Reviewerly/exHarmony huggingface dataset:
Visual Rhythm-based Convolutional Neural Networks and Adaptive Fusion for a Multi-stream Architecture Applied to Human Action Recognition

In this work, we address the problem of human action recognition in videos. We propose and analyze a multistream architecture containing image-based networks pre-trained on the large ImageNet. Different image representations are extracted from the videos to feed the streams, in order to provide complementary information for the system. Here, we propose new streams based on visual rhythm that encodes longer-term information when compared to still frames and optical flow. Our main contribution is a stream based on a new variant of the visual rhythm called Learnable Visual Rhythm (LVR) formed by the outputs of a deep network. The features are collected at multiple depths to enable the analysis of different abstraction levels. This strategy significantly outperforms the handcrafted version on the UCF101 and HMDB51 datasets. We also investigate many combinations of the streams to identify the modalities that better complement each other. Experiments conducted on the two datasets show that our multi-stream network achieved competitive results compared to state-of-the-art approaches.
Is population Explosion a threat to Pakistan’s Economic Development?

Purpose: Population growth and its impact on economic development have been a subject of major concern among demographers, economists, and social scientists. Some believe that rapid population growth is a significant problem, while others consider it just a hoax. Previous studies have discovered the negative as well as the positive and profound impact of rapid population growth on economic development. The present study analyses population growth and its impact on economic development in the context of Pakistan.&#x0D; Design/Methodology/Approach: A Descriptive Methodology is found suitable to analyze the population growth and its impact on economic development in the context of Pakistan. In this regard, 50 articles from google scholar and statistical reports from World Bank are studied. The data is taken from World Bank Indicators and presented in the form of charts.&#x0D; Findings: It is found that poor family planning, high fertility, low status of women in society, and illiteracy are the main contributors to rapid population growth in Pakistan. The study concludes that rapid population growth in Pakistan contributes to higher unemployment, lower per-capita income, and depletion of natural resources.&#x0D; Implications/Originality/Value: It further recommends that rapid population growth can be addressed by educating the women, contraceptive-use campaigns or community-based programs, and investing in family planning programs.&#x0D;
On Application of Block Kaczmarz Methods in Low-Rank Matrix Factorization

Matrix factorization techniques compute low-rank product approximations of high dimensional data matrices and as a result, are often employed in recommender systems and collaborative filtering applications.However, many algorithms for this task utilize an exact least-squares solver whose computation is time consuming and memory-expensive.In this paper we discuss and test a block Kaczmarz solver that replaces the least-squares subroutine in the common alternating scheme for lowrank matrix factorization.This variant trades a small increase in factorization error for significantly faster algorithmic performance.In doing so we find block sizes that produce a solution comparable to that of the least-squares solver for only a fraction of the runtime and working memory requirement. Introduction.With the recent rise and ubiquity of online services, the volume of data available for and demanding analysis has exploded.This data often resides in extremely high-dimensional space, and for this reason, it is often computationally and intuitively beneficial to reduce its dimension, offering decreased required storage space and insight into the latent trends within the data.Matrix factorizations (e.g., principal component analysis/singular value decomposition (PCA/SVD) [18], CUR factorization [12], pseudo-skeleton approximation [5,16], and the nonnegative matrix factorization (NMF) [11,21]) are common approaches to find factor matrices whose product is a low-rank approximation to the original data matrix.These factor matrices, when combined with auxiliary methods, can help identify useful trends and patterns present in the data.As a result, matrix factorization models often find use in tasks such as collaborative filtering [7,17,22], which predicts a user's preferences based on historical preferences of other similar/dissimilar users (e.g., for e-commerce product recommendation), and topic modeling [1], which discovers latent themes or topics in a dataset.Specific applications in which these models have been applied are document classification [2], facial recognition [20], and collaborative filtering for movie recommendation (as part of the winning submission to the Netflix Prize competition) [9].While many dimension-reduction and topic modeling approaches are framed as matrix factorization models, we focus on the simple low-rank factorization model for data X ∈ R m×n that seeks A ∈ R m×k , generally called the dictionary matrix, and S ∈ R k×n , generally called the representation matrix, such that X ≈ AS.Here we consider the factor rank k to be a user-defined parameter but assume that k < min{m, n} so that the resulting factorization reduces the dimension of the original data and/or reveals latent themes in the data.Generally, the factor rank k can be chosen according to a priori information or a heuristic method.Data points are typically stored as columns of X, thus m represents the number of features or attributes of a single observation, and n represents the number of observations.
Diverse Super-Resolution with Pretrained Hierarchical Variational Autoencoders

