Reviewerly/exHarmony huggingface dataset:
Effects of transfer learning for handwritten digit classification in a small training sample size situation

A deep learning approach is believed to be one of the most useful for image pattern recognition. Generally, deep learning requires a large number of samples. In particular, it is prone to overlearning when the number of training samples is small. However, it is common for practical pattern recognition problems to use a limited number of training samples. One way to design a deep neural network with such a small number of training samples is to use transfer learning. Transfer learning, known to be pre-trained with a large number of samples, and its pre-trained neural networks are expected to be applicable to other pattern recognition problems, especially when the number of training samples is small. It is difficult to develop a handwritten character classification system because handwritten characters are not always readily available and the number of samples is generally small. In this paper, we examine effects of transfer learning for handwritten digit classification under a small number of training samples. Experimental results show that transfer learning is more effective than convolutional neural networks (CNNs) in classifying handwritten digits.
Depth-Based Reinforcement Learning Algorithm

With the continuous development of Internet, cloud computing, 5g technology and we media industry, people generate and obtain more and more information every day, and most of these information exists in the form of text. Automatic text technology can quickly help people obtain clear and readable information in massive text. Aiming at the problem that the traditional text technology based on deep learning can not generate high-quality long text summary, this paper explores a deep proxy text generation technology based on reinforcement learning. The model divides the long-term and short-term memory network into different agents, and each agent is responsible for encoding a part of the source text. After that, each agent will broadcast its own coding results, so that all agents can share the global context information. After the final coding, the model integrates the information from multiple agents through a context agent attention mechanism and transmits it to the decoder, so as to optimize the process of long text summary generation.
Model Construction of Digital Audit System for Power Grid Enterprises Driven by Knowledge Atlas

As a new technical tool, knowledge mapping plays an important role in traditional audit. By building a knowledge map and combining knowledge and data, information can be transformed into knowledge and applied to audit work, which is helpful to solve the problems of inconsistency and unsystematic in traditional audit. State Grid Energy Internet Research Institute (hereinafter referred to as “State Grid Energy Internet Research Institute”) relies on knowledge mapping technology to build a digital audit system for power grid enterprises, which is the foundation and support platform for achieving comprehensive audit applications, and is of great significance for power grid enterprises to establish a digital audit system. Based on the existing knowledge map platform of State Grid Corporation of China, this paper analyzes the development process of audit practice of power grid enterprises over the years, combs the relationship and development status of knowledge map and audit work, and summarizes the value and application of knowledge map in the audit of power grid enterprises. The application mode of knowledge map in power grid enterprise audit is proposed.
Application of ant colony algorithm in distributed artificial intelligence

Ant colony algorithm is a distributed problem solving technology, which can be used to solve problems with a large number of objects or tasks. This algorithm has been applied to many different problems in distributed artificial intelligence (DAI). In this paper, we will discuss how to apply ant colony algorithm to Dai. The main advantage of using ant colony algorithm is that it can work very quickly and effectively with a small amount of data. It also allows us to deal with complex situations. The main idea behind this technology is to find a solution by dividing the problem into smaller parts and then assigning each part to one or more workers. These workers are responsible for finding solutions for their parts. Once all the parts are solved, they will be combined to form a solution. The technology can be used in many different fields, including computer science, biology, sociology and economics. Ant colony algorithm (ACO) is a method that uses ants as its basic unit of calculation.
Application of Artificial Intelligence in Early–Stage Diagnosis of Sepsis

Patient care is a critical task, which requires a lot of effort. Medical practitioners face many challenges, especially during diagnosing different diseases. Sepsis is one of the riskiest diseases, which proves to be lethal for Intensive Care Unit (ICU) patients. World Health Organization (WHO) has declared it a major cause of death worldwide. Early-stage diagnosis of sepsis can help in terminating it in the start. But unfortunately, medical practitioners encounter hitches in the early-stage diagnosis of sepsis. The study used SOFA (Sequential Organ Failure Assessment) for measuring the severity of sepsis in patients. The study employs artificial intelligence techniques such as Multilayer Perceptron (MLP) and Random Forest (RF) to diagnose early-stage of sepsis. The study compared the performance of MLP (connected and non-connected) and Random Forest (connected and non-connected) algorithms. The results indicate that for both of the algorithms, the connected method yielded better results than the non-connected method. Further, it was found that RF both connected and non-connected algorithms yielded better results than MLP algorithms and the Random Forest connected algorithm yielded highly accurate results for diagnosing early-stage sepsis in the 3rd hour.
The Application of Blockchain Technology in the Governance of State-owned Enterprises

