Reviewerly/exHarmony huggingface dataset:
ELEMENTS OF THE FILTERING THEORY AND THE THEORY OF INTERVAL AVERAGES IN THE APPLICATION TO THE PROBLEMS OF ANALYZING THE QUALITY OF ELECTRONIC LIBRARIES

The role and place of electronic libraries as one of the key elements of the foundation of the unified information space of Russia are characterized. The features that determine the complexity (level) of methods and tools for analyzing the state and quality of electronic libraries and impose increasingly high requirements on algorithms for evaluating the quality of software and hardware and digital content are shown. Some key aspects of the formulation and content filling of the basic stages of the methodology for analyzing the quality of electronic libraries ardescribed. It is shown that these aspects are essential in the framework of obtaining interval partial (lower and upper) quality estimates and generalized quality estimates of systems of this class using the methods of the theory of interval averages. An approach combining the methods of the theory of interval averages and traditional methods of Kalman filtering (extrapolation) is proposed) to analyze the quality of electronic libraries. The practical implementation of the methodology will allow us to obtain estimated values of specific system parameters at time intervals, which will increase the reliability of the quality analysis and the adequacy of the management of the structure, parameters and modes of operation of objects of this class.
Constrained-Based Differential Privacy (Invited Talk)

Data sets and statistics about groups of individuals are increasingly collected and released, feeding many optimization and learning algorithms. In many cases, the released data contain sensitive information whose privacy is strictly regulated. For example, in the U.S., the census data is regulated under Title 13, which requires that no individual be identified from any data released by the Census Bureau. In Europe, data release is regulated according to the General Data Protection Regulation, which addresses the control and transfer of personal data.Differential privacy has emerged as the de-facto standard to protect data privacy. In a nutshell, differentially private algorithms protect an individual’s data by injecting random noise into the output of a computation that involves such data. While this process ensures privacy, it also impacts the quality of data analysis, and, when private data sets are used as inputs to complex machine learning or optimization tasks, they may produce results that are fundamentally different from those obtained on the original data and even rise unintended bias and fairness concerns.In this talk, I will first focus on the challenge of releasing privacy-preserving data sets for complex data analysis tasks. I will introduce the notion of Constrained-based Differential Privacy (C-DP), which allows casting the data release problem to an optimization problem whose goal is to preserve the salient features of the original data. I will review several applications of C-DP in the context of very large hierarchical census data, data streams, energy systems, and in the design of federated data-sharing protocols. Next, I will discuss how errors induced by differential privacy algorithms may propagate within a decision problem causing biases and fairness issues. This is particularly important as privacy-preserving data is often used for critical decision processes, including the allocation of funds and benefits to states and jurisdictions, which ideally should be fair and unbiased. Finally, I will conclude with a roadmap to future work and some open questions.
Speed Maps: An Application to Guide Robots in Human Environments

We present the concept of speed maps: speed limits for mobile robots in human environments. Static speed maps allow for faster navigation on corridors while limiting the speed around corners and in rooms. Dynamic speed maps put limits on speed around humans. We demonstrate the concept for a mobile robot that guides people to annotated landmarks on the map. The robot keeps a metric map for navigation and a semantic map to hold planar surfaces for tasking. The system supports automatic initialization upon the detection of a specially designed QR code. We show that speed maps not only can reduce the impact of a potential collision but can also reduce navigation time.
A Guidebook to Arbitrating Disputes Involving Blockchains and Smart Agreements Part 2 of 3 Where the Disputes Lie: When Blockchain Technology Will Need Help Sorting Out Its Contract

In Part 1 of this article, the authors introduced the types of agreements that use blockchain technology at “A Guidebook to Arbitrating Disputes Involving Blockchains and Smart Agreements. In this part, the authors discuss the types of agreements that use Blockchain Ledger technology by completing their introduction with an examination of smart agreements.
SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation

Natural language instructions for visual navigation often use scene descriptions (e.g., bedroom) and object references (e.g., green chairs) to provide a breadcrumb trail to a goal location. This work presents a transformer-based vision-and-language navigation (VLN) agent that uses two different visual encoders -- a scene classification network and an object detector -- which produce features that match these two distinct types of visual cues. In our method, scene features contribute high-level contextual information that supports object-level processing. With this design, our model is able to use vision-and-language pretraining (i.e., learning the alignment between images and text from large-scale web data) to substantially improve performance on the Room-to-Room (R2R) and Room-Across-Room (RxR) benchmarks. Specifically, our approach leads to improvements of 1.8% absolute in SPL on R2R and 3.7% absolute in SR on RxR. Our analysis reveals even larger gains for navigation instructions that contain six or more object references, which further suggests that our approach is better able to use object features and align them to references in the instructions.
Dealing with the Unknown: Pessimistic Offline Reinforcement Learning

