Reviewerly/exHarmony huggingface dataset:
Uncertain facing

Uncertain Facing is a data-driven, interactive audiovisual installation that aims to represent the uncertainty of data points of which their positions in 3D space are estimated by machine learning techniques. It also tries to raise concerns about the possibility of the unintended use of machine learning with synthetic/fake data. Uncertain Facing visualizes the realtime clustering of fake faces in 3D space through t-SNE, a non-linear dimensionality reduction technique, with face embeddings of the faces. This clustering reveals what faces are similar to each other based on the assumption of a probability distribution over data points. However, unlike the original purpose of t-SNE that is meant to be used in an objective data exploration in machine learning, it represents data points as metaballs, in which two or more face images become a merged face when they are close enough, to reflect the uncertain and probabilistic nature of data locations the t-SNE algorithm yields. As a result, metaball rendering is used as a means of an abstract, probabilistic representation of data as opposed to exactness that we expect from the use of scientific visualizations. Along with the t-SNE and metaball-based visualization, Uncertain Facing sonifies the change of the overall data distribution in 3D space based on a granular sound synthesis technique. Uncertain Facing also reflects error values, which t-SNE measures at each iteration between a distribution in original high dimensions and a deduced low-dimensional distribution, to represent the uncertainty of data as jittery motion and inharmonic sound. As an interactive installation, Uncertain Facing allows the audience to see the relationship between their face and the fake faces, implying an aspect that machine learning could be misused in an unintended way as face recognition technology does not distinguish between real and fake faces.
Engineering Predecessor Data Structures for Dynamic Integer Sets.

We present highly optimized data structures for the dynamic predecessor problem, where the task is to maintain a set S of w-bit numbers under insertions, deletions, and predecessor queries (return the largest element in S no larger than a given key). The problem of finding predecessors can be viewed as a generalized form of the membership problem, or as a simple version of the nearest neighbour problem. It lies at the core of various real-world problems such as internet routing.In this work, we engineer (1) a simple implementation of the idea of universe reduction, similar to van-Emde-Boas trees (2) variants of y-fast tries [Willard, IPL'83], and (3) B-trees with different strategies for organizing the keys contained in the nodes, including an implementation of dynamic fusion nodes [Pǎtrascu and Thorup, FOCS'14]. We implement our data structures for w = 32,40,64, which covers most typical scenarios.Our data structures finish workloads faster than previous approaches while being significantly more space-efficient, e.g., they clearly outperform standard implementations of the STL by finishing up to four times as fast using less than a third of the memory. Our tests also provide more general insights on data structure design, such as how small sets should be stored and handled and if and when new CPU instructions such as advanced vector extensions pay off.
Combining support vector machine with radial basis function kernel and information gain for sentiment analysis of movie reviews

Abstract A movie review can be considered as an essential data source for both movie producers and potential consumers. The reviews are usually used as a benchmark for knowing the movie’s quality and are utilized as references to whether the film is worth watching. The study of sentiment analysis has received more attention from researchers. This current study aims to classify the sentiment analysis of movie reviews obtained from the IMDb site. The support vector machine (SVM) method was employed to classify the movie review’s sentiments. Meanwhile, the radial basis function (RBF) kernel and information gain (IG) were used to enhance classification. Feature selection was conducted by removing irrelevant features and selecting features with a strong correlation to classification. The IG algorithm was used for feature selection. The current study revealed that the classification accuracy of movie review sentiment analysis using the SVM algorithm, SVM + RBF kernel, and SVM+RBF Kernel+IG are 81.50%, 82.25%, and 87.25%, respectively.
Streaming films and online learning during Covid-19 and onward

"Streaming films and online learning during Covid-19 and onward." Journal of Electronic Resources Librarianship, 33(2), pp. 119–120
Role of Professional Work of a Higher Education Teacher in the Development of Students’ Creativity

