Reviewerly/exHarmony huggingface dataset:
PatchNet: Unsupervised Object Discovery based on Patch Embedding

We demonstrate that frequently appearing objects can be discovered by training randomly sampled patches from a small number of images (100 to 200) by self-supervision. Key to this approach is the pattern space, a latent space of patterns that represents all possible sub-images of the given image data. The distance structure in the pattern space captures the co-occurrence of patterns due to the frequent objects. The pattern space embedding is learned by minimizing the contrastive loss between randomly generated adjacent patches. To prevent the embedding from learning the background, we modulate the contrastive loss by color-based object saliency and background dissimilarity. The learned distance structure serves as object memory, and the frequent objects are simply discovered by clustering the pattern vectors from the random patches sampled for inference. Our image representation based on image patches naturally handles the position and scale invariance property that is crucial to multi-object discovery. The method has been proven surprisingly effective, and successfully applied to finding multiple human faces and bodies from natural images.
Variational Policy Search using Sparse Gaussian Process Priors for Learning Multimodal Optimal Actions

Policy search reinforcement learning has been drawing much attention as a method of learning a robot control policy. In particular, policy search using such non-parametric policies as Gaussian process regression can learn optimal actions with high-dimensional and redundant sensors as input. However, previous methods implicitly assume that the optimal action becomes unique for each state. This assumption can severely limit such practical applications as robot manipulations since designing a reward function that appears in only one optimal action for complex tasks is difficult. The previous methods might have caused critical performance deterioration because the typical non-parametric policies cannot capture the optimal actions due to their unimodality. We propose novel approaches in non-parametric policy searches with multiple optimal actions and offer two different algorithms commonly based on a sparse Gaussian process prior and variational Bayesian inference. The following are the key ideas: 1) multimodality for capturing multiple optimal actions and 2) mode-seeking for capturing one optimal action by ignoring the others. First, we propose a multimodal sparse Gaussian process policy search that uses multiple overlapped GPs as a prior. Second, we propose a mode-seeking sparse Gaussian process policy search that uses the student-t distribution for a likelihood function. The effectiveness of those algorithms is demonstrated through applications to object manipulation tasks with multiple optimal actions in simulations.
Habilidades de Personal Software Process (PSP) para la industria del software en Latinoamérica

This research covers the technical and soft skills required in software development, a globally in-demand activity. These skills are required to efficiently perform in a very globalized, competitive field. Previous studies show the existence of a gap between industry and academia. Training centers provide professionals with technical skills, but as of late, the need for soft skills has become apparent, making it important to study and find ways to embed them into the professionals who join the industry. The application of Personal Software Process (PSP) methodology helps students to strengthen their skills in the software development process, however, the method is not widespread in academia nor industry. This article shows the importance of soft skills, according to Latin American companies, and how PSP can reduce the skills gap.
Makassar Language Word Class Category

Abstract . This study aims to describe the categories of word class of the Makassar language phrases. This research is a qualitative research with a descriptive design. The data of this research are Makassar language phrases obtained from informants who are native speakers of the Makassar language who reside in Bontonompa District, Gowa Regency. To obtain accurate data in this study, observation, interview, elicitation, and introspection techniques were used. The data that has been collected were analyzed using qualitative analysis techniques. The results of the data analysis show that the types of phrases in Makassar consist of six types of phrases with varying formation patterns, namely: noun phrases, verb phrases, adjective phrases, adverb phrases, numeralia phrases, and prepositional phrases. Keywords: word class, phrase, language, Makassar
METODE MODIFIED JACKKNIFE RIDGE REGRESSION DALAM PENANGANAN MULTIKOLINIERITAS (STUDI KASUS INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH)

The human development index is a value where the value showed the measure of living standards comparison in a region. The Human Development Index is influenced by several factors, one of them is the education factor that is the average years of schooling and expected years of schooling. A statistical method to find the correlation between the independent variable and the dependent variable can be conducted using the linear regression method. Linear regression requires several assumptions, one of which is the multicollinearity assumption. If the multicollinearity assumption is not fulfilled, another alternative is needed to estimate the regression parameters. One method that can be used to estimate regression parameters is the ridge regression method with an ordinary ridge regression estimator. Ordinary ridge regression then developed more into several methods, such as generalized ridge regression, jackknife ridge regression, and modified jackknife ridge regression method. The generalized Ridge Regression method causes a reduction to variance in linear regression, while the jackknife ridge regression method is obtained by resampling jackknife process on the generalized ridge regression method. Modified jackknife ridge regression is a combination of generalized ridge regression and jackknife ridge regression method. In this journal, the three ridge regression methods will be compared based on the Mean Squared Error obtained in each method. The results of this study indicate that the jackknife ridge regression method has the smallest MSE value. Keywords: Generalized Ridge Regression, Jackknife Ridge Regression, Modified Jackknife Ridge Regression, Multicolinearity
Czy (a)symetria w obrębie kompleksu orofacjalnego może być związana z preferencją ręki

