Reviewerly/exHarmony huggingface dataset:
Gated Linear Networks

This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs). What distinguishes GLNs from contemporary neural networks is the distributed and local nature of their credit assignment mechanism; each neuron directly predicts the target, forgoing the ability to learn feature representations in favor of rapid online learning. Individual neurons are able to model nonlinear functions via the use of data-dependent gating in conjunction with online convex optimization. We show that this architecture gives rise to universal learning capabilities in the limit, with effective model capacity increasing as a function of network size in a manner comparable with deep ReLU networks. Furthermore, we demonstrate that the GLN learning mechanism possesses extraordinary resilience to catastrophic forgetting, performing almost on par to an MLP with dropout and Elastic Weight Consolidation on standard benchmarks.
Algebraic Attacks against Some Arithmetization-Oriented Primitives

Recent advanced Zero-Knowledge protocols, along with other high-level constructions such as Multi-Party Computations (MPC), have highlighted the need for a new type of symmetric primitives that are not optimized for speed on the usual platforms (desktop computers, servers, microcontrollers, RFID tags...), but for their ability to be implemented using arithmetic circuits.Several primitives have already been proposed to satisfy this need. In order to enable an efficient arithmetization, they operate over large finite fields, and use round functions that can be modelled using low degree equations. The impact of these properties on their security remains to be completely assessed. In particular, algebraic attacks relying on polynomial root-finding become extremely relevant. Such attacks work by writing the cryptanalysis as systems of polynomial equations over the large field, and solving them with off-the-shelf tools (SageMath, NTL, Magma, . . . ).The need for further analysis of these new designs has been recently highlighted by the Ethereum Foundation, as it issued bounties for successful attacks against round-reduced versions of several of them.In this paper, we show that the security analysis performed by the designers (or challenge authors) of four such primitives is too optimistic, and that it is possible to improve algebraic attacks using insights gathered from a careful study of the round function.First, we show that univariate polynomial root-finding can be of great relevance n practice, as it allows us to solve many of the Ethereum Foundation’s challenges on Feistel–MiMC. Second, we introduce a trick to essentially shave off two full rounds at little to no cost for Substitution-Permutation Networks (SPN). This can be combined with univariate (resp. multivariate) root-finding, which allowed to solve some challenges for Poseidon (resp. Rescue–Prime). Finally, we also find an alternative way to set up a system of equations to attack Ciminion, leading to much faster attacks than expected by the designers.
A Multi-Mechanism Seagull Optimization Algorithm Incorporating Generalized Opposition-Based Nonlinear Boundary Processing

The seagull optimization algorithm (SOA), a well-known illustration of intelligent algorithms, has recently drawn a lot of academic interest. However, it has a variety of issues including slower convergence, poorer search accuracy, the single path for pursuing optimization, and the simple propensity to slip into local optimality. This paper suggests a multi-mechanism seagull optimization algorithm (GEN−SOA) that incorporates the generalized opposition-based, adaptive nonlinear weights, and evolutionary boundary constraints to address these demerits further. These methods are balanced and promoted the population variety and the capability to conduct global and local search. Compared with SOA, PSO, SCA, SSA, and BOA on 12 well-known test functions, the experimental results demonstrate that GEN-SOA has a higher accuracy and faster convergence than the other five algorithms, and it can find the global optimal solution beyond the local optimum. Furthermore, to verify the capability of GEN−SOA to solve practical problems, this paper applied GEN−SOA to solve two standard engineering optimization design problems including a welding optimization and a pressure vessel optimization, and the experimental results showed that it has significant advantages over SOA.
Chinese Character Components Segmentation Method Based on Faster RCNN

To solve the component segmentation problem caused by the sticking and overlapping of parts in incoherent handwritten calligraphy characters, we propose a Chinese character part segmentation method based on Faster RCNN.The method utilizes the advantages of Faster RCNN on multi-scale and small targets to solve difficult problems in component segmentation.The hierarchical features of the components were used in our proposed method to identify each layer of the Chinese character structure to obtain the components.Qualitative and quantitative calculations were used to test the segmentation effect of the proposed method.The experimental results demonstrate the accurate segmentation effectiveness of our method for adhering and overlapping components.In addition, these components could be retrieved accurately in the retrieval system, and the mean Average Precision of the top 30 retrieval results reached 95.7%.A better retrieval accuracy reflects a better segmentation effect from the side, which proves the effectiveness of the proposed method.
Military Vehicle Object Detection Based on Hierarchical Feature Representation and Refined Localization