This article presents the results of studying the role of the professional work of a higher education teacher in the development of students’ creativity. The relevance of this problem is justified by the creative activities of a teacher, its main types, and the organization of the didactic process using organizational forms and methods of education facilitating the development of an integral creative individuality. The authors analyzed the types and the content of professional educational problems and studied the fulfillment conditions for students’ creativity, as well as the characteristics, logic, and levels of pedagogic thinking and the criteria of pedagogic creativity. The theoretical methods of the research include the analysis of the literature dedicated to creative development and empirical methods aimed at the analysis and self-analysis of students’ creativity development. The results of the research can be applied by young teachers in practice to improve the methods of teaching engineering subjects.
It Can’t Be Taught Online: Applied Sciences Students during the Pandemic

ruTransition to distance learning during the Covid-19 lockdown in spring 2020 was a challenge to the education system in general and higher education in particular. Applied Sciences were unanimously recognized as the most affected due to their focus on practical skills, being closely tied to the institutional infrastructure, and a moderate curriculum digitalization. This article describes student experiences during the spring semester of academic year 2019/20, using data obtained from 30 interviews with students in Engineering & Technology, Health & Medical Sciences, and Arts & Culture degrees. Delivery of lectures and practical classes, placements, dissertations, and faculty–student interactions are analyzed. Findings are consistent with the widely discussed perception of education during the pandemic not as distance learning but rather as emergency remote teaching that requires supportive measures to compensate for time loss in learning as well as solutions to technical and methodological issues. © 2021, Mir Rossii. All Rights Reserved.
ACM SIGMIS CPR Panel Report: Should Information Systems Professors be More Techno-savvy than Students? (And What Would that Mean for Teaching in Times of the COVID-19 Crisis?)

Rapid advances in information and communication technologies present a challenge to information systems (IS) professors.Not only do these advances frequently make course materials out of date, but also IS professors may struggle to stay current with popular technology applications.In a sense, these forces lead to a paradox that students may be more techno-savvy than their professors (at least in certain areas).Furthermore, students may feel frustrated when techno-savvy professors cannot efficiently teach them in learning technologies.In this paper, we synthesize the panel titled "The Elephant in the Classroom: Do Information Systems Professors Need to be more Techno-Savvy than Students?" at the 55th ACM SIGMIS Computer and People Research Conference in Nashville, Tennessee.Thomas Ferratt, Michael Gallivan, Yaojie Li, Thomas Stafford, Mary Sumner, and Crag Van Slyke served as panelists.We use their discussion to develop techno-savviness as a construct in the IS education context and to describe distinct types of techno-savviness.
Nudging Towards Data Equity: The Role of Stewardship and Fiduciaries in the Digital Economy

In this short piece, we posit that data stewards and digital fiduciaries, by enabling greater participation in data-related decisions, can nudge society towards greater social and economic equity. First, we locate data stewards and digital fiduciaries within the broader debate on new data paradigms, and discuss their respective roles at the individual and community level. Drawing from literature on community stewardship of environmental resources, the paper relatedly touches upon possible duties and business models for data stewards and digital fiduciaries.Thereafter, we discuss conceptual and anecdotal links between “ladders” of participation and greater equity in the context of data. While strong links are posited between participation as facilitated by stewards and forms of data equity, there remains a need to establish, research, and evaluate the potential and limitations of data stewards and digital fiduciaries in the “wild.” The paper concludes with specific questions designed to build a body of work that explores the role that stewards/fiduciaries can play in rebalancing power in the data economy.
RETRACTED: Research and Realization of Artificial Intelligence-based Salary System Optimization

Abstract With the development of technology, computers have penetrated into various industries. At this stage, the way of companies manage their compensation has also gained wide attention, especially in the economics industry and the management industry. No matter what kind of company, the compensation management system is crucial. Due to the continuous progress of our national economy, the competition in the market becomes more and more fierce, and for enterprises, in order to meet the competition in the market, they need to improve the requirements for the compensation system and plan a suitable compensation management system according to the market requirements, it can improve the efficiency and achieve a win-win situation between enterprises and employees. To achieve this requirement, the traditional management model is no longer applicable, and this paper focuses on the research and implementation of salary system optimization based on artificial intelligence.
A simple defense against adversarial attacks on heatmap explanations

With machine learning models being used for more sensitive applications, we rely on interpretability methods to prove that no discriminating attributes were used for classification. A potential concern is the so-called fair-washing - manipulating a model such that the features used in reality are hidden and more innocuous features are shown to be important instead. In our work we present an effective defence against such adversarial attacks on neural networks. By a simple aggregation of multiple explanation methods, the network becomes robust against manipulation. This holds even when the attacker has exact knowledge of the model weights and the explanation methods used.
Bitcoinmania: A Ticking Time Bomb Waiting to Explode