The problems existing in the governance structure of state-owned enterprises include the lack of diversification of ownership structure; Owner checks and balances; Appointment of directors and senior executives; The absence of supervisors on the board of supervisors; However, the issue of the concurrent appointment of the chairman and the president was ultimately caused by disputes of interest within state-owned enterprises. Interests of disputes is essentially unit interior leadership team members, middle-level cadres, the subordinate, between departments and local understanding of differences between the specific performance, is the profit distribution is not reasonable and the imbalance of differences, and ultimately evolve into stakeholders of chasing interests balance work conflict, Based on the expression forms and motivation analysis of interest disputes, the author puts forward the mode of common governance of stakeholders -- by means of the consensus mechanism in blockchain technology; Choose the governance model as appropriate -- with the help of smart contracts in blockchain technology; Cooperation culture and information communication governance model -- with the help of the distributed ledger in blockchain technology three levels to establish its resolution mechanism.
An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study

Predicting the likelihood of success of weight loss interventions using machine learning (ML) models may enhance intervention effectiveness by enabling timely and dynamic modification of intervention components for nonresponders to treatment. However, a lack of understanding and trust in these ML models impacts adoption among weight management experts. Recent advances in the field of explainable artificial intelligence enable the interpretation of ML models, yet it is unknown whether they enhance model understanding, trust, and adoption among weight management experts.This study aimed to build and evaluate an ML model that can predict 6-month weight loss success (ie, ≥7% weight loss) from 5 engagement and diet-related features collected over the initial 2 weeks of an intervention, to assess whether providing ML-based explanations increases weight management experts' agreement with ML model predictions, and to inform factors that influence the understanding and trust of ML models to advance explainability in early prediction of weight loss among weight management experts.We trained an ML model using the random forest (RF) algorithm and data from a 6-month weight loss intervention (N=419). We leveraged findings from existing explainability metrics to develop Prime Implicant Maintenance of Outcome (PRIMO), an interactive tool to understand predictions made by the RF model. We asked 14 weight management experts to predict hypothetical participants' weight loss success before and after using PRIMO. We compared PRIMO with 2 other explainability methods, one based on feature ranking and the other based on conditional probability. We used generalized linear mixed-effects models to evaluate participants' agreement with ML predictions and conducted likelihood ratio tests to examine the relationship between explainability methods and outcomes for nested models. We conducted guided interviews and thematic analysis to study the impact of our tool on experts' understanding and trust in the model.Our RF model had 81% accuracy in the early prediction of weight loss success. Weight management experts were significantly more likely to agree with the model when using PRIMO (χ2=7.9; P=.02) compared with the other 2 methods with odds ratios of 2.52 (95% CI 0.91-7.69) and 3.95 (95% CI 1.50-11.76). From our study, we inferred that our software not only influenced experts' understanding and trust but also impacted decision-making. Several themes were identified through interviews: preference for multiple explanation types, need to visualize uncertainty in explanations provided by PRIMO, and need for model performance metrics on similar participant test instances.Our results show the potential for weight management experts to agree with the ML-based early prediction of success in weight loss treatment programs, enabling timely and dynamic modification of intervention components to enhance intervention effectiveness. Our findings provide methods for advancing the understandability and trust of ML models among weight management experts.
Lower Class of Inmates

Abstract In addition to the prison criminal leadership and general inmate population who have not violated major criminal organization set prison rules, there are lower castes, with specific rules of interaction, that are basically untouchables. It is easy for an inmate to fall from an upper caste to a lower caste, but absolutely impossible for an inmate to rise up from a lower caste. The existence of the lower castes is based on need and designed to solve the following problems: doing the dirty jobs, punishment (mechanism to scare inmates into complying with vory organization set prison rules), scurity (in the outside world, a dangerous person can be isolated in prison, but within the prison, the only recourse is a lower caste), emotional well-being (inmates are trapped in an intimate environment with people of different personalities, making it necessary to have a system where they can at least tolerate each other), and show of power.
OSS Reliability Analysis and Project Effort Estimation Based on Computational Intelligence