Military vehicle object detection technology in complex environments is the basis for the implementation of reconnaissance and tracking tasks for weapons and equipment, and is of great significance for information and intelligent combat. In response to the poor performance of traditional detection algorithms in military vehicle detection, we propose a military vehicle detection method based on hierarchical feature representation and reinforcement learning refinement localization, referred to as MVODM. First, for the military vehicle detection task, we construct a reliable dataset MVD. Second, we design two strategies, hierarchical feature representation and reinforcement learning-based refinement localization, to improve the detector. The hierarchical feature representation strategy can help the detector select the feature representation layer suitable for the object scale, and the reinforcement learning-based refinement localization strategy can improve the accuracy of the object localization boxes. The combination of these two strategies can effectively improve the performance of the detector. Finally, the experimental results on the homemade dataset show that our proposed MVODM has excellent detection performance and can better accomplish the detection task of military vehicles.
Kajian Model Pembelajaran Problem Based Learning (PBL) dalam Pembelajaran di Sekolah

This study aims to analyze the application of Problem Based Learning (PBL) models in learning in schools. This form of research is literature studies. The method used is descriptive qualitative to analyze the application of Problem Based Learning (PBL) model. Data collection techniques use documentation, by searching journals relevant to Problem Based Learning (PBL) through Sinta and Google Scholar (Google Scholar) with the research journal period of 2015-2020. The keyword used for journal search is the Problem Based Learning (PBL) model. The journals obtained as many as 231 journals were then selected 41 journals that met the criteria for further analysis. Based on the results of analysis from 41 journals that obtained information; 1) Problem Based Learning (PBL) model found in the field of science that is often applied in schools with a percentage of 77% and in the field of mathematics studies with a percentage of 31%; 2) the application of pbl model has a positive effect on learners, (3) the impact of Problem Based Learning model in learning can increase interest in learning, problem solving ability, learning motivation, critical thinking, and learner learning outcomes. In conclusion, problem based learning models based on literature review results have a positive impact in improving students' learning abilities and interests.&#x0D; Keywords: Problem Based Learning (PBL), Learning Models, Literature Studies
An Explainable Artificial Intelligence Approach for Detecting Empathy in Textual Communication

Empathy is a necessary component of human communication. However, it has been largely ignored in favor of other concepts such as emotion and feeling in Affective computing. Research that has been carried out regarding empathy in computer science lacks a method of measuring empathy based on psychological research. Likewise, it does not present an avenue for expanding knowledge regarding this concept. We provide a comprehensive study on the nature of empathy and a method for detecting it in textual communication. We measured empathy present in conversations from a database through volunteers and psychological research. Subsequently, we made use of a pattern-based classification algorithm to predict the Empathy levels in each conversation. Our research contributions are: the Empathy score, a metric for measuring empathy in texts; Empathetic Conversations, a database containing conversations with their respective Empathy score; and our results. We show that an explicative pattern-based approach (PBC4cip) is, to date, the best approach for detecting empathy in texts. This is by measuring performance in both nominal and ordinal metrics. We found a statistically significant difference in performance for our approach and other algorithms with lower performance. In addition, we show the advantages of interpretability by our model in contrast to other approaches. This is one of the first approaches to measuring empathy in texts, and we expect it to be useful for future research.
Skeleton Action Recognition Based on Temporal Gated Unit and Adaptive Graph Convolution

In recent years, great progress has been made in the recognition of skeletal behaviors based on graph convolutional networks (GCNs). In most existing methods, however, the fixed adjacency matrix and fixed graph structure are used for skeleton data feature extraction in the spatial dimension, which usually leads to weak spatial modeling ability, unsatisfactory generalization performance, and an excessive number of model parameters. Most of these methods follow the ST-GCN approach in the temporal dimension, which inevitably leads to a number of non-key frames, increasing the cost of feature extraction and causing the model to be slower in terms of feature extraction and the required computational burden. In this paper, a gated temporally and spatially adaptive graph convolutional network is proposed. On the one hand, a learnable parameter matrix which can adaptively learn the key information of the skeleton data in spatial dimension is added to the graph convolution layer, improving the feature extraction and generalizability of the model and reducing the number of parameters. On the other hand, a gated unit is added to the temporal feature extraction module to alleviate interference from non-critical frames and reduce computational complexity. A channel attention mechanism based on an SE module and a frame attention mechanism are used to enhance the model’s feature extraction ability. To prevent model degradation and ensure more stable training, residual links are added to each feature extraction module. The proposed approach was ultimately able to achieve 0.63% higher accuracy on the X-Sub benchmark with 4.46 M fewer parameters than GAT, one of the best SOTA methods. Inference speed of our model reaches as fast as 86.23 sequences/(second × GPU). Extensive experimental results further validate the effectiveness of our proposed approach on three large-scale datasets, namely, NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton.
An Overview of Self-Adaptive Differential Evolution Algorithms with Mutation Strategy

Differential Evolution (DE) is a widely used global searching algorithm that solves real-world optimization problems. It is categorized as a stochastic parameter optimization method that has a broad spectrum of applications, notably neural networks, logistics, scheduling, and modeling. In practice, different optimization issues need different parameter settings. Due to DE simplicity, ease of implementation, and dependability, many scientists were interested in examining this algorithm. Nonetheless, the quality of DE and its variations are directly influenced by different mutation techniques and control parameter settings. In this paper, an overview and analogy of some algorithms that employ different mutation techniques will be illustrated. Additionally, a novel strategy that uses different mutation methods is proposed and compared with some existing strategies.
Barren ground depressions, natural H2 and orogenic gold deposits: Spatial link and geochemical model

A review of the localities in continental rocks where H2-rich gases have been reported, showed that they are mainly located near orogenic gold deposits. Two types of geomorphological features known as markers of gas venting in sedimentary basins were also systematically observed near orogenic gold deposits on satellite images. They consist in both barren ground depressions and high densities of small (<20 m in diameter) circular- and comet-shaped white spots in 32 and 7 localities, respectively. Point pattern analysis revealed that the white spots are self-organized, and similar to previously described vegetation patterns associated with termite mounds and fairy circles. We proposed a geochemical model to account for this relationship between orogenic gold deposits, H2 emanations and geomorphological features. Fe‑carbonates are ubiquitous mineral products associated with gold mineralization. They can further dissolve in the presence of aqueous fluid due to their high reactivity below 200 °C to produce magnetite and up to ∼1 mol H2 per kg of rock along with ∼3 mol/kg CO2. This process induces a solid volume decrease of 50 %. Therefore, we propose that Fe‑carbonate dissolution is (1) the primary source of H2 in orogenic gold deposit areas, and (2) involved in the formation of the geomorphological structures reported here, providing a new framework to understand their seemingly complex formation. Ground depressions and white spots are possible tools for gold exploration. Actually, we identified four new areas where we suspect possible orogenic gold deposits. The association between H2-rich gas and ground depressions was also made near other formations containing Fe‑carbonates such as iron formations and carbonatites. This suggests that H2 production through Fe‑carbonate dissolution is not restricted to gold deposits. The global H2 production in crustal rocks associated with Fe‑carbonate alteration is estimated to 3 × 105 mol/yr.
Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network

Solar irradiance forecasting is fundamental and essential for commercializing solar energy generation by overcoming output variability. Accurate forecasting depends on historical solar irradiance data, correlations between various meteorological variables (e.g., wind speed, humidity, and cloudiness), and influences between the weather contexts of spatially adjacent regions. However, existing studies have been limited to spatiotemporal analysis of a few variables, which have clear correlations with solar irradiance (e.g., sunshine duration), and do not attempt to establish atmospheric contextual information from a variety of meteorological variables. Therefore, this study proposes a novel solar irradiance forecasting model that represents atmospheric parameters observed from multiple stations as an attributed dynamic network and analyzes temporal changes in the network by extending existing spatio-temporal graph convolutional network (ST-GCN) models. By comparing the proposed model with existing models, we also investigated the contributions of (i) the spatial adjacency of the stations, (ii) temporal changes in the meteorological variables, and (iii) the variety of variables to the forecasting performance. We evaluated the performance of the proposed and existing models by predicting the hourly solar irradiance at observation stations in the Korean Peninsula. The experimental results showed that the three features are synergistic and have correlations that are difficult to establish using single-aspect analysis.
A Comparative Study of the Genetic Deep Learning Image Segmentation Algorithms

Medical optical imaging, with the aid of the “terahertz tomography”, is a novel medical imaging technique based on the electromagnetic waves. Such advanced imaging techniques strive for the detailed theoretical and computational analysis for better verification and validation. Two important aspects, the analytic approach for the understanding of the Schrodinger transforms and machine learning approaches for the understanding of the medical images segmentation, are presented in this manuscript. While developing an AI algorithm for complex datasets, the computational speed and accuracy cannot be overlooked. With the passage of time, machine learning approaches have been further modified using the Bayesian, genetic and quantum approaches. These strategies have boosted the efficiency of the machine learning, and specifically the deep learning tools, by taking into account the probabilistic, evolutionary and quantum qubits hypothesis and operations, respectively. The current research encompasses the detailed analysis of image segmentation algorithms based on the evolutionary approach. The image segmentation algorithm that converts the color model from RGB to HSI and the image segmentation algorithm that uses the clustering technique are discussed in detail, and further extensions of these genetic algorithms to quantum algorithms are proposed. Based on the genetic algorithm, the optimal selection of parameters is realized so as to achieve a better segmentation effect.
CLTSA: A Novel Tunicate Swarm Algorithm Based on Chaotic-Lévy Flight Strategy for Solving Optimization Problems

In this paper, we proposed a tunicate swarm algorithm based on Tent-Lévy flight (TLTSA) to avoid converging prematurely or failing to escape from a local optimal solution. First, we combined nine chaotic maps with the Lévy flight strategy to obtain nine different TSAs based on a Chaotic-Lévy flight strategy (CLTSA). Experimental results demonstrated that a TSA based on Tent-Lévy flight (TLTSA) performed the best among nine CLTSAs. Afterwards, the TLTSA was selected for comparative research with other well-known meta-heuristic algorithms. The 16 unimodal benchmark functions, 14 multimodal benchmark functions, 6 fixed-dimension functions, and 3 constrained practical problems in engineering were selected to verify the performance of TLTSA. The results of the test functions suggested that the TLTSA was better than the TSA and other algorithms in searching for global optimal solutions because of its excellent exploration and exploitation capabilities. Finally, the engineering experiments also demonstrated that a TLTSA solved constrained practical engineering problems more effectively.
Employment of Ensemble Machine Learning Methods for Human Activity Recognition

The endeavor to detect human activities and behaviors is targeted as a real-time detection mechanism that tends to predict the form of human motions and actions. Though sensors like accelerometer and gyroscopes are noticeable in human motion detection, categorizing unique and individual human gestures require software-based assistance. With the widespread implementation of machine learning algorithms, human actions can be distinguished into multiple classes. Several state-of-the-art machine learning algorithms can be applied to this specified field which will give suitable outcomes, yet due to the bulk of the dataset, complexity can be made apparent, which will reduce the efficiency of the model. In our proposed research, ensemble learning methods have been established by assembling several trained and tuned machine learning models. The adopted dataset for the model has been preprocessed through PCA (principal component analysis), SMOTE oversampling (synthetic minority oversampling technique), and K-means clustering, which reduced the dataset to essentials, keeping the weight of the features intact and reducing complexity. Maximum accuracy of 99.36% was achieved from both stacking and voting ensemble methods.
Further Exploration of Deep Aggregation for Shadow Detection

Shadow detection is a fundamental challenge in the field of computer vision. It requires the network to understand the global semantics and local details of the image. All existing methods depend on the aggregation of the features of a multi-stage pre-trained convolution neural network but in comparison to high-level capabilities, low-level capabilities provide less to the detection performance. Using low-level features not only increases the complex difficulty of the network but also reduces the time efficiency. In this article, we propose a new shadow detector, which only uses high-level features and explores the complementary information between adjacent feature layers. Experiments show that the technique in this paper can accurately detect shadows and perform well compared with the most advanced methods. The detailed experiments performed in three public shadow detection datasets SUB, UCF, and ISTD demonstrate that the suggested method is efficient and stable.
The ABC recommendations for validation of supervised machine learning results in biomedical sciences

OPINION article Front. Big Data, 27 September 2022Sec. Medicine and Public Health Volume 5 - 2022 | https://doi.org/10.3389/fdata.2022.979465
A Heterogeneous Federated Transfer Learning Approach with Extreme Aggregation and Speed

Federated learning (FL) is a data-privacy-preserving, decentralized process that allows local edge devices of smart infrastructures to train a collaborative model independently while keeping data localized. FL algorithms, encompassing a well-structured average of the training parameters (e.g., the weights and biases resulting from training-based stochastic gradient descent variants), are subject to many challenges, namely expensive communication, systems heterogeneity, statistical heterogeneity, and privacy concerns. In this context, our paper targets the four aforementioned challenges while focusing on reducing communication and computational costs by involving recursive least squares (RLS) training rules. Accordingly, to the best of our knowledge, this is the first time that the RLS algorithm is modified to completely accommodate non-independent and identically distributed data (non-IID) for federated transfer learning (FTL). Furthermore, this paper also introduces a newly generated dataset capable of emulating such real conditions and of making data investigation available on ordinary commercial computers with quad-core microprocessors and less need for higher computing hardware. Applications of FTL-RLS on the generated data under different levels of complexity closely related to different levels of cardinality lead to a variety of conclusions supporting its performance for future uses.