Reviewerly/exHarmony huggingface dataset:
A Novel Expert System for Diagnosis of Iron Deficiency Anemia

Diagnosis of a disease is one of the most important processes in the field of medicine. Thus, computer-aided detection systems are becoming increasingly important to assist physicians. The iron deficiency anemia (IDA) is a serious health problem that requires careful diagnosis. Diagnosis of IDA is a classification problem, and there are various studies conducted. Researchers also use feature selection approaches to detect significant variables. Studies so far investigate different classification problems such as outliers, class imbalance, presence of noise, and multicollinearity. However, datasets are usually affected by more than one of these problems. In this study, we aimed to create multiple systems that can separate diseased and healthy individuals and detect the variables that have a significant effect on these diseases considering influential classification problems. For this, we prepared different datasets based on the original dataset whose outliers were removed using different outlier detection methods. Then, a multistep classification algorithm was proposed for each dataset to see the results under irregular and regulated conditions. In each step, a different classification problem is handled. The results showed that it is important to consider each question together as it can and should change the outcome. Dataset and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>R</mi> </math> codes used in the study are available as supplementary files online.
Stochastic noise can be helpful for variational quantum algorithms

Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In notions of classical machine learning, they are avoided for example by means of stochastic gradient descent methods. In this work, we provide evidence that the saddle points problem can be naturally avoided in variational quantum algorithms by exploiting the presence of stochasticity. We prove convergence guarantees and present practical examples in numerical simulations and on quantum hardware. We argue that the natural stochasticity of variational algorithms can be beneficial for avoiding strict saddle points, i.e., those saddle points with at least one negative Hessian eigenvalue. This insight that some levels of shot noise could help is expected to add a new perspective to notions of near-term variational quantum algorithms.
Learning Rate Perturbation

Learning rate is one of the most important hyper-parameters that has significant influence for neural network training. Learning rate schedules are widely used in real practice to adjust the learning rate according to pre-defined schedules for the fast convergence and good generalization. However, existing learning rate schedules are all heuristic algorithms and lack theoretical support. Therefore, people usually choose the learning rate schedules through multiple ad-hoc trial, and the obtained learning rate schedules are sub-optimal. To boost the performance of the obtained sub-optimal learning rate schedule, we propose a generic learning rate schedule plugin, called LEArning Rate Perturbation (LEAP), which can be applied to various learning rate schedules to improve the model training by introducing a certain perturbation to the learning rate. We found that, with such simple yet effective strategy, training processing exponentially favors flat minima rather than sharp minima with guaranteed convergence, which leads to better generalization ability. In addition, we conduct extensive experiments which show that training with LEAP can improve the performance of various deep learning models on diverse datasets using various learning rate schedules (including constant learning rate).
Supervised Reinforcement Learning for ULV Path Planning in Complex Warehouse Environment

The rapid development of the logistics industry leads to an urgent need for intelligent equipment to improve warehouse transportation efficiency. Recent advances in unmanned logistics vehicles (ULVs) make them particularly important in smart warehouses. However, the complex warehouse environment poses a significant challenge to ULV transportation path planning. Multiple ULVs need to transport cargoes with good coordination ability to overcome the low efficiency of a single ULV. The ULVs also need to interact with the environment to achieve optimal path planning with obstacle avoidance. In this paper, we propose a supervised deep reinforcement learning (SDRL) approach for logistics warehouses that enables autonomous ULVs path planning for cargo transportation in a complex environment. The proposed SDRL approach is featured by (1) designing the supervision module to imitate the behaviors of experts and thus improve the coordination ability of multiple ULVs, (2) optimizing the generator of the imitation learning based on the proximal policy optimization to boost the learning performance, and (3) developing the policy module via deep reinforcement learning to avoid obstacles when navigating the ULVs in warehouse environments. The experiments over dynamic and fixed-point warehouse environments show that the proposed SDRL approach outperforms its rivals regarding average reward, training speed, task completion rate, and collision times.
Substantive Aspects of Training for the Profession Developer of Educational Trajectories in a Multidisciplinary University

The article lists the prerequisites for the emergence of new interdisciplinary professions at the intersection of pedagogy and information and communication technologies. As an example, a new promising profession of a developer of individual educational trajectories is given, described in the Atlas of new professions 3.0, developed under the auspices of the Agency for Strategic Initiatives. As conceived by the authors, this is a professional who creates a “route” for training new specialists from courses offered by educational institutions, including those available online, as well as simulators, simulators, internships, etc., and develops an educational track based on them, taking into account the psychotype, abilities and goals individual person. Since a brief description of this profession in the Atlas does not give a complete picture of its content, for the purposes of understanding the meaningful aspect of the professional training of such specialists in a multidisciplinary university, a description of the methodological basis of the designer of individual educational trajectories is given. The authors of this article describe the principle of operation of the constructor, developed at Peter the Great St. Petersburg Polytechnic University and allowing the selection of the content of professional training of the developer of individual educational trajectories in a multidisciplinary university.
Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources

Abstract Objectives This paper developed federated solutions based on two approximation algorithms to achieve federated generalized linear mixed effect models (GLMM). The paper also proposed a solution for numerical errors and singularity issues. And showed the two proposed methods can perform well in revealing the significance of parameter in distributed datasets, comparing to a centralized GLMM algorithm from R package (‘lme4’) as the baseline model. Methods The log-likelihood function of GLMM is approximated by two numerical methods (Laplace approximation and Gaussian Hermite approximation, abbreviated as LA and GH), which supports federated decomposition of GLMM to bring computation to data. To solve the numerical errors and singularity issues, the loss-less estimation of log-sum-exponential trick and the adaptive regularization strategy was used to tackle the problems caused by federated settings. Results Our proposed method can handle GLMM to accommodate hierarchical data with multiple non-independent levels of observations in a federated setting. The experiment results demonstrate comparable (LA) and superior (GH) performances with simulated and real-world data. Conclusion We modified and compared federated GLMMs with different approximations, which can support researchers in analyzing versatile biomedical data to accommodate mixed effects and address non-independence due to hierarchical structures (i.e., institutes, region, country, etc.).
Improved Biogeography-Based Optimization Algorithm Based on Hybrid Migration and Dual-Mode Mutation Strategy

To obtain high-quality Pareto optimal solutions and to enhance the searchability of the biogeography-based optimization (BBO) algorithm, we present an improved BBO algorithm based on hybrid migration and a dual-mode mutation strategy (HDBBO). We first adopted a more scientific nonlinear hyperbolic tangent mobility model instead of the conventional linear migration model which can obtain a solution closer to the global minimum of the function. We developed an improved hybrid migration operation containing a micro disturbance factor, which has the benefit of strengthening the global search ability of the algorithm. Then, we used the piecewise application of Gaussian mutation and BBO mutation to ensure that the solution set after mutation was also maintained at a high level, which helps strengthen the algorithm’s search accuracy. Finally, we performed a convergence analysis on the improved BBO algorithm and experimental research based on 11 benchmark functions. The simulation results showed that the improved BBO algorithm had superior advantages in terms of optimization accuracy and convergence speed, which showed the feasibility of the improved strategy.
Innovation and Key Benefits of Business Models in Blockchain Companies

Existing business models are changed and disrupted by blockchain technology, which warrants more research. This study tries to describe the phenomena of blockchain technology in relation to business model innovation in the enterprise ecosystem on the basis of that. Blockchain technology has been shown to increase organizational performance empirically. The value system concept is used in this study to explain this phenomena. This study employs a multiple-case study to respond to research questions using abductive reasoning. Overall, the outcomes According to this study, blockchain technology benefits an organization in four different ways: I value capture through higher profitability; (ii) value creation through private partnerships; (iii) value delivery through smart contracts; and (iv) value proposition that drives value by raising the organization's business value through operational improvements. This report also suggests a blockchain ecosystem with various private and consortium organizations. Private ecosystem focused on internal organizational performance enhancement within one corporate enterprise. The consortium ecosystem, on the other hand, is focused on fostering corporate cooperation.
Yahooism or Internet Fraud in the Nigerian Higher Education System

This study interrogates narrow-mindedness and laziness leading many of the Nigerian undergraduates to be tempted to cheat and fraud on Internet instead of working hard for their studies. The author proposes a contextual survey around a tendency also called “yahooism”, “yahoo-yahooism”, as most of the first attempted cybercrimes were realized by sending yahoo emails. This harmful tendency is contrasted with Prof Obiora Ike’s teaching on the value of hard work as the road to wealth. The study used the quantitative survey method of 372 undergraduates from two purposively selected higher in-stitutions in Nigeria, namely: Edo State University Uzairue and Federal Polytechnic Auchi, both in Etsako West Local Government Area of Edo State, Nigeria. A questionnaire with a set of nine semi-structured questions is used to obtain data from the students between 15th July and 15th August 2022.
Deep Hierarchical Interval Type 2 Self-Organizing Fuzzy System for Data-Driven Robot Control

