Reviewerly/exHarmony huggingface dataset:
Robustness Implies Generalization via Data-Dependent Generalization Bounds

This paper proves that robustness implies generalization via data-dependent generalization bounds. As a result, robustness and generalization are shown to be connected closely in a data-dependent manner. Our bounds improve previous bounds in two directions, to solve an open problem that has seen little development since 2010. The first is to reduce the dependence on the covering number. The second is to remove the dependence on the hypothesis space. We present several examples, including ones for lasso and deep learning, in which our bounds are provably preferable. The experiments on real-world data and theoretical models demonstrate near-exponential improvements in various situations. To achieve these improvements, we do not require additional assumptions on the unknown distribution; instead, we only incorporate an observable and computable property of the training samples. A key technical innovation is an improved concentration bound for multinomial random variables that is of independent interest beyond robustness and generalization.
A Bird\'s Eye View of Human Facial Expression Recognition Techniques

bstract: Human Face Expression Recognition is one of the most remarkable and promising errands in friendly interaction. For the most part, facial emotions are normal and straight forward means for human creatures to impart their expressions and goals. Facial emotions are the critical attributes of non-verbal interactions. In this paper depicts the review of Face Expression Recognition (FER) strategies which incorporate the three significant steps, for example, preprocessing, face detection and emotion classification. In this review work, clarifies the different kinds of FER procedures with its significant handout. Till now many works is done in Facial expression recognition. This paper will in general concentrate on the work done in recognition utilizing various methods. Facial recognition is significant viewpoint in making more human like PCs where human intelligent machine can be construct. Various methods are utilized till now on various viewpoints; in this paper we will be mainly focusing on feature based facial expression recognition. This paper additionally centers on classification strategies.
Networking as a new type of partnership in education and science

The transition from an industrial to a post-industrial society is determined, first of all, by building an open society, which puts the ideas of rationalism, freedom of innovation, and the promotion of ideas at the forefront. An open society gives rise to open structures, including in higher education. Universities, as platforms for collective creativity, learning, innovation, are opening to meet social needs, work to transform the economy in particular and society as a whole. These are no longer just universities with an established network of connections and strong international relations; they are already hubs that implement networking, which allows different participants to create new educational and scientific products through cooperation, exchange of resources and experience. This article analyzes the world experience in building educational hubs, highlights the main elements of their architecture. Based on the experience of the D.Serikbayev East Kazakhstan Technical University, the assumption proved that the transformation of a university into a hub is a natural step for organizations that have public recognition, strong science, and innovative educational projects. The methodology for creating a network format for the life of the university is described in detail, the process of developing common universal approaches to the implementation of the network interaction of the university in the educational and scientific fields is characterized, and the concept and model of the hub chosen by the university is substantiated. The format of network interaction of EKTU allows successfully implementing projects in the educational, research, and production areas.
Learning neural state-space models: do we need a state estimator?

In recent years, several algorithms for system identification with neural state-space models have been introduced. Most of the proposed approaches are aimed at reducing the computational complexity of the learning problem, by splitting the optimization over short sub-sequences extracted from a longer training dataset. Different sequences are then processed simultaneously within a minibatch, taking advantage of modern parallel hardware for deep learning. An issue arising in these methods is the need to assign an initial state for each of the sub-sequences, which is required to run simulations and thus to evaluate the fitting loss. In this paper, we provide insights for calibration of neural state-space training algorithms based on extensive experimentation and analyses performed on two recognized system identification benchmarks. Particular focus is given to the choice and the role of the initial state estimation. We demonstrate that advanced initial state estimation techniques are really required to achieve high performance on certain classes of dynamical systems, while for asymptotically stable ones basic procedures such as zero or random initialization already yield competitive performance.
Application-Based Usability Evaluation Metrics

Abstract Testing is one of the critical stages in the software development life cycle (SDLC). Usability testing is a very important field that helps the applications be usable and easy for the users. Because of the importance of usability testing, a metrics has been developed to help in measuring the usability through converting the main qualitative usability attributes in ISO to quantitative steps that provide the developer a framework to follow in developing to achieve usability of their applications and helps the tester with a checklist and a tool to measure the usability percentage of their application. The framework provides a set of steps to achieve the usability attributes and answers the question of how you could measure this attribute with the defined steps. The framework results in a 95% average accuracy in the high-rate application and a 59% average accuracy in the low-rate application. Finally, the framework is programmed in a tool to measure the usability percentage of the application through a checklist and provides a scheme to help the developer achieve the best results in usability.
Socio-Pedagogical Role of General Education Teachers in Supporting Personal Development

