Reviewerly/exHarmony huggingface dataset:
Understanding Algorithms

Abstract Algorithms account for an ever-larger component of the central government’s operations and actions, and hence perform an ever-important role in the delivery of public services to citizens and businesses. We analysed the activities and processes for which central government and its associated organisations use algorithms, classified these into categories, and identified the risks involved in the use of algorithms. In addition, we examined how central government and its associated organisations manage the operation and control the quality of algorithms. Most of the algorithms used by central government are relatively simple. They have a limited effect on private individuals, as it is only such relatively simple algorithms that take automatic decisions. Many of these decisions involve the automation of certain administrative activities, for example the automated sending of letters confirming the receipt of a communication. We did not find any fully self-learning algorithms in central government, only learning algorithms. We urge governments to evaluate algorithms on basis of a predetermined set of criteria and present a framework for such evaluations.
Data Mining Prospective Associated with the Purchase of Life Insurance Through Predictive Models

This work proposes the creation of an analytical model which allows improving the effectiveness of sales through the use of business intelligence and data mining methodologies which allow analyzing the historical information of corporate clients and determining the probability of buying a product. In this research, methodologies, and tools are used that allow structuring the steps to define the task, collect and analyze data, choose and configure the model, format data, evaluate results and report them to decision-makers. This will allow testing various analytical models that train them and compare them with historical data, provide new data, which eventually will help increase sales effectiveness, highlighting that the data used for this analysis are demographic data, socio-economic aspects, and any information that contributes to having a framework to be reused in future sales campaigns.
The Intercompany Settlement Blockchain: Benefits, Risks, and Internal IT-Controls

Abstract Blockchain is a novel technology that can be employed for a plethora of business applications. A blockchain provides highly reliable transaction data because it has a number of properties that guarantee the integrity of the data that is processed and stored. Paradoxically, the use of blockchain technology is associated with peculiar risks that need to be controlled. Therefore, the sophistication of this technology warrants a rethink of current IT-controls to manage accounting processes while novel controls need to be established to control the technology itself. In this chapter we discuss the impact of blockchain technology on IT-controls, and what IT-controls should be in place to mitigate risks using a case study of a blockchain implementation at the KLM Royal Dutch Airlines.
A Software Development Model for Analytical Semantic Similarity Assessment on Spanish and English

We propose a software development method for semantic text similarity based on the analysis of structural properties of pairs of snippets of texts. An agile software method focuses on the detection of biases on generated prototypes to enhance the quality of the expected features. A system for the detection of semantic text similarities is designed and implemented using the software method. The system design defines a set of structures for recording and computation of similarities using algorithms with polynomial complexities. The algorithms exploit knowledge bases for information extraction. Two implementations for English and Spanish were produced to test the portability of the system on European languages. The An experiment on Spanish and English text snippet is performed to test the performance of the prototypes. Results show that the degree of accuracy of the proposed algorithms are improved as the knowledge base content increases. The advantages of this methodologies is the elicitation of similarities between texts and the assessment of the similarity degree from scratch without prior knowledge assumptions.
PRSONA

As an increasing amount of social activity moves online, online communities have become important outlets for their members to interact and communicate with one another. At times, these communities may identify opportunities where providing their members specific privacy guarantees would promote new opportunities for healthy social interaction and assure members that their participation can be conducted safely. On the other hand, communities also face the threat of bad actors, who may wish to disrupt their activities or bring harm to members. Reputation can help mitigate the threat of such bad actors, and there has been a wide body of work on privacy-preserving reputation systems. However, previous work has overlooked the needs of small, tight-knit communities, failing to provide important privacy guarantees or address shortcomings with common implementations of reputation. This work features a novel design for a privacy-preserving reputation system which provides these privacy guarantees and implements a more appropriate reputation function for this setting. Further, this work implements and benchmarks said system to determine its viability in real-world deployment. This novel construction addresses shortcomings with previous approaches and provides new opportunity to its target audience.
Fisher Information as a Utility Metric for Frequency Estimation under Local Differential Privacy