To solve the dimensional explosion problem, this paper proposes a new architecture for the fuzzy system, the deep hierarchical self-organizing interval type-2 fuzzy system (DHSOIT2FS). Each sub-fuzzy system is a self-organizing interval type-2 fuzzy system, constructed online, with rules constructed by a rule online update algorithm, consequent parameters updated by iterative least squares, and antecedent parameters are updated using a gradient descent algorithm. DHSOIT2FS uses a classic serial-layered structure to build the overall framework. The first layer uses the first two dimensions of data as input. Each subsequent layer uses the output of the previous layer with the next dimensional data as input until it is built. During the training process, each data point is trained with DHSOIT2FS before passing in the next data point to achieve online construction. The effectiveness of the approach in this paper is illustrated using two numerical simulation examples. The proposed method is also applied to a data-driven control example of a single-link robot and achieves good tracking results.
A Multimodal Differential Evolution Algorithm in Initial Orbit Determination for a Space-Based Too Short Arc

Under the too short arc scenario, the evolutionary-based algorithm has more potential than traditional methods in initial orbit determination. However, the underlying multimodal phenomenon in initial orbit determination is ignored by current works. In this paper, we propose a new enhanced differential evolution (DE) algorithm with multimodal property to study the angle-only IOD problem. Specifically, a coarse-to-fine convergence detector is implemented, based on the Boltzmann Entropy, to determine the evolutionary phase of the population, which lays the basis of the balance between the exploration and exploitation ability. A two-layer niching technique clusters the individuals to form promising niches after each convergence detected. The candidate optima from resulting niches are saved as supporting individuals into an external archive for diversifying the population, and a local search within the archive is performed to refine the solutions. In terms of performance validation, the proposed multimodal differential evolution algorithm is evaluated on the CEC2013 multimodal benchmark problems, and it achieved competitive results compared to 11 state-of-the-art algorithms, which present its capability of multimodal optimization. Moreover, several IOD experiments and analyses are carried out on three simulated scenarios of space-based observation. The findings show that, compared to traditional IOD approaches and EA-based IOD algorithms, the proposed algorithm is more successful at finding plausible solutions while improving IOD accuracy.
Similarity-based Multi-source Transfer Learning Approach for Time Series Classification

This study aims to develop an effective method of classification concerning time series signals for machine state prediction to advance predictive maintenance (PdM). Conventional machine learning (ML) algorithms are widely adopted in PdM, however, most existing methods assume that the training (source) and testing (target) data follow the same distribution, and that labeled data are available in both source and target domains. For real-world PdM applications, the heterogeneity in machine original equipment manufacturers (OEMs), operating conditions, facility environment, and maintenance records collectively lead to heterogeneous distribution for data collected from different machines. This will significantly limit the performance of conventional ML algorithms in PdM. Moreover, labeling data is generally costly and time-consuming. Finally, industrial processes incorporate complex conditions, and unpredictable breakdown modes lead to extreme complexities for PdM. In this study, similarity-based multi-source transfer learning (SiMuS-TL) approach is proposed for real-time classification of time series signals. A new domain, called "mixed domain," is established to model the hidden similarities among the multiple sources and the target. The proposed SiMuS-TL model mainly includes three key steps: 1) learning group-based feature patterns, 2) developing group-based pre-trained models, and 3) weight transferring. The proposed SiMuS-TL model is validated by observing the state of the rotating machinery using a dataset collected on the Skill boss manufacturing system, publicly available standard bearing datasets, Case Western Reserve University (CWRU), and Paderborn University (PU) bearing datasets. The results of the performance comparison demonstrate that the proposed SiMuS-TL method outperformed conventional Support Vector Machine (SVM), Artificial Neural Network (ANN), and Transfer learning with neural networks (TLNN) without similarity-based transfer learning methods.
PDFEstimator: An R Package for Density Estimation and Analysis

This article presents PDFEstimator, an R package for nonparametric probability density estimation and analysis, as both a practical enhancement and alternative to kernel-based estimators. PDFEstimator creates fast, highly accurate, data-driven probability density estimates for continuous random data through an intuitive interface. Excellent results are obtained for a diverse set of data distributions ranging from 10 to $10^6$ samples when invoked with default parameter definitions in the absence of user directives. Additionally, the package contains methods for assessing the quality of any estimate, including robust plotting functions for detailed visualization and trouble-shooting. Usage of PDFEstimator is illustrated through a variety of examples, including comparisons to several kernel density methods.
Creative Wand: A System to Study Effects of Communications in Co-creative Settings

