Reviewerly/exHarmony huggingface dataset:
A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules

In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness.
Finding Hierarchical Structure in Binary Sequences: Evidence from Lindenmayer Grammar Learning

In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether humans learn the higher-order regularities of a highly simplified input where only sequential-order information marks the hierarchical structure. To this end, we implemented a sequence generated by the Fibonacci grammar in a serial reaction time task. This deterministic grammar generates aperiodic but self-similar sequences. The combination of these two properties allowed us to evaluate hierarchical learning while controlling for the use of low-level strategies like detecting recurring patterns. The deterministic aspect of the grammar allowed us to predict precisely which points in the sequence should be subject to anticipation. Results showed that participants' pattern of anticipation could not be accounted for by "flat" statistical learning processes and was consistent with them anticipating upcoming points based on hierarchical assumptions. We also found that participants were sensitive to the structure constituency, suggesting that they organized the signal into embedded constituents. We hypothesized that the participants built this structure by merging recursively deterministic transitions.
Design of Expert System Diagnosis of Catfish Disease with Forward Chaining Method

The expert system can be used as a means for consulting and assisting experts and catfish breeders who areexperiencing problems in identifying catfish diseases and their solutions. So that this expert system can be accessedeasily by anyone and anywhere connected to the internet network, this expert system is made web-based. The MySQLdatabase used in this system will store facts that were built using the PHP programming language. Likewise, systemdevelopment is only limited to diagnosing catfish diseases. The output of this system is in the form of diseaseinformation in catfish and how to handle it. The form of research used by the author is a literature study and is appliedto experimental research. The software development method used by the author is to use the Forward Chaining methodwhich consists of rules. The results of the research that have been made, it is found that this website and expert systemmake it easy for ordinary people or beginners to cultivate catfish in order to produce healthy and superior catfish.
Deep radio signal clustering with interpretability analysis based on saliency map

With the development of information technology, radio communication technology has made rapid progress. Many radio signals that have appeared in space are difficult to classify without manually labeling. Unsupervised radio signal clustering methods have recently become an urgent need for this situation. Meanwhile, the high complexity of deep learning makes it difficult to understand the decision results of the clustering models, making it essential to conduct interpretable analysis. This paper proposed a combined loss function for unsupervised clustering based on autoencoder. The combined loss function includes reconstruction loss and deep clustering loss. Deep clustering loss is added based on reconstruction loss, which makes similar deep features converge more in feature space. In addition, a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency Map. Extensive experiments have been conducted on a modulated signal dataset, and the results indicate the superior performance of our proposed method over other clustering algorithms. In particular, for the simulated dataset containing six modulation modes, when the SNR is 20 dB, the clustering accuracy of the proposed method is greater than 78%. The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model.
Complex Emotion Recognition via Facial Expressions with Label Noises Self-Cure Relation Networks

Current deep learning-based facial expression recognition mainly focused on the six basic human emotions and relied on large-scale and well-annotated data. For complex emotion recognition, such a large amount of data are not easy to obtain, and a high-quality annotation is even more difficult. Therefore, in this paper, we regard complex emotion recognition via facial expressions as a few-shot learning problem and introduce a metric-based few-shot model named self-cure relation networks (SCRNet), which is robust to label noises and is able to classify facial images of new classes of emotions by only few examples from each. Specifically, SCRNet learns a distance metric based on deep features abstracted by convolutional neural networks and predicts a query image's emotion category by computing relation scores between the query image and the few examples of each new class. To tackle the label noise problem, SCRNet gives corrected labels to noise data via class prototype stored in external memory during the meta-training phase. Experimenting on public datasets as well as on synthetic noise datasets demonstrates the effectiveness of our method.
Fast verification and public key storage optimization for unstructured lattice-based signatures

A recent work of Sipasseuth, Plantard and Susilo proposed to accelerate lattice-based signature verifications and compress public key storage at the cost of a precomputation on a public key. This first approach, which focused on a restricted type of key, did not include most NIST candidates or most lattice representations in general. In this work, we first present a way to improve even further both their verification speed and their public key compression capability by using a generator of numbers that better suit the method needs. We then also generalize their framework to apply to q-ary lattice schemes as well as classical lattices using Hermite Normal Form, improving their security and applicable scope, thus exhibiting potential trade-offs to accelerate lattice-based signature verification in general and compression of the public key on the verifier side for unstructured lattices.
The Bias–Variance Tradeoff in Cognitive Science

Abstract The bias–variance tradeoff is a theoretical concept that suggests machine learning algorithms are susceptible to two kinds of error, with some algorithms tending to suffer from one more than the other. In this letter, we claim that the bias–variance tradeoff is a general concept that can be applied to human cognition as well, and we discuss implications for research in cognitive science. In particular, we show how various strands of research in cognitive science can be interpreted in light of the bias–variance tradeoff, giving insight into individual differences in learning, the nature of cognitive processes, and debates in cognitive science research.
A case-based reasoning tool to recommend drinking water source protection actions

In response to a need expressed by water actors to support them in identifying appropriate actions for protecting drinking water sources, we modelled, designed, implemented, tested, and validated a prototype case-based reasoning (CBR) system. This required an intensive knowledge acquisition and structuring process which we conducted in collaboration with 102 water management and governance actors in Quebec, Canada. Knowledge was organized in a case base containing nearly 200 past experiences implementing water protection actions at different decision-making scales (local, regional, provincial), by various organizations (government, municipalities, water-related associations), over the last two decades. This paper describes our case engineering process to design case attributes based on content analysis. It presents the case edition, the case retrieval process, and the prototype's implementation and validation, conducted through a rigorous and transparent participatory procedure. This is, to our knowledge, the first successful attempt to apply a CBR approach to support water actors in protecting drinking water sources. It provides empirical evidence of the positive potential of such an approach for knowledge sharing and transfer and can be generalized to other similar contexts.
Feature construction using explanations of individual predictions

Feature construction can contribute to comprehensibility and performance of machine learning models. Unfortunately, it usually requires exhaustive search in the attribute space or time-consuming human involvement to generate meaningful features. We propose a novel heuristic approach for reducing the search space based on aggregation of instance-based explanations of predictive models. The proposed Explainable Feature Construction (EFC) methodology identifies groups of co-occurring attributes exposed by popular explanation methods, such as IME and SHAP. We empirically show that reducing the search to these groups significantly reduces the time of feature construction using logical, relational, Cartesian, numerical, and threshold num-of-N and X-of-N constructive operators. An analysis on 10 transparent synthetic datasets shows that EFC effectively identifies informative groups of attributes and constructs relevant features. Using 30 real-world classification datasets, we show significant improvements in classification accuracy for several classifiers and demonstrate the feasibility of the proposed feature construction even for large datasets. Finally, EFC generated interpretable features on a real-world problem from the financial industry, which were confirmed by a domain expert.
Ensemble framework for causality learning with heterogeneous Directed Acyclic Graphs through the lens of optimization

Directed Acyclic Graphs (DAGs) are informative graphical outputs of causal learning algorithms to visualize the causal structure among variables. In practice, different causal learning algorithms are often used to establish a comprehensive analysis pool, which leads to the challenging problem of ensembling the heterogeneous DAGs with diverse and conflicting embedded information. While naive approaches such as MEDIAN and greedy heuristics are presented in the literature, a mathematical framework to build the ensemble DAG is not investigated. Therefore, we propose a two-stage ensemble framework for causality learning with heterogeneous DAGs. In the first stage, we implement a data partitioning procedure to categorize the input data. Then, we apply multiple causal learning algorithms to each class and ensemble the results across the partitions for each method. The results across different approaches in the second stage are then ensembled to produce the final DAG. We define a novel mathematical model based on the marginal contribution concept, reflecting the collective information for each edge from input graphs. We investigate the relationship between the proposed modeling approach and well-known problems such as rank aggregation and the Traveling Salesman Problem. We also present a parametric analysis for the distance function, enriched by insights from their asymptotic behavior. We design a simulation experiment to show the computational advantage of adopting a lazy constraints solution approach. In addition to the T-cell synthetic dataset, we use the dataset of water main breaks from 2017 to 2021 in the City of Tampa, Florida, to demonstrate the framework’s applicability. The results show that the optimal solution of the proposed framework outperforms current approaches in the literature based on causality-based Structural Intervention Distance performance measures.
Dynamic pressure surface deformation measurement based on a chromatic confocal sensor

To enable the real-time measurement of pressure deformations in sealed cavities, a high-precision method of detecting the deformation of a material surface is proposed. By combining a chromatic confocal displacement sensor with a pressure sensor, we can acquire dynamic online strain measurements that consider the effects of the deformed material and internal environmental conditions. A 90mm×90mm static mechanical cylindrical cavity is simulated using finite element software. The interior of the cylindrical cavity is continuously pressurized at up to 400 kPa with a material deformation of 300 µm. We experimentally obtain the spectral peak wavelengths corresponding to the surface deformation experiment, record the spectral data at 20 kPa intervals, and use the Voigt fitting algorithm to reduce sensor errors. The results show that the experimental results differ from the simulated results by 1.43 µm, with a relative error of 0.083% after sequential pressurization and depressurization using a pressure calibrator, and the uncertainty error of pressure deformation measurement is 1.495 µm. Thus, the proposed method is robust against external disturbances and is suitable for micrometer-level surface deformation monitoring, which has numerous applications in the high-precision inspection industry.
Federated Approach for Lung and Colon Cancer Classification

Deep learning is fueled by massive data. However, medical data availability is a challenge affecting the robustness of models for Computer-Aided Diagnostics. Several factors contribute to the limited amount of labeled data. One is the expertise involved in annotating biopsies and scans collected from laboratories. Another is the sensitive nature of medical information. This research, therefore, focuses on using data obtained on different diseases using the same technique to increase the number of data available to boost the automatic feature engineering capability of deep learning. Hence, the paper studies a multi-center-based training of a model capable of classifying two different diseases into their sub-classes. Data for each disease is hosted on separate devices, keeping the original data private to that device. VGG 16 is trained locally by each center, and the parameters are shared and aggregated for the global model. We utilized the LC25000 dataset of Lung and Cancer biopsy images for our experiment. The global model was then tested separately with client 1 (lung) and client 2 (colon) test sets. We also performed centralized learning (CL) by combining the four classes used in the decentralized experiment. Very high results were obtained by our approach, outperforming the states-of-the arts while preserving data privacy.
Deep Learning Based Sensitive Data Detection

The growing popularity of edge techniques, such as IoT, 5G, blockchain, make it increasingly challenging to protect sensitive data due to the amount of data increases and the growing volume of regulatory policies. To properly protect sensitive data, it is very important to identify sensitive data and implement data anonymization to ensure the quality and proper use of data anonymization techniques. This work focuses on proactively sensitive data identification, classification and anonymization using machine learning techniques. We first investigated the sensitive data extraction from both structured data and unstructured data, in which Bert models and Regular expressions were used to achieve the identification of sensitive data in real-time. Meanwhile, we propose a comprehensive sensitive detection framework combining the Bert model with regular expressions that can achieve high precision and good generalization capability with not so large corpus. The experimental results demonstrate the effectiveness of proposed solution.
Accuracy Degrading: Toward Participation-Fair Federated Learning

Centralized learning now faces data mapping and security constraints that make it difficult to carry out. Federated learning with a distributed learning architecture has changed this situation. By restricting the training process to participants’ local, federated learning addresses the model training needs of multiple data sources while better protecting data privacy. However, in real-world application scenarios, federated learning faces the need to achieve fairness in addition to privacy protection. In practice, it could happen that some federated learning participants with specific motives may short join the training process to obtain the current global model with only limited contribution to the federated learning whole, resulting in unfairness to the participants who previously participated in federated learning. We propose the FedACC framework with a server-initiated global model accuracy control method to address this issue. Besides measuring the accumulative contributions of newly joined participants and providing participants with a model with an accuracy that matches their contributions, the FedACC still guarantees the validity of participant gradients based on the accuracy-decayed model. Under the FedACC framework, users do not have access to the full version of the current global model early in their training participation. However, they must produce a certain amount of contributions before seeing the full-accuracy model. We introduce an additional differential privacy mechanism to protect clients’ privacy further. Experiments demonstrate that the FedACC could obtain about 10%–20% accuracy gain compared to the state-of-the-art methods while balancing the fairness, performance, and security of federated learning.
Tracing and classifying German intensifiers via information theory

We apply information theoretic notions to model intensifiers in German. We show that information theory can explain that despite their common referential meaning, some intensifiers are extremely frequent (so), while others are uncommon (arsch ‘butt’), and seem to induce a stronger intensifying effect. We introduce two notions to model the expressivity of an intensifier in a given message: the local (paradigmatic) information content of an intensifier (surprisal), and the transitional information, based on Markov transition probabilities. Based on a large corpus of intensified predicative adjective phrases from German Twitter data, we confirm that (1) local information and transitional information are strongly anti-correlated: The more common an intensifier is, the lower its expressive value in communication and the less it constrains following adjectival heads; and (2) “stackings” of multiple intensifiers are frequent and are used to frame and strengthen the expressiveness of the intensification. We further confirm that stackings tend to occur in incremental surprisal rank order, following the Uniform Information Density hypothesis. Our analyses show that information theory is not limited to the domain of referential semantics and morphosyntactic patterns of reduction, but offers a framework to capture the expressive function of German intensifiers.
Edge Data Center Organization and Optimization by Using Cage Graphs

Data center organization and optimization are increasingly receiving attention due to the ever-growing deployments of edge and fog computing facilities. The main aim is to achieve a topology that processes the traffic flows as fast as possible and that does not only depend on AI-based computing resources, but also on the network interconnection among physical hosts. In this paper, graph theory is introduced, due to its features related to network connectivity and stability, which leads to more resilient and sustainable deployments, where cage graphs may have an advantage over the rest. In this context, the Petersen graph cage is studied as a convenient candidate for small data centers due to its small number of nodes and small network diameter, thus providing an interesting solution for edge and fog data centers.
An Intelligent Approach Based on Cleaning up of Inutile Contents for Extremism Detection and Classification in Social Networks

Extremism is a growing threat worldwide that presents a significant danger to public safety and national security. Social networks provide extremists with spaces to spread their ideas through commentaries or tweets, often in Asian English. In this paper, we propose an intelligent approach that cleans the text’s content, analyzes its sentiment, and extracts its features after converting it to digital data for machine learning treatments. We apply 16 intelligent machine learning classifiers for extremism detection and classification. The proposed artificial intelligence methods for Asian English language data are used to extract the essential features from the text. Our evaluation of the proposed model with an extremism dataset proves its effectiveness compared to the standard classification models based on various performance metrics. The proposed model achieves 93,6% accuracy for extremism detection and 97,0% for extremism classification.
An AI-Based System Offering Automatic DR-Enhanced AR for Indoor Scenes

In this work, we present an AI-based Augmented Reality (AR) system for indoor planning and refurbishing applications. AR can be an important medium for such applications, as it facilitates more effective concept conveyance and additionally acts as an efficient and immediate designer-to-client communication channel. However, since AR only overlays, and cannot replace or remove, our system relies on Diminished Reality (DR) to support deployment to real-world already furnished indoor scenes. Further, and contrary to the traditional mobile AR application approach, our system offers on-demand Virtual Reality (VR) viewing, relying on spherical (360°) panoramas, capitalizing on their user-friendliness for indoor scene capturing. Given that our system is an integration of different AI services, we analyze its performance differentials concerning the components comprising it. This analysis is both quantitative and qualitative, with the latter realized through user surveys, and provides a complete systemic assessment of an attempt for a user-facing, automatic AR/DR system.
360-Degree Virtual Reality Videos in EFL Teaching: Student Experiences

In technology-enhanced language learning environments, virtual reality (VR) has become an effective tool to support contextualized learning and promote immersive experiences with learning materials. This case study, using the qualitative research method, explored students' attitudes and perceptions of engaging in VR language learning. Twenty-eight Chinese sophomores attended this VR project in a travel English unit. They wore VR headsets to watch 360 VR videos containing famous tourist attractions from four countries during a 6-week project. Data were collected from final reflections and interviews. Results of final reflections indicated that students showed positive feedback on this new teaching method in English learning. In addition, interview data present the advantages and disadvantages of implementing immersive VR learning in EFL classrooms.