Image super-resolution is a one-to-many problem, but most deep-learning based methods only provide one single solution to this problem. In this work, we tackle the problem of diverse super-resolution by reusing VD-VAE, a state-of-the art variational autoencoder (VAE). We find that the hierarchical latent representation learned by VD-VAE naturally separates the image low-frequency information, encoded in the latent groups at the top of the hierarchy, from the image high-frequency details, determined by the latent groups at the bottom of the latent hierarchy. Starting from this observation, we design a super-resolution model exploiting the specific structure of VD-VAE latent space. Specifically, we train an encoder to encode low-resolution images in the subset of VD-VAE latent space encoding the low-frequency information, and we combine this encoder with VD-VAE generative model to sample diverse super-resolved version of a low-resolution input. We demonstrate the ability of our method to generate diverse solutions to the super-resolution problem on face super-resolution with upsampling factors ×4, ×8 and ×16.
Creativity and Digitalization

This article is a part of the discussion of Ilya Kasavin’s article “Creativity as a social phenomenon” and is devoted to the analysis of creativity in the era of digitalization. The author discusses creativity in computer programs and the actions of assistant robots. They can be creative because they are able to find new solutions to various problems. The Go program used new strategies that human players had never played before; another program predicted the crystal structure of various substances that had not yet been studied experimentally. The concept of implicit knowledge is used to answer the question about the specifics of creativity. H. Collins singled out three types of tacit knowledge: relative, somatic and collective. The first is associated with the acquirement of various procedures – like cooking, the second – various skills related to body control, such as swimming or cycling, the third – social and cultural rules. Only the third type of tacit knowledge is associated with creativity. Programs and assistant robots can master relative and somatic implicit knowledge. They are not able to work with collective implicit knowledge. Creativity cannot be digitized. At the same time, digitalization forms important social prerequisites for the development of creativity. Currently, a new social group is being formed – the precariat. This is a marginal group; its representatives are not very firmly included in social structures. It is possible to single out the scientific precariat. With the help of digital technologies, it forms new ways, mechanisms, strategies and tools for solving various problems, form new ideas that expand our collective tacit knowledge.
INSURANCE

Chapter 16 relating to Insurance makes the point that an industry concerned with data and risk has exactly the characteristics that are or should be suitable for AI usage. It also notes, however, that there are real technical questions in the topic that we don't yet have answers to. The chapter highlights interrelationships with other chapters, particularly those on data (because of the richness of data in the insurance industry) and discrimination (because of existing rules on this subject). The impact of GDPR-type rules must also be considered in any product using AI and that point applies beyond the insurance industry. The chapter shows how many things must be joined together when emerging technologies are subject to legal analysis.
Emerging Paradigm of Employees’ Involvement in Decision Making and Organizational Effectiveness: Further Evidence from Nigerian Manufacturing Firms

This study explored employees’ involvement in decision making and organizational effectiveness of manufacturing firms. The cross-sectional survey research design was employed. Data were elicited from the middle and lower level employees of manufacturing firms registered under the Manufacturing Association of Nigeria (M.A.N.), Edo/Delta Chapter retrieved as at 31st October, 2021. For the purpose of the research, ten firms were selected with a total staff population of 1,839. Taro Yamane’s formula was used in arriving at a sample of 329 employees and 216 responses retrieved were analyzed using the simple percentage, mean statistics, and hypotheses tested with linear regression after multicollinearity test and correlation matrix revealed no collinearity problem. Results of hypotheses tested showed that employees’ involvement in decision making has significant effect on organizational productivity, organizational adaptability, and organizational flexibility. Accordingly, the study recommended that the top management level of manufacturing firms should see the need to constantly involve the middle and lower level employees in their decision making processes whether directly or through advisory participation, to continually gain more effectiveness.
Considerations on Current State of Sensors Attached to Drones Applications in Environmental Quality Monitoring

Environmental pollution can be detected by installing special devices on the drone, called sensors, which will collect data on air, water, and soil quality. The topic of using drones with sensors is currently known and is applied in various fields such as agriculture, meteorology, hydrology, marine and oceanographic prospecting, gas leak inspection, environmental monitoring, etc. Air, water, and soil sensors and collectors attached to drones are used to detect and collect contaminants. The remote sensing method uses multispectral optical and thermal sensors for modelling and mapping applications.&#x0D;
Geochemical exploration of ore deposits that are hidden beneath the surface. Status and prospects