OSS (open-source software) systems serve as the key components of critical infrastructures in the society. As for the OSS development paradigm, the bug tracking systems are used for software quality management in many OSS projects. It is important to appropriately control the quality for the progress status of OSS project, because the software failure is caused by the poor handling of effort control. In particular, the GUI of OSS will be frequently made a dramatic difference according to the major version upgrade. The changing in GUI of OSS will depend on the development and management effort of OSS in the specified version. Considering the relationship between GUI and OSS development process, the UX/UI design of OSS will change with the procedure of OSS development. This chapter focuses on the method of effort estimation for OSS project. Then, the pixel data and OSS fault big data are analyzed by using the deep learning. Moreover, we discuss the effort assessment method in the development phase by using the effort data.
Mental Models and Algorithms of Deduction

This chapter begins with an outline of logic and of the attempts to use it as a theory of human deduction. The fatal impediments to this approach led to the model theory in which models based on the meanings of premises yield deductive conclusions. And the chapter describes in detail the implementation of this theory’s account of deductions based on sentential connectives such as “if,” and how this simulation led to the discovery of systematic but compelling fallacies. The chapter outlines how algorithms based on models simulate deductions of the spatial relations among objects. And it points out the problems that need to be solved to extend accounts of elementary inferences from quantified assertions to deal with multiply-quantified relations. One alternative to the model theory is the idea that human deduction relies on probabilities. This approach concerns only which inferences people make, not the underlying mental processes by which they are made. The model theory fills the gap, because it applies to the deductions of probabilities, both those based on frequencies or proportions, and those based on evidence pertinent to unique events. The chapter ends with an account of why theories of human deduction need to be simulated in computer programs.
Cognitive Modeling Paradigms

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Bayesian Models of Cognition

Many of the problems that human minds need to solve – including learning concepts, causal relationships, and languages – require making informed inferences from limited data. Bayesian models of cognition consider how an ideal agent should solve these problems, drawing on ideas from probability theory, statistics, machine learning, and artificial intelligence. The resulting models can then be used to understand human behavior, identifying in formal terms the knowledge that human minds draw on when solving these problems and identifying potential mechanisms by which their solutions might be implemented. This chapter provides an introduction to Bayesian models of cognition, starting with the basic principles of probability theory and then considering more advanced topics such as graphical models, causal learning, hierarchical Bayesian models, and Markov chain Monte Carlo. The chapter ends with a brief review of recent theoretical developments.
Computational Models of Skill Acquisition

Computer models of the acquisition of cognitive skills build on a long and progressive tradition of research. Since 1979, a wide range of psychologically plausible mechanisms for learning during skill practice have been implemented in computational models. This repertoire of mechanisms goes a long way towards answering the questions implied by Fitts’ (1964) division of practice into three phases: How does skill practice get started? How is a partially learned skill improved during practice? How does a skill change as practice is extended beyond mastery? Nine distinct modes of learning are identified. Each can be implemented in several different ways. The majority of models explain the speed-up of task completion that occurs during practice. There are fewer attempts to model the origin, consequences, and ultimate elimination of errors.
Model Validation, Comparison, and Selection

Progress in the computational cognitive sciences depends critically on model evaluation. This chapter provides an accessible description of key considerations and methods important in model evaluation, with special emphasis on evaluation in the forms of validation, comparison, and selection. Major sub-topics include qualitative and quantitative validation, parameter estimation, cross-validation, goodness of fit, and model mimicry. The chapter includes definitions of an assortment of key concepts, relevant equations, and descriptions of best practices and important considerations in the use of these model evaluation methods. The chapter concludes with important high-level considerations regarding emerging directions and opportunities for continuing improvement in model evaluation.
An Evaluation of Computational Modeling in Cognitive Sciences

Computer modeling of specific psychological processes began over fifty years ago. Cognitive scientists do not use computers merely as tools, but also as inspiration about the nature of mental processes. Computational cognitive science has a long way to go. There are many unanswered questions. However, cognitive scientists believe that the mind/brain is in principle intelligible in terms of whatever turns out to be the best theory of what computers can do. The overview of cognitive science given in this chapter should suffice to show that significant progress has been made.
Deep Learning

This chapter introduces deep learning (DL) in the framework of experimentalism, taking inspiration from Pierre Oleron’s explanation of human intellectual activities in terms of long (or, deep) circuits. A history of DL is presented, from its origin in the mid-twentieth century to the breakthrough of deep neural networks (DNNs) in the last decades. Architectural and representational issues are then discussed in depth. Convolutional neural networks, the most popular and successful DL algorithm to date, are reviewed in detail. Finally, adaptive activation functions in DNNs are presented in the context of homeostatic neuroplasticity, surveyed, and analyzed.
Computational Modeling of Basic Cognitive Functionalities