The socio-pedagogical role of general education teachers is an integral part of their professional status in kindergartens and schools. In recent years in Bulgaria, with the development of the concept of inclusive education, the responsibilities of pedagogical specialists in terms of providing support for personal development have increased. These responsibilities are also the basis of the socio-pedagogical construction as a prerequisite for the development of pedagogical competencies. This article presents parts of a research among pedagogical specialists to recognise socio-pedagogical support as their essential professional competence in modern education.
RF Signal Classification with Synthetic Training Data and its Real-World Performance

Neural nets are a powerful method for the classification of radio signals in the electromagnetic spectrum. These neural nets are often trained with synthetically generated data due to the lack of diverse and plentiful real RF data. However, it is often unclear how neural nets trained on synthetic data perform in real-world applications. This paper investigates the impact of different RF signal impairments (such as phase, frequency and sample rate offsets, receiver filters, noise and channel models) modeled in synthetic training data with respect to the real-world performance. For that purpose, this paper trains neural nets with various synthetic training datasets with different signal impairments. After training, the neural nets are evaluated against real-world RF data collected by a software defined radio receiver in the field. This approach reveals which modeled signal impairments should be included in carefully designed synthetic datasets. The investigated showcase example can classify RF signals into one of 20 different radio signal types from the shortwave bands. It achieves an accuracy of up to 95 % in real-world operation by using carefully designed synthetic training data only.
Predicting within and across language phoneme recognition performance of self-supervised learning speech pre-trained models

In this work, we analyzed and compared speech representations extracted from different frozen self-supervised learning (SSL) speech pre-trained models on their ability to capture articulatory features (AF) information and their subsequent prediction of phone recognition performance for within and across language scenarios. Specifically, we compared CPC, wav2vec 2.0, and HuBert. First, frame-level AF probing tasks were implemented. Subsequently, phone-level end-to-end ASR systems for phoneme recognition tasks were implemented, and the performance on the frame-level AF probing task and the phone accuracy were correlated. Compared to the conventional speech representation MFCC, all SSL pre-trained speech representations captured more AF information, and achieved better phoneme recognition performance within and across languages, with HuBert performing best. The frame-level AF probing task is a good predictor of phoneme recognition performance, showing the importance of capturing AF information in the speech representations. Compared with MFCC, in the within-language scenario, the performance of these SSL speech pre-trained models on AF probing tasks achieved a maximum relative increase of 34.4%, and it resulted in the lowest PER of 10.2%. In the cross-language scenario, the maximum relative increase of 26.7% also resulted in the lowest PER of 23.0%.
Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and Effect Classification

This paper introduces causal scoring as a novel approach to frame causal estimation in the context of decision making. Causal scoring entails the estimation of scores that support decision making by providing insights into causal effects. We present three valuable causal interpretations of these scores: effect estimation (EE), effect ordering (EO), and effect classification (EC). In the EE interpretation, the causal score represents the effect itself. The EO interpretation implies that the score can serve as a proxy for the magnitude of the effect, enabling the sorting of individuals based on their causal effects. The EC interpretation enables the classification of individuals into high- and low-effect categories using a predefined threshold. We demonstrate the value of these alternative causal interpretations (EO and EC) through two key results. First, we show that aligning the statistical modeling with the desired causal interpretation improves the accuracy of causal estimation. Second, we establish that more flexible causal interpretations are plausible in a wider range of settings and propose conditions to assess their validity. We showcase the practical utility of causal scoring through diverse scenarios, including situations involving unobserved confounding due to self-selection, lack of data on the primary outcome of interest, or lack of data on how individuals behave when intervened. These examples illustrate how causal scoring facilitates reasoning about flexible causal interpretations of statistical estimates in various contexts. They encompass confounded estimates, effect estimates on surrogate outcomes, and even predictions about non-causal quantities as potential causal scores.
Application of Inlislite Version 3.0 at Library and Archives Service of Bengkulu Province