Local Differential Privacy (LDP) is the de facto standard technique to ensure privacy for users whose data is collected by a data aggregator they do not necessarily trust. This necessarily involves a tradeoff between user privacy and aggregator utility, and an important question is to optimize utility (under a given metric) for a given privacy level. Unfortunately, existing utility metrics are either hard to optimize for, or they only indirectly relate to an aggregator's goal, leading to theoretically optimal protocols that are unsuitable in practice. In this paper, we introduce a new utility metric for when the aggregator tries to estimate the true data's distribution in a finite set. The new metric is based on Fisher information, which expresses the aggregators information gain through the protocol. We show that this metric relates to other utility metrics such as estimator accuracy and mutual information and to the LDP parameter \varepsilon. Furthermore, we show that under this metric, we can approximate the optimal protocols as \varepsilon \rightarrow 0 and \varepsilon \rightarrow \infty, and we show how the optimal protocol can be found for a fixed \varepsilon, although the latter is computationally infeasible for large input spaces.
Secure Maximum Weight Matching Approximation on General Graphs

Privacy-preserving protocols for matchings on general graphs can be used for applications such as online dating, bartering, or kidney donor exchange. In addition, they can act as a building block for more complex protocols. While privacy-preserving protocols for matchings on bipartite graphs are a well-researched topic, the case of general graphs has experienced significantly less attention so far. We address this gap by providing the first privacy-preserving protocol for maximum weight matching on general graphs. To maximize the scalability of our approach, we compute an 1/2-approximation instead of an exact solution. For N nodes, our protocol requires O(N log N) rounds, O(N^3) communication, and runs in only 12.5 minutes for N=400.
Fingerprinting and Personal Information Leakage from Touchscreen Interactions

The study aims to understand and quantify the privacy threat landscape of touch-based biometrics. Touch interactions from mobile devices are ubiquitous and do not require additional permissions to collect. Two privacy threats were examined - user tracking and personal information leakage. First, we designed a practical fingerprinting simulation experiment and executed it on a large publicly available touch interactions dataset. We found that touch-based strokes can be used to fingerprint users with high accuracy and performance can be further increased by adding only a single extra feature. The system can distinguish between new and returning users with up to 75% accuracy and match a new session to the user it originated from with up to 74% accuracy. In the second part of the study, we investigated the possibility of predicting personal information attributes through the use of touch interaction behavior. The attributes we investigated were age, gender, dominant hand, country of origin, height, and weight. We found that our model can predict the age group and gender of users with up to 66% and 62% accuracy respectively. Finally, we discuss countermeasures, limitations and provide suggestions for future work in the field.
Splitting Hairs and Network Traces

The widespread use of encryption and anonymization technologies---e.g., HTTPS, VPNs, Tor, and iCloud Private Relay---makes network attackers likely to resort to traffic analysis to learn of client activity. For web traffic, such analysis of encrypted traffic is referred to as Website Fingerprinting (WF). WF attacks have improved greatly in large parts thanks to advancements in Deep Learning (DL). In 2019, a new category of defenses was proposed: traffic splitting, where traffic from the client is split over two or more network paths with the assumption that some paths are unobservable by the attacker.
Dynamic impairment classification through arrayed comparisons

The multivariate normative comparison (MNC) method has been used for identifying cognitive impairment. When participants' cognitive brain domains are evaluated regularly, the longitudinal MNC (LMNC) has been introduced to correct for the intercorrelation among repeated assessments of multiple cognitive domains in the same participant. However, it may not be practical to wait until the end of study for diagnosis. For example, in participants of the Multicenter AIDS Cohort Study (MACS), cognitive functioning has been evaluated repeatedly for more than 35 years. Therefore, it is optimal to identify cognitive impairment at each assessment, while the family-wise error rate (FWER) is controlled with unknown number of assessments in future. In this work, we propose to use the difference of consecutive LMNC test statistics to construct independent tests. Frequency modeling can help predict how many assessments each participant will have, so Bonferroni-type correction can be easily adapted. A chi-squared test is used under the assumption of multivariate normality, and permutation test is proposed where this assumption is violated. We showed through simulation and the MACS data that our method controlled FWER below a predetermined level.
Brief Inspection and Testing of Various Retrieval Techniques

Searching is a traversal technique in the data structure, and it is used to find a particular element in a given list of items.This research paper presents the primary searching algorithm types and analyses their working process, time complexity, space complexity, advantages, disadvantages, and examples.Our study found that hash search is efficient for more significant data items, and exponential search is used for an infinite set of elements.Whereas binary search is suitable for mid -sized data items and applicable for arrays and linked lists, Dependent upon the analysis, a comparative study is being made so that the user can choose the type of searching technique based on the requirement.
Item non-response imputation in the Korea National Health and Nutrition Examination Survey

The Korea National Health and Nutrition Examination Survey (KNHANES) is a public health survey that assesses individuals' health and nutritional status and monitors the prevalence of major chronic diseases. In general, sampling weights are adjusted for unit non-responses and imputation is conducted for item non-responses. In this study, we proposed strategies for imputing item non-responses in the KNHANES in order to improve the usefulness of data, minimize bias, and increase statistical power.After applying logical imputation, we adopted 2 separate imputation methods for each variable type: unweighted sequential hot-deck imputation for categorical variables and sequential regression imputation for continuous variables. For variance estimation, multiple imputations were applied to the continuous variables. To evaluate the performance of the proposed strategies, we compared the marginal distributions of imputed variables and the results of multivariable regression analysis for the complete-case data and the expanded data with imputed values, respectively.When comparing the marginal distributions, most non-responses were imputed. The multivariable regression coefficients presented similar estimates; however, the standard errors decreased, resulting in statistically significant p-values. The proposed imputation strategies may cope with the loss of precision due to missing data, thus enhancing statistical power in analyses of the KNHANES by providing expanded data with imputed values.The proposed imputation strategy may enhance the utility of data by increasing the number of complete cases and reducing the bias in the analysis, thus laying a foundation to cope with the occurrence of item non-responses in further surveys.
A fuzzy trust measurement method considering patients' trust opinions in Internet plus Healthcare

With the outbreak of COVID-19, Internet plus Healthcare has developed rapidly with a number of Internet plus Healthcare platforms emerging. The problem of doctor-patient trust is a key issue restricting the development of the Internet plus Healthcare, which has aroused extensive attention of scholars. The patient's perceived trust on the Internet plus Healthcare platform has the characteristics of subjectivity, ambiguity, and high perceived risk. Therefore, existing trust calculation method becomes inapplicable because these characteristics have not been considered. In order to solve this problem, this study extracts influencing factors of patient trust on the Internet plus Healthcare platform, gives a trust calculation method based on intuitionistic fuzzy set theory, and added a risk preference coefficient in order to integrate the characteristics of patients' high perceived risk into the proposed method. This method is conducive to the platform to provide patients with more accurate doctor recommendations.
Transferring Knowledge From Text to Video: Zero-Shot Anticipation for Procedural Actions

Can we teach a robot to recognize and make predictions for activities that it has never seen before? We tackle this problem by learning models for video from text. This paper presents a hierarchical model that generalizes instructional knowledge from large-scale text corpora and transfers the knowledge to video. Given a portion of an instructional video, our model recognizes and predicts coherent and plausible actions multiple steps into the future, all in rich natural language. To demonstrate the capabilities of our model, we introduce the Tasty Videos Dataset V2, a collection of 4022 recipes for zero-shot learning, recognition and anticipation. Extensive experiments with various evaluation metrics demonstrate the potential of our method for generalization, given limited video data for training models.
Human-Robot Body Experience: An Artificial Intelligence Perspective

Abstract Human body experience is remarkably flexible, which enables us to integrate passive tools as well as intelligent robotic devices into our body representation. Accordingly, it can serve as a role model to make (assistive) robots interact seamlessly with their users or to provide (humanoid) robots with a human-like self-perception and behavior generation. This article discusses the potential of understanding human body experience and applying it to robotics. Particular focus is set on how to use artificial intelligence techniques and create intelligent artificial agents from insights about human body experience. The discussion is based on a summary of the author’s habilitation thesis and combines theoretical and experimental perspectives from psychology, cognitive science and neuroscience as well as computer science, engineering, and artificial intelligence. From this, it derives directions for future developments towards creating artificial body intelligence with human-like capabilities.
A case study–based analysis on remote medical monitoring with AWS cloud and internet of things (IoT)

The 21st century has seen great advancements in computing, machine understanding and healthcare. With Covid-19, it has become more evident that technology can impact healthcare and related disciplines deeply. Computer vision, combined with the latest advancements in digital image processing and deep learning, has enabled researchers to see and understand microscopic objects in detail. It has also enabled doctors to make better decisions related to treatment. Computer vision has helped to set up automated screening centres combined with thermal scanners. A lot of data is produced now which can be put together to train better algorithms and to get better results. In this research paper, we explore the role computer vision has played in modern healthcare and how it continues to influence the industry. Remote monitoring of IoT devices enables remote monitoring of data transfer or validity from IoT devices. Remote monitoring of IoT devices ingests real-time device data from IoT devices in order to analyse the condition of each IoT device and provide you notifications if there are data transmission difficulties or data that is out of range. Remote monitoring of IoT devices also provides a framework for gathering diagnostic data in order to derive outcome-oriented insights about the health of your assets. Computer vision allows computers and systems to extract useful information from digital photos, movies and other visual inputs through the internet.
Correlation between Linguistic Measures: An Extended Analysis

In this paper we reconsider the results that appeared originally in Coloma (2017) about the possible existence of negative correlation between linguistic measures, using a newly-assembled database of 80 languages for which we have the same text (which is the fable known as “The North Wind and the Sun”). Most conclusions of the original paper become reinforced, especially the ones related to the existence of language complexity trade-offs. This is particularly clear when we look at partial correlation coefficients between three linguistic ratios (phonemes per syllable, syllables per word, and words per clause), especially when we use simultaneous-equation regression methods and instrumental variables.
Extended $$H_i/H_\infty $$-Optimal Fault Detection for INS/GPS Integrated Systems

This chapter aims to deal with FD issues for an integrated system of inertial navigation system and global position system (INS/GPS). To this end, state space models of the INS/GPS integrated system are first established. An observer-based FDF for residual generation is then constructed with the observer gain matrix and the post-filter being designed in the context of $$H_i/H_\infty $$ -optimization. By utilizing techniques of innovation analysis and orthogonal projection, a solution to the extended $$H_i/H_\infty $$ -FDF is given in terms of Riccati recursions, followed by a residual evaluation strategy. Finally, a flight experiment is implemented to show the practical applicability of the method.
Research Progress on Vision–Language Multimodal Pretraining Model Technology

Because the pretraining model is not limited by the scale of data annotation and can learn general semantic information, it performs well in tasks related to natural language processing and computer vision. In recent years, more and more attention has been paid to research on the multimodal pretraining model. Many vision–language multimodal datasets and related models have been proposed one after another. In order to better summarize and analyze the development status and future trend of vision–language multimodal pretraining model technology, firstly this paper comprehensively combs the category system and related tasks of vision–language multimodal pretraining. Secondly, research progress on vision–language multimodal pretraining is summarized and analyzed from the two dimensions of image–language and video–language models. Finally, problems with and development trends in vision–language multimodal pretraining are discussed.
A study on the Improvement of the General Education Curriculum Based on Analysis of the Learning Objective

Competency-based education focuses on the question, “What can be done?” Therefore, it is necessary to educate learners so that they can perform what they know in the context of life. In addition, it is necessary to present specific learning objectives so that students can understand the content of the subject and use their knowledge to acquire the competencies expressed in specific situations. Therefore, in this study the actual condition of the learning objective of the lesson plans for general education subjects was identified. Also, I tried to propose systematic improvement measures and action strategies for the effective implementation of the general education curriculum. To this end, semantic network analysis was conducted by collecting the lesson plans of 138 subjects in the general education curriculum of University A.As a result, ‘understanding’, ‘acquisition’, and ‘cultivation’ appeared as the keywords of the general education curriculum learning objectives. However, I found that it was insufficient to present practical and specific learning objectives that are characteristic of the competency-based curriculum. Based on these results, I propose that we review the learning objectives system, strengthen the instructor's competency, and establish a goal-based curriculum system to operate the competency-based general education curriculum.
Analysis of College Student's Satisfaction and Needs for Competency-based Curriculum of General Education －Focusing on the Revised IPA, Borich Priority Formula and Thematic Analysis

As society changes in its demands of human resources with various competencies, the effort for improving the competency of college students is provided by centering the curriculum on a college general education. The purpose of this study is to confirm the satisfaction and needs of college students with a competency-based general education curriculum, and to list the implications of the organization and operation of such a curriculum.This study was conducted for students at K University using an online survey over the course of four weeks, involving the responses of 1,160 students. The results of this study are as follows: first, as a result of analyzing the satisfaction among students of the general education curriculum with the revised IPA analysis, both the satisfaction and the relative importance of the course composition in both stage and level were high. Second, as a result of analyzing the educational needs of general education courses, the demand for convergent, communication, utilizing information technology, career exploration, and career development domains were high. Also, we confirmed through open questions with thematic analysis that the demand for sports, engineering, foreign language, psychology, financial and economy-related subjects was high. Third, as a result of analyzing the educational needs of a competency-based general education curriculum, the demand for creative execution competency, open-minded inquiry, and citizenship was high.In conclusion, this study presented significant implications on what the faculty, the department and the college itself should consider in designing and operating a competency-based general education curriculum―one that provides curriculum-related education and one that outlines a mid to long-term road map.