Reinforcement Learning (RL) has been shown effective in domains where the agent can learn policies by actively interacting with its operating environment. However, if we change the RL scheme to offline setting where the agent can only update its policy via static datasets, one of the major issues in offline reinforcement learning emerges, i.e. distributional shift. We propose a Pessimistic Offline Reinforcement Learning (PessORL) algorithm to actively lead the agent back to the area where it is familiar by manipulating the value function. We focus on problems caused by out-of-distribution (OOD) states, and deliberately penalize high values at states that are absent in the training dataset, so that the learned pessimistic value function lower bounds the true value anywhere within the state space. We evaluate the PessORL algorithm on various benchmark tasks, where we show that our method gains better performance by explicitly handling OOD states, when compared to those methods merely considering OOD actions.
The Role of Social Media Influence on the Marketing Performance: Moderating Role of Customer Satisfaction

Currently, Social media has played a significant role to fulfill the customers’ demand and enhance the marketing performance of the organization and attained the focus of recent studies and regulators. Thus, the present study investigates the impact of social media strategies, social media analysts, and active presence on social media on the social media performance and social media performance impact of marketing performance of the electronic industry in UAE. This study also examines the mediating role of social media performance among the nexus of social media strategies, social media analysts, active presence on social media and marketing performance, and the moderating role of customer satisfaction among the relationships of social media and marketing performance. The questionnaires have been used to gather the data, and smart-PLS has been used to analyze the data. The results revealed that social media strategies and active presence on social media have a positive association with social media performance and social media performance has a positive association with marketing performance. The outcomes also indicated that social media performance positively mediates among the nexus of social media strategies, social media analysts, active presence on social media, and social media performance, and the customer satisfaction significantly moderates among the nexus of social media performance and marketing performance. This study helped the regulation-making authorities and guided them to enhance their focus towards social media that could enhance the marketing performance.
New frontiers in marketing research methods: forensic marketing - using forensic science frameworks and methods in marketing research

Forensic science has been prominently used in many fields in science. Forensic science is most practiced prominently in six major fields of science. More recently, forensics has been used as a research model in the field of business. Thus, we have forensic economics, which has been established for 40 years, and. forensic accounting has been established for 30 years. Borrowing techniques from both business fields, we now have forensic marketing. The purpose of this chapter is to illustrate a new forensic investigative research model. This chapter will add to the field and foundation to the theory of marketing This chapter will illustrate a five-point forensic marketing research model: (a) market position forensics; (b) marketing law and policy forensics; (c) marketing financials and pricing forensics; (d) brand offering forensics; and (e) distribution channels forensics. This chapter is present a new breakthrough in marketing by offering a forensic investigative technique in marketing.
Finding the “Just Right” Tools for Environmental Geochemistry Research at the Tar Creek Superfund Site Ottawa County, OK

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Finding the “Just Right” Tools for Environmental Geochemistry Research at the Tar Creek Superfund Site Ottawa County, OKAuthorsClaireHayhowiDDanielBrabanderiDRebeccaJimMartinLivelySee all authors Claire HayhowiDCorresponding Author• Submitting AuthorWellesley CollegeiDhttps://orcid.org/0000-0002-7940-4598view email addressThe email was not providedcopy email addressDaniel BrabanderiDWellesley CollegeiDhttps://orcid.org/0000-0002-2967-3087view email addressThe email was not providedcopy email addressRebecca JimLEAD Agencyview email addressThe email was not providedcopy email addressMartin LivelyLEAD Agencyview email addressThe email was not providedcopy email address
Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training

The mean field (MF) theory of multilayer neural networks centers around a particular infinite-width scaling, where the learning dynamics is closely tracked by the MF limit. A random around this infinite-width limit is expected from a large-width expansion to the next order. This has been studied only in shallow networks, where previous works employ heavily technical notions or additional formulation ideas amenable only to that case. Treatment of the multilayer case has been missing, with the chief difficulty in finding a formulation that captures the stochastic dependency across not only time but also depth. In this work, we initiate the study of the in the case of multilayer networks, at any network depth. Leveraging on the neuronal embedding framework recently introduced by Nguyen and Pham, we systematically derive a system of dynamical equations, called the second-order MF limit, that captures the limiting distribution. We demonstrate through the framework the complex interaction among neurons in this second-order MF limit, the stochasticity with cross-layer dependency and the nonlinear time evolution inherent in the limiting fluctuation. A limit theorem is proven to relate quantitatively this limit to the of large-width networks. We apply the result to show a stability property of gradient descent MF training: in the large-width regime, along the training trajectory, it progressively biases towards a solution with minimal fluctuation (in fact, vanishing fluctuation) in the learned output function, even after the network has been initialized at or has converged (sufficiently fast) to a global optimum. This extends a similar phenomenon previously shown only for shallow networks with a squared loss in the ERM setting, to multilayer networks with a loss function that is not necessarily convex in a more general setting.
Sovereign digital currency system with decentralized infrastructure

In this paper, we consider the concept of a 1-tier national monetary system based on a digital sovereign currency issued by a central bank. In contrast to the approach, based on a centralized technical infrastructure, used in most, if not all, CBDC projects, we suggest to use a decentralized infrastructure. Such an infrastructure can be thought of as a peer-to-peer network consisting of nodes, which are equipotent participants and are equally privileged, each providing a complete set of services required to maintain digital wallets and process transactions with digital currency. Also, unlike most of CBDC projects, two possible formats of digital titles of property are considered: digital accounts with continuously modifiable amount and token-like digital coins with a fixed non-modifiable amount. The suggested model of currency system involves built-in payment mechanisms enabling one to carry out, both online and offline, transfers of digital currency with immediate finality and irrevocability. In this paper, we present an overview of the suggested model of currency system.
Deep Semantic Edge for Cell Counting and Localization in Time-Lapse Microscopy Images

Cell counting and localization in microscopy images is one of the most important steps in Assisted Reproduction Technology (ART). This paper proposes a fully automatic method to achieve this goal, which consists of three main parts. The first part is region of interest(ROI) extracting and cell counting. These two tasks are transfered to a multi-class target detection problem that can be jointly solved by existing deep learning methods. To impose temporal consistency that the number of cells should be non-decreasing in time, a simple yet effective post-processing strategy is adopted to smooth the result. Secondly, an improved network with a new upsampling module and a new loss function is proposed to predict crisp and complete contours of cells from a higher semantic level. The edges are more robust to noise, blurring and occlusion, which facilitates the following ellipse fitting a lot. Finally, elliptical proposals are fitted on the extracted contours and correct ones are selected in an effective way. Extensive experiments are carried out to show the advantages of the proposed method.
Quadrotor Control on SU(2) X R3 with SLAM Integration

We present a trajectory tracking controller for a quadrotor unmanned aerial vehicle (UAV) configured on $SU(2)\times R^3$, and relate this result to a family of geometric tracking controllers on $SO(3)\times R^3$. The theoretical results are complemented by simulation examples, and the controller is subsequently implemented in practice and integrated with a simultaneous localization and mapping (SLAM) system through an extended Kalman filter (EKF). This facilitates the operation of the UAV without external motion capture systems, and we demonstrate that the proposed control system can be used for inventorying tasks in a supermarket environment without external positioning systems.
Accuracy Comparison of VGG-19 Model and Combined Resnet-50 and VGG-19 Model

VGGNet and ResNet have become popular algorithms for classifying images. This paper will compare the accuracy of the VGG‑19 model when used alone and the accuracy of VGG‑19 model when used in combination with the Resnet‑50 model. Both the models are pre trained models, and we have used these pre trained models on a dataset. The dataset consists of 133 classes of dog breeds with a total of 6680 training images and 836 test images. The objective of this study is to understand if it is more suitable to use VGGNet alone or if combining it with ResNet will provide better accuracy.
MODULE DEVELOPMENT OF ENCOURAGE EXPRESSION TO IMPROVE THE SOCIALEDUCATION COMPETENCY OF EARLY CHILDHOOD EDUCATION PROGRAMS

This research aims to know: 1) the way to develop the module of encourages expression, 2) the module ofencourage expression is able to improve the teacher of early childhood education programs competency. Themethod research used is the method research and development as well the experimental method. Collectingthe data use: interviewing guidance, questionnaire, pre-test- posttest and documentation. The data is analyzeddescriptive qualitatively and quantitative. The research result is 1) produced the module od encourageexpression which has been developed through the expert validity, limited testing, extensive testing, 2) themodule of encourage expression is able to improve the teacher competency of early childhood educationprograms. This case is showed by: a) the teacher transformation on the cognitive, affective and behavioraspect in dealing with the children, b) the teachers begin to accustom themselves to use the encourageexpression.
A Multi-objective Structure Variant Bacterial Heuristic Feature Selection Method in High-dimensional Data Classification