Introduction: The orofacial region is a common space for various functions such as speech or taking food. It was found that the tongue is lateral braced while speaking. The tongue bracing is a biological feature, independent on the speaker’s language. Chewing is an advanced activity that involves various muscles working coordinately. It is claimed that it is carried out on both or one side: right or left and it may be influenced by various factors. The main hypothesis (H1) in this study is dependence of two variables: dependent (DV), which is preference of side chewing(PCS) and independent (IV), which is handedness described by Laterality Quotient (LQ).Methods: The sample consists of 230 participants aged 21-43. Descriptive statistics were used to capture the distribution of variables. The Spearman’s rang correlation coefficient indicated a weak correlation between chewing and handedness (- .24), therefore in the next step the Chi-Square test of independence was applied to check the association between variables. Finally, the ordinal regression model was estimated to assess the aforementioned relationship. Results: It has been noted that in more than 1/2 of participants the side of chewing corresponds to dominant hand. However it has been noticed that about 1/3 of participants use both sides equally while chewing. It must be though other factors – regardless indicated in the research exclusion criteria – that affect the preference of chewing side (PCS).Conclusions: Analysis, both the Chi-Square test and the ordinal regression model confirmed that activity of the orofacial region while chewing is significantly related to handedness (H1).
Introducing the Voxel Interactive Contour Tool for Online Radiation Intensity Analytics (VICTORIA)

In this paper, the Voxel Interactive Contour Tool for Online Radiation Intensity Analytics (VICTORIA) web viewer is presented as a solution to the issue of inaccessible, expensive, and insecure Digital Imaging and Communications in Medicine (DICOM) and Monte Carlo (MC) dose viewers. VICTORIA is a tool for the visualization and comparison of dose distributions with underlying anatomy that is accessible, free, designed with security in mind, and available on the web. The code is open-source and under the GNU General Public License, and it can be found at the EGSnrc Github repo in the DICOM-viewer branch. The viewer should be useful for researchers using EGSnrc file types .egsphant and .3ddose from the EGSnrc toolkit, patients who want a simple tool to view files, or researchers using DICOM Computed Tomography (CT) and DICOM Radiotherapy (RT) Dose files in low- or middle-income countries without access to treatment planning systems.
Evolution of Activation Functions: An Empirical Investigation

The hyper-parameters of a neural network are traditionally designed through a time consuming process of trial and error that requires substantial expert knowledge. Neural Architecture Search (NAS) algorithms aim to take the human out of the loop by automatically finding a good set of hyper-parameters for the problem at hand. These algorithms have mostly focused on hyper-parameters such as the architectural configurations of the hidden layers and the connectivity of the hidden neurons, but there has been relatively little work on automating the search for completely new activation functions, which are one of the most crucial hyper-parameters to choose. There are some widely used activation functions nowadays which are simple and work well, but nonetheless, there has been some interest in finding better activation functions. The work in the literature has mostly focused on designing new activation functions by hand, or choosing from a set of predefined functions while this work presents an evolutionary algorithm to automate the search for completely new activation functions. We compare these new evolved activation functions to other existing and commonly used activation functions. The results are favorable and are obtained from averaging the performance of the activation functions found over 30 runs, with experiments being conducted on 10 different datasets and architectures to ensure the statistical robustness of the study.
Gaze Estimation using Transformer

Recent work has proven the effectiveness of transformers in many computer vision tasks. However, the performance of transformers in gaze estimation is still unexplored. In this paper, we employ transformers and assess their effectiveness for gaze estimation. We consider two forms of vision transformer which are pure transformers and hybrid transformers. We first follow the popular ViT and employ a pure transformer to estimate gaze from images. On the other hand, we preserve the convolutional layers and integrate CNNs as well as transformers. The transformer serves as a component to complement CNNs. We compare the performance of the two transformers in gaze estimation. The Hybrid transformer significantly outperforms the pure transformer in all evaluation datasets with less parameters. We further conduct experiments to assess the effectiveness of the hybrid transformer and explore the advantage of self-attention mechanism. Experiments show the hybrid transformer can achieve state-of-the-art performance in all benchmarks with pre-training.To facilitate further research, we release codes and models in https://github.com/yihuacheng/GazeTR.
Digital rupee – dissecting the viability of CBDC in India

Crypto Currencies are gaining widespread attention all around the globe. Given many of the disadvantages and high risks involved in crypto transactions, new emergent mechanisms of exchange like Central Bank Digital Currency (CBDC) are entering the market domain which seems relatively promising and less risky. Indian economy can be characterized by ever-rising Non Performing Assets (NPA), low money multiplier, tax evasion, poor tax base, the menace of black money, and high currency deposit ratio (CDR); our research finds that CBDC along with effective policy intervention mechanism can act as a panacea towards ebbing out such perplexities which the economy is confronting. Research points out the poor banking habit among the Indian population which is the primary driving force behind the big divorce between theoretical and actual money multiplier in India. Implementation of CBDC has the requisite potential towards developing banking habits in the masses. Akin to two faces of a coin, with charms come concerns and in the same consonance implementation of CBDC also poses some daunting challenges. First and foremost is the concern of data security in the contemporary milieu when the advancement of science and technology is at its zenith and concomitantly cyber crimes have been witnessing a spur. However, CBDC is a form of cryptocurrency which is characterized by the storage of data using blockchain technology. Under the blockchain framework of data storage, pieces of data are stored in a decentralized manner which makes data theft and deletion an arduous task for cybercriminals. The use of mechanisms like ‘Tokenization’ can also contribute towards securing the data of depositors and preventing frauds and scams. Another impediment in the way is the existing digital divide in the economy, however, implementation of CBDC mechanism in a phased and targeted manner can curb out this impediment to a greater extent. The unprecedented economic crisis owing to the pandemic might have devastating implications in the future and in order to rejuvenate and get all cylinders firing for the Indian economy, CBDC implementation is a dire exigency given the implementation is efficient and in a phased and targeted manner.
On Proximal Policy Optimization's Heavy-Tailed Gradients

Modern policy gradient algorithms, notably Proximal Policy Optimization (PPO), rely on an arsenal of heuristics, including loss clipping and gradient clipping, to ensure successful learning. These heuristics are reminiscent of techniques from robust statistics, commonly used for estimation in outlier-rich (heavy-tailed) regimes. In this paper, we present a detailed empirical study to characterize the heavy-tailed nature of the gradients of the PPO surrogate reward function. We demonstrate pronounced heavy-tailedness of the gradients, specifically for the actor network, which increases as the current policy diverges from the behavioral one (i.e., as the agent goes further off policy). Further examination implicates the likelihood ratios and advantages in the surrogate reward as the main sources to the observed heavy-tailedness. Subsequently, we study the effects of the standard PPO clipping heuristics, demonstrating how these tricks primarily serve to offset heavy-tailedness in gradients. Motivated by these connections, we propose incorporating GMOM (a high-dimensional robust estimator) into PPO as a substitute for three clipping tricks, achieving performance close to PPO (with all heuristics enabled) on a battery of MuJoCo continuous control tasks.
예술대학 신입생을 위한 진로교육 교과목 개발 및 효과 분석 - 진로구성주의를 기반으로

The purpose of this study was to develop a career education curriculum for college freshmen majoring in arts and to analyze the effects on college life adaptation. In this study, the curriculum development principles based on career construction theory were derived, and subjects were developed and applied as a career education methodology to meet new environmental changes and to meet the characteristics for college freshmen majoring in arts. As a results, there was a significant improvement in environmental adaptation and career adaptation in college of arts students' adaptation to college life (environmental adaptation t = 2.553, p = .015, career adaptation t = 2.916, p = .006). However, in the areas of learning, interpersonal relations, and emotional aspects, the average score increased slightly before and after the application of the curriculum, but there was no statistically significant improvement. As a result of in-depth interviews with volunteers, the college of art students showed high satisfaction with the self-understanding process. However, there was still dissatisfaction with the process of creating a story about their life. Based on the results of this study, a multifaceted study should be carried out that addresses specific elements such as career curriculum and programs that reflect the characteristics of each major in the future.
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions

Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360° videos to video-conferencing and live streaming. However, robustly delivering visual content under fluctuating networking conditions on devices of diverse capabilities remains an open problem. In recent years, advances in the field of deep learning on tasks such as super-resolution and image enhancement have led to unprecedented performance in generating high-quality images from low-quality ones, a process we refer to as neural enhancement. In this paper, we survey state-of-the-art content delivery systems that employ neural enhancement as a key component in achieving both fast response time and high visual quality. We first present the components and architecture of existing content delivery systems, highlighting their challenges and motivating the use of neural enhancement models as a countermeasure. We then cover the deployment challenges of these models and analyze existing systems and their design decisions in efficiently overcoming these technical challenges. Additionally, we underline the key trends and common approaches across systems that target diverse use-cases. Finally, we present promising future directions based on the latest insights from deep learning research to further boost the quality of experience of content delivery systems.
Theoretical and Methodological Model of the Influence of Mindfulness Practices on the Teacher’s Personality

The relevance of the study lies in covering the potential of meditative practices in the educational process. The purpose of the study is to carry out a theoretical analysis and outline the probable forms of integration of mindfulness practices into the educational process on the example of teachers, as well as to create a theoretical model of the influence of mindfulness practices on the teacher’s personality with professional destruction. The study highlights the main theoretical models of understanding mindfulness practices. The most effective and most frequently used techniques of awareness practice are analysed. The main educational centres of mindfulness in Ukraine are highlighted. Theoretical analysis of empirical research of foreign colleagues in the field of positive influence of mindfulness practices on the personality is carried out. The causes of professional destruction of teachers are analysed. The expediency and efficiency of this integration are argued. Among the positive changes due to the use of awareness practices by teachers are: improving the level of concentration and recollection; increasing attention to the needs and emotional states of students; creating emotional balance and preventing burnout; increasing the level of stress resistance; improving the quality of relationships at work and at home; increasing productivity and as a result of academic success; forming openness and readiness for selfdevelopment; development of the level of reflection and skills to regulate one’s emotions; improving the psychological climate in the classroom and maintaining overall psychological well-being; improving the quality of life and increasing the subjective feeling of happiness. The scientific novelty lies in the creation and operationalisation of a theoretical model of the influence of mindfulness practices on the teacher’s personality with professional destruction through the lens of cognitive, emotional, physiological, and behavioural levels. It is established that the practice of awareness is the main skill of presence in everything that happens to us, around and inside, in the body, feelings and thoughts
밀레니얼 세대의 그릿과 직무자율성이 회복탄력성 및 잡크래프팅에 미치는 영향 - 호텔기업을 중심으로 -

This study examines whether millennials could see meaning in their career in the hotel industry and how their grit and job autonomy are related to their resilience and job crafting. Finally, it provides solutions for job crafting based on a growth mindset, which is a motivation factor for organizational members. A total of 386 millennials working at 5-star hotels located in Seoul in 2020 participated in this study. Statistical analysis was conducted using an SPSS program and Smart PLS, and research hypotheses were tested using Smart PLS’ structural equation model (SEM). The goodness of fit in the proposed model was evaluated to validate the direct, mediating, and moderating effects of variables, and the moderating effects of growth mindset were estimated using Smart PLS-SEM. Therefore, in the 4th IR era, people with hybrid profiles who have stronger non-cognitive skills such as grit, job autonomy, resilience, and growth mindset will be more competitive compared to those with cognitive skills.
Ensuring information security in the higher school system on the example of medical universities

The activity of modern higher education institution has multidisciplinary nature. It possesses meaningful information resource (including confidential), information technologies, procedures, research results, personal data, etc. The confidential information and novel innovative technologies security has been becoming extremely relevant.
글로벌 대형유통기업의 서비스 실패에 관한 사례 연구: 서비스 마케팅믹스별 실패요인을 중심으로

Purpose: Recently, the managing customer complaints properly has emerged as key source of competitive advantage in the large distribution industry. Effective customer complaint management helps firms minimize service failures and incense the capability to respond to customer''s needs. Despite this importance, the in-depth prior study of a firm''s service failures is very limited. Therefore, the actual service failure cases of large discount stores in Korea were analyzed in this study, and the types of service failures that occur at the service interface were identified.Method: Specifically, a total of 48,307 cases of customer complaints that have occurred in the past three years were collected from 1 January 2016 to 31 December 2018. Using 7 dimensions of service marketing mix. we have classified and analyzed systematically the service failure cases collected.Results: Among the cases of service failures, 34,921 (72.3%) cases were involved with the product factor, followed by 6,152 (12.7%) cases with person factor and 5,392 (11.2%) cases with process factor.Conclusion: By linking the main causes of service failure with the service marketing mix variables, this research presented a more systematic analytic model and verified by applying it to large domestic distribution company. Understanding the main factors affecting customer complaints n the large distribution industry can provide managers useful information and insight who want to achieve an effective customer complaint management.