Background of the study: An application is a pair or installation of technology in a library in the form of a service transition, where the transition is moving the library to realize service renewal and the use of technology into their daily activities in order to increase to usability of a library. This study aims to determine how the application INLISLite version 3.0 at the library and archives service of Bengkulu province in improving its services for users who need the information. Purpose: This study aims to find out how the application of INLISLite version 3.0 at the Bengkulu Province Library and Archives Service uses the TAM (Technology Accepted Model) theory from Davis. Method: The design of this research is descriptive qualitative. The research was conducted by in-depth interviews with three informants from the circulation service section. Researchers use the theory of TAM (Technology Accepted Model) from Davis, focusing on perceptions of convenience and usefulness Findings: The results of the study show that the application of INLISLite version 3.0 at the Bengkulu Provincial Library and Archives Service has been carried out well, very easy and useful in helping the work of the circulation librarian. Conclusion: The application of the INLISLite application version 3.0 at the Bengkulu Provincial Library and Archives Service is very easy and useful in helping the work of librarians who are reviewed through the TAM model with two indicators of convenience and usability, each of which has 6 important aspects, namely easy to learn, controllable, and easy to use. understandable, flexible, easy to skill, easy to use, speed up work, improve performance, increase productivity, effectiveness, simplify work and be useful.
Privacy Preserving Homomorphic Encryption In Cloud Storage

The incredible speed with which Information Technology (IT) has evolved in recent decades has brought about a major change in people's daily lives and in practically all areas of knowledge. The diversification of means of access using mobile devices, the evolution of technologies such as virtualization, added to a growing demand from users for new systems and services adapted to these new market trends, were the fuel for the emergence of a new paradigm, Cloud Computing. The general objective of this paper is to enable the offer of privacy preservation system provided by third parties through which Cloud Data Storage Services customers can continuously monitor the integrity of their files.
Are We All in a Truman Show? Spotting Instagram Crowdturfing through Self-Training

Influencer Marketing generated $16 billion in 2022. Usually, the more popular influencers are paid more for their collaborations. Thus, many services were created to boost profiles' popularity metrics through bots or fake accounts. However, real people recently started participating in such boosting activities using their real accounts for monetary rewards, generating ungenuine content that is extremely difficult to detect. To date, no works have attempted to detect this new phenomenon, known as crowdturfing (CT), on Instagram. In this work, we propose the first Instagram CT engagement detector. Our algorithm leverages profiles' characteristics through semi-supervised learning to spot accounts involved in CT activities. Compared to the supervised approaches used so far to identify fake accounts, semi-supervised models can exploit huge quantities of unlabeled data to increase performance. We purchased and studied 1293 CT profiles from 11 providers to build our self-training classifier, which reached 95\% F1-score. We tested our model in the wild by detecting and analyzing CT engagement from 20 mega-influencers (i.e., with more than one million followers), and discovered that more than 20% was artificial. We analyzed the CT profiles and comments, showing that it is difficult to detect these activities based solely on their generated content.
Causal Dynamics Learning for Task-Independent State Abstraction

Learning dynamics models accurately is an important goal for Model-Based Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics model which is vulnerable to spurious correlations and therefore generalizes poorly to unseen states. In this paper, we introduce Causal Dynamics Learning for Task-Independent State Abstraction (CDL), which first learns a theoretically proved causal dynamics model that removes unnecessary dependencies between state variables and the action, thus generalizing well to unseen states. A state abstraction can then be derived from the learned dynamics, which not only improves sample efficiency but also applies to a wider range of tasks than existing state abstraction methods. Evaluated on two simulated environments and downstream tasks, both the dynamics model and policies learned by the proposed method generalize well to unseen states and the derived state abstraction improves sample efficiency compared to learning without it.
Zero Inflated and Zero Truncated Negative Binomial Weighted Garima Distributions for Modeling Count Data

In this paper, we introduced models for zero inflated and zero truncated based on the negative binomial-weighted Garima (NB-WG) distribution.The zero inflated negative binomialweighted Garima (ZINB-WG) distribution is a discrete probability distribution for the excessive zero counts and overdispersion which is a mixture of Bernoulli distribution and negative binomial-weighted Garima (NB-WG) distribution.Meanwhile, a new zero truncated distribution named as the zero truncated negative binomial-weighted Garima (ZTNB-WG) distribution can be used when the response variable is the set of positive integers.Some properties of the two different versions of NB-WG distribution are discussed and the estimation of the parameters is derived by maximum likelihood method (MLE).In addition, the usefulness of the proposed distributions is illustrated by real data sets.
A Variational Quantum Algorithm For Approximating Convex Roofs

Many entanglement measures are first defined for pure states of a bipartite Hilbert space, and then extended to mixed states via the convex roof extension. In this article we alter the convex roof extension of an entanglement measure, to produce a sequence of extensions that we call $f$-$d$ extensions, for $d \in \mathbb{N}$, where $f:[0,1]\to [0, \infty)$ is a fixed continuous function which vanishes only at zero. We prove that for any such function $f$, and any continuous, faithful, non-negative function, (such as an entanglement measure), $μ$ on the set of pure states of a finite dimensional bipartite Hilbert space, the collection of $f$-$d$ extensions of $μ$ detects entanglement, i.e. a mixed state $ρ$ on a finite dimensional bipartite Hilbert space is separable, if and only if there exists $d \in \mathbb{N}$ such that the $f$-$d$ extension of $μ$ applied to $ρ$ is equal to zero. We introduce a quantum variational algorithm which aims to approximate the $f$-$d$ extensions of entanglement measures defined on pure states. However, the algorithm does have its drawbacks. We show that this algorithm exhibits barren plateaus when used to approximate the family of $f$-$d$ extensions of the Tsallis entanglement entropy for a certain function $f$ and unitary ansatz $U(θ)$ of sufficient depth. In practice, if additional information about the state is known, then one needs to avoid using the suggested ansatz for long depth of circuits.
New Additions to the Guitar Family: Lego and Automatic Microtonal Guitars

One of the modern classical guitar’s biggest strengths is also one of its biggest weaknesses. The instrument depends on an equal temperament fretboard system that has lent itself to brilliant repertoire and ease of production, yet this same fretboard system does not lend itself to perfectly tuned notes and tuning systems not found in Eurocentric classical music. Many guitarists and luthiers have sought a fretboard system that marries the benefits of equal temperament with the rich possibilities of just intonation and other tuning systems. This paper presents two new fretboard designs and discuss their role in the history of guitar fretboard construction. Our first design is a fully functional fretboard made from the popular toy ‘Legos’ and solves many of the problems that previous designs had, including ease of use and production, affordability, and versatility. Our second design is a one-string prototype for an automatic (automated) fretboard that would allow performers to switch tuning systems instantly at the press of a button.
Main mechanisms of blockchain technology implementation in digital technologies application

Purpose. To analyze the basic principles of blockchain technology implementation, highlighting the algorithms for reaching consensus in the blockchain network to ensure its reliability; to identify key problems in the implementation of such technology and suggest ways to overcome them; to perform a systematic analysis of the blockchain technology contradictions and suggest ways to eliminate them. Methodology. The research used the basics of economic analysis of economic entities to compare their managements centralized and decentralized models. This approach showed that another wave of transformation of business and social models has unfolded in recent years, caused by the next-generation digital technologies involving the economys transition to the digital area. Revealing the blockchain technology mechanisms is based on modern databases and peer-to-peer computer networks, covering in detail the main means of contradictions, application, and implementation of blockchain technology. Findings. The main results of these studies are obtained in the digital economy. The paper shows that digital technologies open up a wide range of opportunities for different sectors of the economy. The research highlights the features and principles of distributed registry technology (Blockchain) applications. It is shown that as a decentralized data registry, blockchain technology is the most discussed and relevant topic in the digital economy. Originality. The paper further developed the main component of the digital economy, which is progressing most intensively, the distributed ledger technology (Blockchain). The paper analyzed its strengths, such as cost reduction, increased security, and transparency of transactions that attracted the attention of various sectors of the economy. The authors approach for eliminating the revealed mechanisms of contradictions, application, and implementation of blockchain technology is presented. The definition of the digital economy, digital technologies in the economy, and end-to-end digital technologies have been improved to clarify the understanding of the economic management decentralization problems. That showed that the digital economy has several subtleties associated with insufficient research and comprehension of technical implementation and flexibility. Practical value. The research results will be useful for expanding ideas about the blockchain technology implementation in different sectors of the economy, accompanied by lower costs, increased security and transparency of economic entities, and improving their economic efficiency and development in digital technologies application. The blockchain technologies implantation at the enterprises of the mining and metallurgical industry allows making the production and sales of products more efficient and transparent, and at the same time significantly reduces the human factor.