Avatar, Robot) factorial experiment (N = 283) was conducted. Results suggest that the digital nature of the interaction overpowers any individual role effects, with acceptance depending upon individual traits (belief in machine heuristic; power usage). Implications for theory and the design of digital healthcare facilities are discussed.
Structure-Aware Adaptive Diffusion for Video Saliency Detection

This paper proposes a novel saliency model that reveals the long-term info to boost detection accuracy.The saliency estimation of conventional methods heavily depends on the locally revealed shortterm info, and they could easily be trapped into imperfect configurations.In contrast, our method can take full consideration of common consistency of those reliable low-level predictions from the perspective of the entire video sequence.Meanwhile, we adopt a newly designed self-learning strategy which is guided by the low-rank analysis to adaptively reveal the long-term spatial-temporal video coherency.To avoid the error accumulations, we also propose a novel non-local descriptor to enhance the discriminative power of the feature space.Thus, the newly revealed the long-term info can be directly regarded as a trustful indicator to sustain additional low-rank analysis, which would serve as the basis toward selective fusion and significantly enhance the detection accuracy.
KEFST: a knowledge extraction framework using finite-state transducers

Purpose The purpose of this research study is to extract and identify named entities from Hadith literature. Named entity recognition (NER) refers to the identification of the named entities in a computer readable text having an annotation of categorization tags for information extraction. NER is an active research area in information management and information retrieval systems. NER serves as a baseline for machines to understand the context of a given content and helps in knowledge extraction. Although NER is considered as a solved task in major languages such as English, in languages such as Urdu, NER is still a challenging task. Moreover, NER depends on the language and domain of study; thus, it is gaining the attention of researchers in different domains. Design/methodology/approach This paper proposes a knowledge extraction framework using finite-state transducers (FSTs) – KEFST – to extract the named entities. KEFST consists of five steps: content extraction, tokenization, part of speech tagging, multi-word detection and NER. An extensive empirical analysis using the data corpus of Urdu translation of Sahih Al-Bukhari , a widely known hadith book, reveals that the proposed method effectively recognizes the entities to obtain better results. Findings The significant performance in terms of f-measure, precision and recall validates that the proposed model outperforms the existing methods for NER in the relevant literature. Originality/value This research is novel in this regard that no previous work is proposed in the Urdu language to extract named entities using FSTs and no previous work is proposed for Urdu hadith data NER.
A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs

By enabling correct differentiation in stochasticcomputation graphs (SCGs), the infinitely differentiable Monte-Carlo estimator (DiCE) can generate correct estimates for the higher order gradients that arise in, e.g., multi-agent reinforcement learning and meta-learning. However, the baseline term in DiCE that serves as a control variate for reducing variance applies only to first order gradient estimation, limiting the utility of higherorder gradient estimates. To improve the sampleefficiency of DiCE, we propose a new baseline term for higher order gradient estimation. This term may be easily included in the objective, and produces unbiased variance -reduced estimators under (automatic) differentiation, without affecting the estimate of the objective itself or of the first order gradient estimate. It reuses the same baseline function (e.g., the state-value function in reinforcement learning) already used for the first order baseline. We provide theoretical analysis and numerical evaluations of this new baseline, which demonstrate that it can dramatically reduce the variance of DiCE’s second order gradient estimators and also show empirically that it reduces the variance of third and fourth order gradients. This computational tool can be easily used to estimate higher order gradients with unprecedentedefficiency and simplicity wherever automatic differentiation is utilised, and it has the potential tounlock applications of higher order gradients in reinforcement learning and meta-learning.
Performance of Evaluation for AES with ECC in Cloud Environment

During the day; Technology is Cloud, where people can share resources, services and information across the Internet.Due to information on the Internet, security is considered a major problem.The information should be protected by an unauthorized user and should be sent to a person with a private and confidential intent.On this proposed work, to provide a secure method, secure connection, authentication, confidentiality and thirdparty data protection in cloud computing.In the combination of the Advanced Encryption Standard (AES) and Cryptographic Curve Cryptography (ECC) analyzing of different parameters like storage, encryption time, decryption time and correlation.The results show that the impact of this integrated approach is more important than the other secure algorithms.
A novel hybrid publication recommendation system using compound information

Publication recommendation is an interesting but challenging research problem. Most existing studies only use partial information of papers' contents, reference network or co-author relationship, which leads to an unsatisfied recommendation result. In this study, we propose a novel hybrid publication recommendation approach using compound information which retrieves top-K most relevant papers from a publication depository for a set of user input keywords. Our advantages comparing to the existing methods include: (1) Reaching a better recommendation results by taking the advantages of both content-based recommendation and citation-based recommendation and exploring much richer information of papers in one method; (2) Effectively solving the cold-start problem for new published papers by considering the vitality of papers and the impact factor of venues into the citation network; (3) Saving a large overhead in calculating the content-based similarity between papers and user input keywords by doing paper clustering based on the citation network. Extensive experiments on DBLP and Microsoft Academic datasets demonstrate that PubTeller improves the state-of-the-art methods with 4% in Precision and 4.5% in Recall.
AUTOMATIC GLANDS SEGMENTATION IN HISTOLOGICAL IMAGES OBTAINED BY ENDOSCOPIC BIOPSY FROM VARIOUS PARTS OF THE COLON

Artificial intelligence is rapidly gaining ground in online detection, endoscopic and morphological characterization of colon epithelial neoplasms. Even for pathologists identification of metaplasia and dysplasia in the epithelium of the mucous glands could be an extremely difficult task. The same task in vivo, directly during the endoscopic examination is no less difficult, therefore the development of auxiliary mathematical models for image recognition is requested.
The Remote Process Support for Collaborative Work

The modern time is characterized not only by the rapid development of fundamental science and its mathematization but also by the development of its applied branches, the emerging trend of transition to a digital society. A special place is occupied by innovative technologies in applied industries. Modern processes and trends in the development of human society require fast and effective collaboration of managers and the use of IT management for decision-making. The article proposes a model of multi-agent information system, built on the basis of the simplest expert system. The purpose of the model is to provide remote collaboration with specified performance indicators. This model is designed to solve problems in the field of financial and budgetary process and uses elements of remote control of joint work. The expert system is presented as an organizational mechanism for automated solution of the problem of remote control of joint work. As usual, a knowledge base is a set of facts and inference rules. The article defines a number of indicators, including those affecting the adaptation of personnel to the requirements of the environment in remote control of joint work. The authors used a multi-agent model and a model of knowledge representation in the expert system. The synthesis of these models made it possible to develop an integrated model of remote control of joint work, ensuring the achievement of the planned performance indicators.
Diversifying Future-Making Through Itinerative Design

“Designed in California” is a brand statement used by high-tech manufacturers to denote provenance and cachet of digital innovation and modernity. In this paper, we explore philosophically alternate design perspectives to those this statement embodies, reporting and reflecting on a long-term multi-sited project that seeks to diversify future-making by engaging communities of “emergent” users in “developing” regions. We argue that digital technologies are typically created with a design lens firmly focused on “first world” populations, assuming a base set of cultural norms, resource availabilities, and technological experience levels that do not strongly align with those of emergent users. We discuss and argue for inclusive technology design methods, present our approach, and detail indicative results and case studies as an example of the potential of these perspectives in uncovering radical innovations. Distilling findings and lessons learned, we present a methodology—itinerative design—that pivots between emergent user communities across multiple regions, driving digital innovation through the periphery of mainstream design’s current remit.
Optimizing N-Tier Application Scalability in the Cloud

An effective cloud computing environment requires both good performance and high efficiency of computing resources. Through extensive experiments using a representative n-tier benchmark application (Rice University Bulletin Board System (RUBBoS)), we show that the soft resource allocation (e.g., thread pool size and database connection pool size) in component servers has a significant impact on the overall system performance, especially at high system utilization scenarios. Concretely, the same software resource allocation can be a good setting in one hardware configuration and then becomes an either under- or over-allocation in a slightly different hardware configuration, causing a significant performance drop. We have also observed some interesting phenomena that were caused by the non-trivial dependencies between the soft resources of servers in different tiers. For instance, the thread pool size in an Apache web server can limit the total number of concurrent requests to the downstream servers, which surprisingly decreases the Central Processing Unit (CPU) utilization of the Clustered Java Database Connectivity (C-JDBC) clustering middleware as the workload increases. To provide a globally optimal (or near-optimal) soft resource allocation of each tier in the system, we propose a practical iterative solution approach by combining a soft resource aware queuing network model and the fine-grained measurement data of every component server. Our results show that to truly scale complex distributed systems such as n-tier web applications with expected performance in the cloud, we need to carefully manage soft resource allocation in the system.
Effects of Volumetric Augmented Reality Displays on Human Depth Judgments

Many driving scenarios involve correctly perceiving road elements in depth and manually responding as appropriate. Of late, augmented reality (AR) head-up displays (HUDs) have been explored to assist drivers in identifying road elements, by using a myriad of AR interface designs that include world-fixed graphics perceptually placed in the forward driving scene. Volumetric AR HUDs purportedly offer increased accuracy of distance perception through natural presentation of oculomotor cues as compared to traditional HUDs. In this article, the authors quantify participant performance matching virtual objects to real-world counterparts at egocentric distances of 7-12 meters while using both volumetric and fixed-focal plane AR HUDs. The authors found the volumetric HUD to be associated with faster and more accurate depth judgements at far distance, and that participants performed depth judgements more quickly as the experiment progressed. The authors observed no differences between the two displays in terms of reported simulator sickness or eye strain.
Hacia una integración de mindfulness e inteligencia emocional en psicología y educación

Background: This article presents an empirical review of the integration of mindfulness and emotional intelligence. Mindfulness is a cognitive intervention model that helps people to develop awareness of their experience in the present moment favoring emotional self-regulation. Similarly, emotional intelligence has been conceptualized as a constellation of abilities related to the perception, understanding and adequate management of emotions within and among people. Both conceptual models are associated with a good sense of well-being and personal development. State of the art: This paper reviews the preliminary scientific evidence published in psychology and education literature, and examines the mediating effects of these two constructs in the process of emotional regulation and quality of life of individuals. Conclusions: The study results indicate that the interaction of mindfulness and emotional intelligence offers important benefits for people's well- being. It was also reported that emotional intelligence acts as mediator between mindfulness and greater positive emotions. The purpose of this review article is to identify a model of integration for emotional regulation that includes «active ingredients» to influence well-being and the way in which the practice of mindfulness contributes to increase the abilities of emotional intelligence.