At the time of writing this article, the Bitcoin bomb has exploded; after peaking at $64,863 on 14 April 2021, the value of Bitcoin has plunged 45% to $31,276 on May 21, 2021; consequently, nearly $600 billion of value evaporated from Bitcoin’s market cap, i.e. from $1.182 trillion (April 2021) to less than $600 billion (May 2021). Bitcoin’s high price valuations coincides with its halving dates; aftermath of each halving event has led to a bubble formation within one year and a crash in the ensuing few months after a perceived peak has been achieved. The three previous halving events are a testimony to this fact; after the halving #1 on 28 November 2012 (block reward was reduced from 50 BTC to 25 BTC), price of each bitcoin increased from $76 on 9 July 2013 to a peak of $1,153 on 5 December 2013, an increase of 1,417% (but fell to $177 in January 2015). Following the halving #2 on 9 July 2016 (block reward was reduced from 25 BTC to 12.5 BTC), the price of bitcoin charted a remarkable ascent, from $963 on 2 January 2017 to $20,089 on December 17 in the same year (a surge of 1,986%, followed by a nosedive to $3,557 in January 2018). The halving #3 has been most unprecedented by any stretch of imagination, the value of each bitcoin has skyrocketed from $7,194 on 1 January 2020 to $64,863 on 14 April 2021; however in the subsequent month (May 2021), Bitcoin gave back half of the gain (price reduction of 20% or more could be considered as a major correction). It is important to mention that Blockchain is not confined to Bitcoin and its survival as a revolutionary technology does not depend on cryptocurrencies. Bitcoin and some altcoins may become obsolete, but blockchain will continue to forge ahead unabated as many life-changing inventions have done before.
The music psychology that uses fuzzy models to promote mental health

Through the analysis of design psychology, this paper first sorts out the development context of design psychology, and then elaborates the definition of design psychology. Moreover, this paper combines the actual demands of music psychology to construct a model, applies the fuzzy model to this model, and systematically studies the basic concepts of design psychology. Simultaneously, this paper combines its concept with examples of interface interaction design of mobile Internet product to study and observe the application of design psychology in this field. In addition, this paper summarizes a set of scientific and reasonable specific methods for interface interaction design, and then uses this method to guide the practice of system model interaction design, constructs the model structure based on demand analysis, and optimizes the system structure. Finally, this paper designs experiments to verify the performance of the model. The research results show that the model constructed in this paper has certain practical effects.
Towards a Compact SAT-Based Encoding of Itemset Mining Tasks

Many pattern mining tasks have been modeled and solved using constraints programming (CP) and propositional satisfiability (SAT). In these two well-known declarative AI models, the problem is encoded as a constraints network or a propositional formula, whose associated models correspond to the patterns of interest. In this new declarative framework, new user-specified constraints can be easily integrated, while in traditional data mining, such additional constraints might require an implementation from scratch. Unfortunately, these declarative data mining approaches do not scale on large datasets, leading to huge size encodings. In this paper, we propose a compact SAT-based encoding for itemset mining tasks, by rewriting some key-constraints. We prove that this reformulation can be expressed as a Boolean matrix compression problem. To address this problem, we propose a greedy approach allowing us to reduce considerably the size of the encoding while improving the pattern enumeration step. Finally, we provide experimental evidence that our proposed approach achieves a significant reduction in the size of the encoding. These results show interesting improvements of this compact SAT-based itemset mining approach while reducing significantly the gap with the best state-of-the-art specialized algorithm.
Adapting a Hybrid Intelligent Reinforcement Learning Environment

Learning is seen as the process of interaction between two sides. First one is a virtual teacher - an intellectual learning environment that accumulates in its base the didactically and methodically structured material of the specific discipline for transferring it to the student. The other side is the student who is “absorbed” by his consciousness in the learning environment and actively perceived material transferred to him, i.e. not just putting it in his mind (memory), but also conducive to the rational organization of the learning process. This article outlines the principles of organizing a hybrid intelligent learning environment, integrating models based on knowledge of the production type, and neural network technologies for decision-making based on two learning strategies: the self-navigation strategy of the student through the discipline material and the strategy of his complete submission to the intellectual learning environment. This helps to support the important role of the student in the formation of learning scenario and facilitates the solution of the problems of adapting the intellectual learning environment to the individual characteristics of the student, not only adapting with reinforcement, but also with the teacher, to a certain extent, in the role of which the student acts.
Impact of Personality Types and Matching Messaging on Password Strength