Feature selection (FS) has been studied as a multi-objective problem in recent years to improve the classification effect. In this paper, to expand the ability of Bacterial Foraging Optimization (BFO) in feature selection problems for classification, a Multi-objective Adapting Chemotaxis Bacterial Foraging Optimization (abbreviated as MOACBFO) is proposed. In MOACBFO, a structural variation strategic model is proposed to reduce the computational complexity for multi-objective problems and applied with a dynamically updated external matrix to record the performance of bacteria on two objectives. In addition, an adaptive chemotaxis step mechanism is designed to help the bacteria jump out of the local optimality. To further enhance the diversity of bacteria searching capability, a feature subset updating strategy is developed. The optimal feature subset and fitness value are stored in the external matrix continuously by comparing them with the historical value records. The performance of the proposed algorithm is demonstrated by comparing it with four other advanced swarm intelligent algorithms on 11 high-dimensional microarray datasets. The results indicate that the MOACBFO performs better in achieving a lower classification error rate using a small number of features.
The Potential of App Using for Interactive Smart Learning Environments

A “smart learning” environment requires a combination of both formal and informal learning experiences along with the involvement of immersive activities. Accordingly, contextualized learning resources can be formed instantly, including digital content, activities, and connection during the learning process. Applications (apps) developed on smartphones (or mobile devices), with their ultimate purposes, can assist the adaptive, personalized, and ubiquitous learning experience for students. However, creating algorithms for the learning process by apps remains a problem for both teachers and students. Apps can be integrated as both specific subject tools and smart learning activities for digital literacy in various education programs of higher education institutions.
Hidden nonmacrorealism: Reviving the Leggett-Garg inequality with stochastic operations

The Leggett-Garg inequality (LGI) distinguishes nonmacrorealistic channels from macrorealistic ones by constraining the experimental outcomes of the underlying system. In this work, we propose a class of channels which, initially, cannot violate the LGI (in the form of the temporal Bell inequality) but can violate it after the application of stochastic pre- and post- operations (SPPOs). As a proof-of-principle experiment, we demonstrate the stochastic pre- and post- operations in an amplitude-damping channel with photonic qubits. We denote the above phenomenon as hidden nonmacrorealistic channels. We also discuss the relationship between this hidden nonmacrorealistic channels (in terms of the temporal Clauser-Horne-Shimony-Holt (CHSH) inequality) and the strongly nonlocality-breaking channel, which breaks the hidden spatial CHSH nonlocality for arbitrary states. In general, if the channel satisfies hidden nonmacrorealism, it is not a strongly CHSH nonlocality-breaking channel.
Application Research of Higher Vocational English Teaching Platform Based on Machine Learning Supported by Information Technology

Compared with traditional English classroom teaching, higher vocational English teaching based on network platform is more conducive to the development of professional quality of higher vocational students. Teachers and students can find relevant materials in the platform according to the needs of courses and professional skills, which is more suitable than simple book teaching and can provide higher vocational students with English learning materials and a good language environment related to their majors. The development of network information technology provides rich resources and a good platform for the reform of English teaching mode in higher vocational colleges. Combining network information technology with teaching content improves the efficiency of classroom teaching and promotes the efficiency of students' extracurricular preparation. This paper expounds the advantages of network platform in higher vocational English teaching, and analyzes the significance of information technology in higher vocational English teaching. Then, it discusses the specific application of information technology in higher vocational English teaching from the related technical level of English teaching platform based on machine learning, and points out that both teachers and students should use information technology correctly in order to give full play to the advantages of information teaching.
Jubilee: Secure Debt Relief and Forgiveness

JUBILEE is a securely computed mechanism for debt relief and forgiveness in a frictionless manner without involving trusted third parties, leading to more harmonious debt settlements by incentivising the parties to truthfully reveal their private information. JUBILEE improves over all previous methods:- individually rational, incentive-compatible, truthful/strategy-proof, ex-post efficient, optimal mechanism for debt relief and forgiveness with private information- by the novel introduction of secure computation techniques to debt relief, the “blessing of the debtor” is hereby granted for the first time: debt settlements with higher expected profits and a higher probability of success than without using secure computationA simple and practical implementation is included for “The Secure Spreadsheet”.Another implementation is realised using Raziel smart contracts on a blockchain with Pravuil consensus.
A Brief on Innovation towards Forensic Science

Digital forensics (sometimes referred to as digital forensic science) may be a branch of forensic science encompassing the recovery and investigation of fabric found in digital devices, often in reference to computer crime. The term digital forensics was originally used as a synonym for computer forensics but has expanded to hide investigation of all devices capable of storing digital data. With roots within the personal computing revolution of the late 1970s and early 1980s, the discipline evolved during a haphazard manner during the 1990s, and it had been not until the first 21st century that national policies emerged.