The reduction in the number of deposits reaching the modern erosional surface has led to the need to develop and improve the geochemical exploration methods for searching for deposits under deep cover. They are focused on the identification of gas and lithochemical halos of indirect (CO2, H2, He, NH4+, K+ ions, etc.) and direct indicators (Au, Pb, Cu, etc.) for buried ore bodies and blind deposits. The data of experimental and methodical works for numerous deposits of different genesis suggest that the formation of superimposed halos of these two groups of indicators of ore-forming environment proceeds by a single mechanism. Their joint migration to the day surface and the subsequent unloading into the atmosphere form halos in the subsurface air, where the concentration of the indicative elements many times exceeds the geochemical background. That allows laser analytical equipment used in meteorology (lidars, correlation spectrometers, etc.) to be applied during fi ldwork to monitor the composition of the atmosphere.
Beauty in art: synthesis of the beautiful and the ugly

Beauty is that category of aesthetics, the definition of which is still the subject of controversy among philosophers and art researchers. Beauty is both objective and subjective, absolute and relative, it is an imperative of art and it develops historically. Beauty forms a dialectical triad together with two other leading aesthetic categories — the beautiful and the ugly. In this triad, different types of synthesis of the beautiful and the ugly form the basis of historical types of beauty. Philosophers, artists, art researchers have always had a professional interest in the dialectical connection «beautiful-ugly». However, firstly, the relations between these categories have not been studied previously from the standpoint of their dialectical analysis and synthesis at the same time. Secondly, different types of synthesis of the beautiful and the ugly did not stand out. These two conditions are taken into account in the article. In addition, beauty is considered as an aesthetic meta–category in the context of the dialectical triad «beautiful — ugly — beauty». Together, this made it possible to describe, explain and predict the change of historical types of beauty in the arts in the article. According to the author’s concept, three such objective types are distinguished and they correspond to the mimetic, non-mimetic and post-mimetic stages of the development of spatial arts. So the beautiful dominates the ugly (the art of the Ancient World, Antiquity, the Middle Ages, Renaissance and Modern times); the beautiful borders on the ugly (the art of modernism); the ugly dominates the beautiful (the art of postmodernism). The author also substantiates the assumption that today the next type of beauty has appeared. It arose like the previous ones in the conditions of radical changes in the artistic perception of the system-organizational principles of the Universe.
A Sentiment Analysis of Turkish Tweets Shared in Nursing Week During the Pandemic

Aim: This study aimed to conduct an artificial intelligence-based sentiment analysis of Turkish tweets about nursing during the nursing week during the COVID-19 pandemic.Method: This is a retrospective descriptive survey.Between May 4 and May 19, 2021, Turkish tweets were analyzed using the Python library Tweepy.The search terms "nurse, nursing, and nursing week" were used to analyzed tweets for their positivity, neutrality, or negativity.Results: The analysis of 24,944 tweets revealed that tweets frequently express neutral emotions.The negative tweets frequently discussed issues such as societal gender perception, professionalism, burnout during the pandemic, salaries, inadequate nursing workforce, inequalities, violence against healthcare professionals, and the deaths of nurses.Conclusions: Social media applications can be recommended as important tools for raising awareness of the nursing profession identity, professionalism, visibility, and the perception of society towards nursing, nursing problems, and recommendations for solutions.
Metadata Repositories

Summary This chapter presents a review of online repositories where researchers can share data, code, and experiments. In particular, it covers OpenML, an online platform for sharing and organizing machine learning data automatically. OpenML contains thousands of datasets and algorithms, and millions of experimental results. We describe the basic philosophy involved, and its basic components: datasets, tasks, flows, setups, runs, and benchmark suites. OpenML has API bindings in various programming languages, making it easy for users to interact with the API in their native language. One important feature of OpenML is the integration into various machine learning toolboxes, such as Scikit-learn, Weka, and mlR. Users of these toolboxes can automatically upload all their results, leading to a large repository of experimental results.
Evaluating Recommendations of Metalearning/AutoML Systems

Abstract This chapter discusses some typical approaches that are commonly used to evaluate metalearning and AutoML systems. This helps us to establish whether we can trust the recommendations provided by a particular system, and also provides a way of comparing different competing approaches. As the performance of algorithms may vary substantially across different tasks, it is often necessary to normalize the performance values first to make comparisons meaningful. This chapter discusses some common normalization methods used. As often a given metalearning system outputs a sequence of algorithms to test, we can study how similar this sequence is from the ideal sequence. This can be determined by looking at a degree of correlation between the two sequences. This chapter provides more details on this issue. One common way of comparing systems is by considering the effect of selecting different algorithms (workflows) on base-level performance and determining how the performance evolves with time. If the ideal performance is known, it is possible to calculate the value of performance loss . The loss curve shows how the loss evolves with time or what its value is at the maximum available time (i.e., the time budget) given beforehand. This chapter also describes the methodology that is commonly used in comparisons involving several metalearning/AutoML systems with recourse to statistical tests.
Blockchain Technology in Healthcare: Use Cases Study

Blockchain technology is now regarded as one of the most interesting and possibly innovative technologies. It enables information to be stored and exchanged securely and transparently without the need for a centralized authority to regulate it. A few of the primary benefits of this technology is the atomicity of the stored data. Given its features, this technology has the potential to give answers to challenges encountered in a very sensitive sector, notably healthcare. The medical field is dealing with several issues that some are attempting to address today. The most important are the administration of medical records and the claims process, the acceleration of clinical and biomedical research, and the advancement of the biomedical and health data registry. The major challenge is the processing and analysis of patient records due to the large amount of data collected. The security of this data is another challenge to consider. Due to the high connectivity, these systems are prone to malicious attacks. In addition, it is difficult to ensure confidentiality due to the exchange of sensitive data. This chapter discusses the use of blockchain technology in healthcare sector. The purpose of this survey was to provide an overview of the features and concepts related to security requirements of blockchain in a healthcare system. It shows that this technology has a major role in terms of security of patient's medical data.
Procedure of the design of photovoltaic systems applied to ornamental lighting

Photovoltaic systems have the advantage of reducing atmospheric pollution, avoiding 𝑪𝑶𝟐 emissions. Another advantage is the energy savings they produce and that in the long term they can return the investment made. PV systems off grids are applied in remote and rural areas, where there is no electricity distribution network. The use of PV systems off grids can be included in ornamental lighting, as they have low levels of luminous efficiency due to the use of old technologies. Therefore, this study presents a procedure for the design of PV systems applied to ornamental lighting, starting from obtaining adjusted values of solar irradiance, based on estimated solar irradiance data from NASA in the city of Cuenca (Ecuador) and measured data. The critical month (maximum consumption and minimum irradiance) is selected, which corresponds to June with a value of 𝟑, 𝟕𝟔 𝒌𝑾𝒉/𝒎𝟐 . The results indicate that these systems are feasible to be implemented, due to the fact that the total investment cost is around $10.450,00 compared to a 5,2 km grid extension system that costs around $44.512,00. Finally, the responses of the PV systems off grid applied to ornamental lighting are tested in MATLAB/Simulink.
Game and Punishment Criminal Justice Lens on Commission Structure Ethics: An Abstract

Gaming behaviors may cause salespeople to bend or break agreements with customers, reducing customer trust (Román and Ruiz 2005) and increasing supply chain costs to business customers. The salesperson’s firm is also directly harmed by gaming behaviors. The opportunity to engage in gaming behaviors increases as the complexity of the commission structures increases (Larkin 2014; Owan, Tsuru, and Uehara 2015; Oyer 1998, Tzioumis and Gee 2013). Tiered commission structures, also known as nonlinear compensation plans, may be particularly prone to gaming behaviors because salespeople can earn greater commission rates as they reach certain levels of sales performance during a commission period (Freeman et al. 2019; Larkin 2014; Owan et al. 2015).Commission structures are often complex. In tiered commission structures, the percentage of variable pay changes at different levels of sales performance. Salesperson behaviors may change just prior to commission deadlines, depending on goal achievement. According to the deterrence doctrine, people weight temptation versus consequences before engaging in unethical behaviors. When the difference between tiers is large, it creates a larger temptation. Furthermore, salespeople should frame sales revenue differently depending on whether it leads to a new commission tier. Salespeople are likely to justify and downplay the consequences of gaming behavior that avoids rewards framed as a “loss” compared to gaming behavior intended to achieve a “gain.” Using a sample of professional salespeople participating in an experiment, this research shows that salespeople are more likely to engage in gaming behavior to avoid a loss, particularly when rewards stakes are high and their visibility within the organization is low. These findings demonstrate that commission plan structure has an impact on salesperson behaviors above and beyond the extant paradigm of variable versus fixed compensation.KeywordsSales ethicsSales commission structureCompensationSalesperson behaviorUnethical behaviorSales management