People often create passwords for their accounts that are insecure.An insecure password is often easy to guess -thus, hackers can easily access their victims' accounts.It is important for users to know how to create and manage secure passwords so they can better protect themselves from hackers.It is well-known that different users have different personality types, such as Big Five and True Colors.This research examines if there is any link between personality types and password security behavior.Each participant was shown either a matching or mismatching message based on their personality type, and it was measured whether their password security behavior changed a month later.Our results show that 66% of participants with a Green (knowledgeable and competent) personality type chose a strong password, compared to less than 50% of other personality types.Our results also demonstrate that messaging has a statistical impact on improving password security behavior.
RETRACTED: Application Research of Accounting Informationization Based on Cloud Computing

Abstract With the development of the network, many new computer-related technologies are constantly emerging. At present, many industries have begun to use cloud computing to continuously strengthen the development of enterprises. This paper make some specific suggestions about the construction of enterprise accounting information. I believe it will be helpful for building a model that combines corporate accounting information system and cloud computing.
UTILITY OF WASTE WATER FOR FARMING USING ARTIFICIAL INTELLIGENCE

Majority of India is depended upon the agriculture and farming, the main resource for cultivating land is water, due to recent industrialization water in the rivers was polluted. To overcome from this polluted water for cultivation farmers need to take necessary steps. Owing to the lack of adequate awareness of cultivating agriculture fields with pure water, for irrigation purposes farmers are facing tremendous difficulty with industrial waste in rivers, due to huge increase of population and high utility of ground water leads to declining of ground water level. Water shortage is the major concern in the present days. Since river water is rendered more acidic by runoff from fields, hold’s solvent waste minerals and salts with dangerous fertilizer nutrients due to the polluted water irrigation becomes hard, as a result crop cannot survive. To overcome from this problem in this paper we propose a model to purify the waste water for farming purpose using Artificial Intelligence and creating awareness among the farmers how Artificial Intelligence technology is used for irrigation of quality crop in the fields
Literary work education model based on intelligent machine learning and reader scoring criteria factors

In order to improve the effect of literary works education, this paper combines intelligent machine learning and reader scoring criteria factors to construct an intelligent education model, and proposes a collaborative filtering recommendation algorithm based on item proportion factors and time decay. When calculating the user similarity, this paper adds the scale factor of the intersection of common scoring items to all the scoring items, and considers the non-intersection part of the user scoring items. Secondly, when predicting the project score, this paper adds a time decay function, combines the forgetting curve law to modify the score prediction method, and combines the actual needs to construct the basic framework of the education model. In addition, this paper designs experiments to verify the performance of the literary work education model constructed in this paper. The research results show that the literary work education model constructed in this paper based on intelligent machine learning and reader rating criteria factors has a certain role in promoting the effect of literary education.
Credit Card Fraud Detection using Machine Learning and Data Science

It is important that companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. These problems can be handled with Data Science and its importance, along with Machine Learning. This project aim is to illustrate the modelling of a data set using machine learning with Credit Card. Our objective is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a sample of classification. In this process, we have focused on analysing and pre-processing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data.
Human Being Emotion in Cognitive Intelligent Robotic Control Pt I: Quantum / Soft Computing Approach

The article consists of two parts. Part I shows the possibility of quantum / soft computing optimizers of knowledge bases (QSCOptKB™) as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface. In particular, case, the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™ and QCOptKB™ sophisticated toolkit. Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described. The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown. Developed information technology examined with special (difficult in diagnostic practice) examples emotion state estimation of autism children (ASD) and dementia and background of the knowledge bases design for intelligent robot of service use is it. Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.
Perceived Usefulness, Self-Efficacy, and Emotional Engagement: Does It Affect Student’s Satisfaction while using the Platform?

Many students are dissatisfied with using blended learning platforms. At the same time, student satisfaction with a blended learning platform can determine the quality of learning and learning outcomes. This study analyzed the direct and indirect influence between the user perception of a blended learning platform, self-efficacy, and emotional engagement on student satisfaction. This research includes quantitative research with a type of causality—grade XI students of IPS SMA 14 Negeri Surabaya as a population. A sample of 103 students was taken using saturated sample techniques. Data collection is obtained through interviews, questionnaires, and documentation. The data analysis used is path analysis. The results directly influenced perceptions of the usefulness of blended learning platforms, self-efficacy, and emotional engagement on student satisfaction. The indirect influence between the perception of usefulness of platform and self-efficacy to student satisfaction through emotional engagement.