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System simulation of computer image recognition technology application by using improved neural network algorithm

Digital image technology is penetrating into various fields of people's life, and it has been very mature and can effectively store and transmit data. Moreover, there are still various researches on image recognition, the core of this technology. The algorithm is mainly based on computer technology to obtain the target image for different scene categories, thus completely replacing the traditional classification form. Because of the limitations of traditional identification technology, there are some problems in the actual use. It does not depend on the prior knowledge requirements and can carry out complex feature space division. In this paper, an image recognition computer system is established by introducing an improved neural network algorithm. The algorithm is designed and tested, and the results show it has lower image recognition error rate. Subsequently, this research result is applied to the actual scene for testing. The test results show that the improved neural network optimization algorithm can make the extracted features more accurately expressed in the image processing, which is more effective than the traditional algorithm.
Discrimination of geometrically uniform states and the no-signaling principle

Abstract The link between quantum state discrimination and the no-signaling principle is applied to discriminating geometrically uniform states. Specifically, the original discrimination problem is converted to constructing the complementary matrix with predetermined off-diagonal entries. Two such constructions are given explicitly, and therefore three upper bounds on the success probability are derived. Those upper bounds are then employed to estimate the robustness of coherence, where we establish a larger class of states with equal robustness of coherence and l 1 -norm of coherence, and provide the exact answer of the robustness of coherence for any states that are diagonalized by Hadamard matrices.
Framework for Data Trust using Block-Chain Technology and Adaptive Transaction Validation

Trust is the main barrier preventing widespread data sharing. The lack of transparent infrastructures for implementing data trust prevents many data owners from sharing their data and concerns data users regarding the quality of the shared data. Blockchain technology proposes a distributed and transparent administration by employing multiple parties to maintain consensus on an immutable ledger. This project presents an end-to-end framework for data trust to enhance trustworthy data sharing utilizing blockchain technology. We also suggest an adaptive solution to determine the number of transaction validators based on the computed trust value.
EDI-Graphic: A Tool To Study Parameter Discrimination and Confirm Identifiability in Black-Box Models, and to Select Data-Generating Machines

SummaryIn a Data-Generating Experiment (DGE), the data, X, is often obtained from a Black-Box and is approximated with a learning machine/sampler, f(Y,θ);θ∈Θ, Y is random, f is known. When X has unknown c.d.f., Fθ, non-identifiability of θ cannot be confirmed and may limit the predictive accuracy of the learned model, f(Y,θ̂);θ̂ estimate of θ. Using properties of the Expected P-value for the Kolmogorov-Smirnov test, the Empirical Discrimination Index (EDI) and the Proportion of P-Values Index (PPVI) are introduced: i) to confirm almost surely, discrimination of θ from θ*, i.e. Fθ≠Fθ*, ii) to confirm with EDI-graphics identifiability of θ(∈Θ) by repeating i) for θ* in a fine sieve of Θ, and iii) to compare EDI-graphics and PPVIs of DGEs and select to use the DGE with the greater parameter discrimination and the smaller number of θ* violating identifiability of θ. Among the applications, EDI and PPVI explain why the g-estimate in Tukey’s g-and-h model is better than that for the g-and-k model, unless the sample size is extremely large; h=h0=k. EDI-graphics indicate that Normal learning machines have better parameter discrimination than Sigmoid learning machines and their parameters are non-identifiable.
“Together but not together”: Evaluating Typing Indicators for Interaction-Rich Communication

Messaging is a ubiquitous digital communication medium. It is also a minimal medium of communication because of its inability to convey immediate feedback, tone, facial expressions, hesitations, and pauses, or follow the train of the other person's thoughts. This paper combines quantitative and qualitative approaches for analyzing richer forms of typing indicators in messaging interfaces, such as showing text as it is typed. By assessing users' subjective workload and interpreting these findings in the context of users' experiences, we found that more expressive typing indicators were perceived as "rich in communication", as they helped people communicate more allowing for closer connections. These indicators also increased users' perceived co-presence. In addition, our research suggests there may be benefits of designing customized typing indicators for relationship maintenance and task-based communication.