Recent neural generation systems have demonstrated the potential for procedurally generating game content, images, stories, and more. However, most neural generation algorithms are “uncontrolled” in the sense that the user has little say in creative decisions beyond the initial prompt specification. Co-creative, mixed-initiative systems require user-centric means of influencing the algorithm, especially when users are unlikely to have machine learning expertise. The key to co-creative systems is the ability to communicate ideas and intent from the user to the agent, as well as from the agent to the user. Key questions in co-creative AI include: How can users express their creative intentions? How can creative AI systems communicate their beliefs, explain their moves, or instruct users to act on their behalf? When should creative AI systems take initiative? The answer to such questions and more will enable us to develop better co-creative systems that make humans more capable of expressing their creative intents. We introduce CREATIVE-WAND, a customizable framework for investigating co-creative mixed-initiative generation. CREATIVE-WAND enables plug-and-play injection of generative models and human-agent communication channels into a chat-based interface. It provides a number of dimensions along which an AI generator and humans can communicate during the co-creative process. We illustrate the CREATIVE-WAND framework by using it to study one dimension of co-creative communication—global versus local creative intent specification by the user—in the context of storytelling.
“If it didn’t happen, why would I change my decision?”: How Judges Respond to Counterfactual Explanations for the Public Safety Assessment

Many researchers and policymakers have expressed excitement about algorithmic explanations enabling more fair and responsible decision-making. However, recent experimental studies have found that explanations do not always improve human use of algorithmic advice. In this study, we shed light on how people interpret and respond to counterfactual explanations (CFEs)---explanations that show how a model's output would change with marginal changes to its input(s)---in the context of pretrial risk assessment instruments (PRAIs). We ran think-aloud trials with eight sitting U.S. state court judges, providing them with recommendations from a PRAI that includes CFEs. We found that the CFEs did not alter the judges' decisions. At first, judges misinterpreted the counterfactuals as real---rather than hypothetical---changes to defendants. Once judges understood what the counterfactuals meant, they ignored them, stating their role is only to make decisions regarding the actual defendant in question. The judges also expressed a mix of reasons for ignoring or following the advice of the PRAI without CFEs. These results add to the literature detailing the unexpected ways in which people respond to algorithms and explanations. They also highlight new challenges associated with improving human-algorithm collaborations through explanations.
It Is like Finding a Polar Bear in the Savannah! Concept-Level AI Explanations with Analogical Inference from Commonsense Knowledge

With recent advances in explainable artificial intelligence (XAI), researchers have started to pay attention to concept-level explanations, which explain model predictions with a high level of abstraction. However, such explanations may be difficult to digest for laypeople due to the potential knowledge gap and the concomitant cognitive load. Inspired by recent work, we argue that analogy-based explanations composed of commonsense knowledge may be a potential solution to tackle this issue. In this paper, we propose analogical inference as a bridge to help end-users leverage their commonsense knowledge to better understand the concept-level explanations. Specifically, we design an effective analogy-based explanation generation method and collect 600 analogy-based explanations from 100 crowd workers. Furthermore, we propose a set of structured dimensions for the qualitative assessment of analogy-based explanations and conduct an empirical evaluation of the generated analogies with experts. Our findings reveal significant positive correlations between the qualitative dimensions of analogies and the perceived helpfulness of analogy-based explanations. These insights can inform the design of future methods for the generation of effective analogy-based explanations. We also find that the understanding of commonsense explanations varies with the experience of the recipient user, which points out the need for further work on personalization when leveraging commonsense explanations.
Study on Teaching Strategies of Scratch Programming in Elementary Schools based on Deep Learning Theory

Scratch programming software uses block building instead of code input, which effectively reduces the difficulty of programming. This paper discusses how to use deep learning theory to improve the teaching effect of Scratch programming, and proposes some effective Scratch programming teaching strategies based on deep learning, which can provide reference for front-line Scratch programming teachers.
Multi-Input Attention Network for Dehazing of Remote Sensing Images

The non-uniform haze distribution in remote sensing images, together with the complexity of the ground information, brings many difficulties to the dehazing of remote sensing images. In this paper, we propose a multi-input convolutional neural network based on an encoder–decoder structure to effectively restore remote sensing hazy images. The proposed network can directly learn the mapping between hazy images and the corresponding haze-free images. It also effectively utilizes the strong haze penetration characteristic of the Infrared band. Our proposed network also includes the attention module and the global skip connection structure, which enables the network to effectively learn the haze-relevant features and better preserve the ground information. We build a dataset for training and testing our proposed method. The dataset consists of remote sensing images with two different resolutions and nine bands, which are captured by Sentinel-2. The experimental results demonstrate that our method outperforms traditional dehazing methods and other deep learning methods in terms of the final dehazing effect, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM).