Reviewerly/exHarmony huggingface dataset:
FD-DE: Differential Evolution with fitness deviation based adaptation in parameter control

Differential Evolution (DE) is arguably one of the most powerful stochastic optimization algorithms for different optimization applications, however, even the state-of-the-art DE variants still have many weaknesses. In this study, a new powerful DE variant for single-objective numerical optimization is proposed, and there are several contributions within it: First, an enhanced wavelet basis function is proposed to generate scale factor F of each individual in the first stage of the evolution; Second, a hybrid trial vector generation strategy with perturbation and t-distribution is advanced to generate different trial vectors regarding different stages of the evolution; Third, a fitness deviation based parameter control is proposed for the adaptation of control parameters; Fourth, a novel diversity indicator is proposed and a restart scheme can be launched if necessary when the quality of the individuals is detected bad. The novel algorithm is validated using a large test suite containing 130 benchmarks from the universal test suites on single-objective numerical optimization, and the results approve the big improvement in comparison with several well-known state-of-the-art DE variants. Moreover, our algorithm is also validated under real-world optimization applications, and the results also support its superiority.
Incentive-Based Federated Learning for Digital-Twin-Driven Industrial Mobile Crowdsensing

Mobile crowdsensing has empowered the Industrial Internet of Things (IIoT) in many ways, such as vehicle-aided traffic flow scheduling, drone-aided visual inspections, etc. However, dynamic perception and cooperative decision making among these heterogeneous and resource-constrained mobile clients in IIoT remains a big challenge. In this article, we propose an incentive-based federated learning scheme for digital twin (DT)-driven industrial mobile crowdsensing. Specifically, we first design a DT-driven industrial mobile crowdsensing architecture to achieve dynamic perception of the complex IIoT environment, among heterogeneous and resource-constrained mobile clients. Second, we develop a novel incentive-based federated learning framework incorporated with a contract-based reputation mechanism and a Stackelberg-based interclient incentive mechanism, to optimize the model accuracy. Third, we devise a knowledge distillation algorithm for the federated learning framework, to address the heterogeneity of nonindependent and identically distributed (Non-IID) data. Extensive experiments on both MNIST/FEMNIST and CIFAR10/100 data sets demonstrate the outperformance of our proposed scheme, in terms of model accuracy, incentive fairness, and data compatibility, compared to state-of-the-art studies.
Building integrated representations through interleaved learning.

Inferring relationships that go beyond our direct experience is essential for understanding our environment. This capacity requires either building representations that directly reflect structure across experiences as we encounter them or deriving the indirect relationships across experiences as the need arises. Building structure directly into overlapping representations allows for powerful learning and generalization in neural network models, but building these so-called distributed representations requires inputs to be encountered in interleaved order. We test whether interleaving similarly facilitates the formation of representations that directly integrate related experiences in humans and what advantages such integration may confer for behavior. In a series of behavioral experiments, we present evidence that interleaved learning indeed promotes the formation of representations that directly link across related experiences. As in neural network models, interleaved learning gives rise to fast and automatic recognition of item relatedness, affords efficient generalization, and is especially critical for inference when learning requires statistical integration of noisy information over time. We use the data to adjudicate between several existing computational models of human memory and inference. The results demonstrate the power of interleaved learning and implicate the formation of integrated, distributed representations that support generalization in humans. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Beyond Graph Convolutional Network: An Interpretable Regularizer-Centered Optimization Framework

Graph convolutional networks (GCNs) have been attracting widespread attentions due to their encouraging performance and powerful generalizations. However, few work provide a general view to interpret various GCNs and guide GCNs' designs. In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centerd optimization framework, in which by building appropriate regularizers we can interpret most GCNs, such as APPNP, JKNet, DAGNN, and GNN-LF/HF. Further, under the proposed framework, we devise a dual-regularizer graph convolutional network (dubbed tsGCN) to capture topological and semantic structures from graph data. Since the derived learning rule for tsGCN contains an inverse of a large matrix and thus is time-consuming, we leverage the Woodbury matrix identity and low-rank approximation tricks to successfully decrease the high computational complexity of computing infinite-order graph convolutions. Extensive experiments on eight public datasets demonstrate that tsGCN achieves superior performance against quite a few state-of-the-art competitors w.r.t. classification tasks.
TranSDet: Toward Effective Transfer Learning for Small-Object Detection

Small-object detection is a challenging task in computer vision due to the limited training samples and low-quality images. Transfer learning, which transfers the knowledge learned from a large dataset to a small dataset, is a popular method for improving performance on limited data. However, we empirically find that due to the dataset discrepancy, directly transferring the model trained on a general object dataset to small-object datasets obtains inferior performance. In this paper, we propose TranSDet, a novel approach for effective transfer learning for small-object detection. Our method adapts a model trained on a general dataset to a small-object-friendly model by augmenting the training images with diverse smaller resolutions. A dynamic resolution adaptation scheme is employed to ensure consistent performance on various sizes of objects using meta-learning. Additionally, the proposed method introduces two network components, an FPN with shifted feature aggregation and an anchor relation module, which are compatible with transfer learning and effectively improve small-object detection performance. Extensive experiments on the TT100K, BUUISE-MO-Lite, and COCO datasets demonstrate that TranSDet achieves significant improvements compared to existing methods. For example, on the TT100K dataset, TranSDet outperforms the state-of-the-art method by 8.0% in terms of the mean average precision (mAP) for small-object detection. On the BUUISE-MO-Lite dataset, TranSDet improves the detection accuracy of RetinaNet and YOLOv3 by 32.2% and 12.8%, respectively.
RegraphGAN: A graph generative adversarial network model for dynamic network anomaly detection

Due to the wide application of dynamic graph anomaly detection in cybersecurity, social networks, e-commerce, etc., research in this area has received increasing attention. Graph generative adversarial networks can be used in dynamic graph anomaly detection due to their ability to model complex data, but the original graph generative adversarial networks do not have a method to learn reverse mapping and require an expensive process in recovering the potential representation of a given input. Therefore, this paper proposes a novel graph generative adversarial network by adding encoders to map real data to latent space to improve the training efficiency and stability of graph generative adversarial network models, which is named RegraphGAN in this paper. And this paper proposes a dynamic network anomaly edge detection method by combining RegraphGAN with spatiotemporal coding to solve the complex dynamic graph data and the problem of attribute-free node information coding challenges. Meanwhile, anomaly detection experiments are conducted on six real dynamic network datasets, and the results show that the dynamic network anomaly detection method proposed in this paper outperforms other existing methods.
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings

Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative training samples that encode the desired similarity semantics.Prior work relies on discrete citation relations to generate contrast samples.However, discrete citations enforce a hard cutoff to similarity.This is counter-intuitive to similarity-based learning and ignores that scientific papers can be very similar despite lacking a direct citation -a core problem of finding related research.Instead, we use controlled nearest neighbor sampling over citation graph embeddings for contrastive learning.This control allows us to learn continuous similarity, to sample hard-to-learn negatives and positives, and also to avoid collisions between negative and positive samples by controlling the sampling margin between them.The resulting method SciNCL outperforms the state-of-theart on the SciDocs benchmark.Furthermore, we demonstrate that it can train (or tune) language models sample-efficiently and that it can be combined with recent training-efficient methods.Perhaps surprisingly, even training a general-domain language model this way outperforms baselines pretrained in-domain. Related WorkContrastive Learning pulls representations of similar data points (positives) closer together, while representations of dissimilar documents (negatives) are pushed apart.A common contrastive objective
“It’s Not Just Hate”: A Multi-Dimensional Perspective on Detecting Harmful Speech Online

Well-annotated data is a prerequisite for good Natural Language Processing models.Too often, though, annotation decisions are governed by optimizing time or annotator agreement.We make a case for nuanced efforts in an interdisciplinary setting for annotating offensive online speech.Detecting offensive content is rapidly becoming one of the most important real-world NLP tasks.However, most datasets use a single binary label, e.g., for hate or incivility, even though each concept is multi-faceted.This modeling choice severely limits nuanced insights, but also performance.We show that a more fine-grained multi-label approach to predicting incivility and hateful or intolerant content addresses both conceptual and performance issues.We release a novel dataset of over 40,000 tweets about immigration from the US and UK, annotated with six labels for different aspects of incivility and intolerance.Our dataset not only allows for a more nuanced understanding of harmful speech online, models trained on it also outperform or match performance on benchmark datasets.Warning: This paper contains examples of hateful language some readers might find offensive.
Innovative Plug-and-Play System for Electrification of Wheel-Chairs

Self-propelled wheelchairs are challenging to drive on off-road routes or require enormous physical effort in situations where the gradient exceeds 8%. For most people, these situations are too strenuous and therefore impractical. This work presents an innovative plug-and-play system to electrify a manual wheelchair. With the constraint of not irreversibly modifying the original wheelchair, a mechanical system is developed to allow motors to turn the rear wheels by friction. Torque and power requirements have been derived by modeling and simulating in Matlab the physical system. To manufacture a working prototype, a control system using standard components is devised, and object-oriented firmware is developed with the C++ programming language. The result is a plug-and-play portable kit that can be adapted to wheelchairs of any size, and the expandable Human-Machine-Interface (HMI) makes it ready to expand the audience that can use it.
A convolutional neural network to detect possible hidden data in spatial domain images

Abstract Hiding secret data in digital multimedia has been essential to protect the data. Nevertheless, attackers with a steganalysis technique may break them. Existing steganalysis methods have good results with conventional Machine Learning (ML) techniques; however, the introduction of Convolutional Neural Network (CNN), a deep learning paradigm, achieved better performance over the previously proposed ML-based techniques. Though the existing CNN-based approaches yield good results, they present performance issues in classification accuracy and stability in the network training phase. This research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain images. The proposed method comprises three phases: pre-processing, feature extraction, and classification. Firstly, in the pre-processing phase, we use spatial rich model filters to enhance the noise within images altered by data hiding; secondly, in the feature extraction phase, we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features; and finally, in the classification, we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation, followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax function. The results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2% with reduced training time up to 30.81%.
QUATRE-EMS: QUATRE algorithm with novel adaptation of evolution matrix and selection operation for numerical optimization

Differential Evolution is a well-known stochastic algorithm in Evolutionary Computation (EC) for single-objective numerical optimization, and its powerful variants secured excellent ranks in recent competitions. These winner DE variants all employed binomial crossover which tackles the positional bias in crossover operation by treating the D parameters separately and independently, however, the binomial crossover in these winner DE variants still have bias from the spatial search perspective, and this was the reason why the QUasi-Affine TRansformation Evolution (QUATRE) algorithm in the literature was proposed. In this paper, a novel QUATRE algorithm with novel adaptation of Evolution Matrix and Selection operation (QUATRE-EMS) was proposed to further improve the overall performance of the QUATRE variants for numerical optimization and the main highlights can be summarized as follows. First, the number of control parameters is reduced from three in DE to two in QUATRE-EMS by proposing an auto-generated evolution matrix. Second, a novel adaptation scheme of the evolution matrix M is proposed for a better adaptation of the landscape of the objective during evolution; Third, a new selection operation is proposed instead of employing the canonical selection operation in DE. Finally, a large test suite containing 100 benchmarks from CEC2013, CEC2014, CEC2017 and CEC2022 test suites for single-objective numerical optimization is used for algorithm validation, and total 11 state-of-the-art algorithms including our QUATRE-EMS are taken into comparison. The experimental results show the superiority of our algorithm in comparison with former QUATRE variants and several state-of-the-art DE variants.
Decoding the ChatGPT mystery: A comprehensive exploration of factors driving AI language model adoption

The increasing ubiquity of Artificial Intelligence (AI) chatbots across a variety of sectors has sparked a burgeoning interest in deciphering the determinants that govern their adoption and usage. This study aims to examine the pivotal factors that influence the practical usage of the AI chatbot, ChatGPT, among a sample of university students. Leveraging the theoretical framework of planned behavior, the research model scrutinizes the interplay between knowledge application, perceived intelligence, usability, attitude, subjective norms, perceived behavioral control, trust, behavioral intention, and actual usage. Data procured from a survey of university students were examined through the lens of structural equation modeling. The outcomes reveal that knowledge application, perceived intelligence, and usability have a positive impact on attitudes towards ChatGPT. Perceived intelligence also influences knowledge application, usability, and trust. Concurrently, attitude and subjective norms notably affect behavioral intention. Contrary to expectations, perceived behavioral control did not significantly influence behavioral intention. Trust emerged as a crucial factor steering behavioral intention, which in turn, positively correlates with the actual usage of ChatGPT. These insights enrich the academic discourse on AI chatbot adoption and provide practical implications for AI developers, educators, and policy makers, striving to enhance user engagement with AI systems in educational contexts.
PyThaiNLP: Thai Natural Language Processing in Python

Wannaphong Phatthiyaphaibun, Korakot Chaovavanich, Charin Polpanumas, Arthit Suriyawongkul, Lalita Lowphansirikul, Pattarawat Chormai, Peerat Limkonchotiwat, Thanathip Suntorntip, Can Udomcharoenchaikit. Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023). 2023.
Ontology-Based Data Access with Dynamic TBoxes in DL-Lite

In this paper we introduce the notion of mapping-based knowledge base (MKB) to formalize the situation where both the extensional and the intensional level of the ontology are determined by suitable mappings to a set of (relational) data sources. This allows for making the intensional level of the ontology as dynamic as traditionally the extensional level is. To do so, we resort to the meta-modeling capabilities of higher-order Description Logics, which allow us to see concepts and roles as individuals, and vice versa. The challenge in this setting is to design tractable query answering algorithms. Besides the definition of MKBs, our main result is that answering instance queries posed to MKBs expressed in Hi(DL-LiteR) can be done efficiently. In particular, we define a query rewriting technique that produces first-order (SQL) queries to be posed to the data sources.
Focusing on What Really Matters: Irrelevance Pruning in Merge-and-Shrink

Merge-and-shrink (M&amp;S) is a framework to generate abstraction heuristics for cost-optimal planning. A recent approach computes simulation relations on a set of M&amp;S abstractions in order to identify states that are better than others. This relation is then used for pruning states in the search when a "better" state is already known. We propose the usage of simulation relations inside the M&amp;S framework in order to detect irrelevant transitions in abstract state spaces. This potentially simplifies the abstraction allowing M&amp;S to derive more informed heuristics. We also tailor M&amp;S to remove irrelevant operators from the planning task. Experimental results show the potential of our approach to construct well-informed heuristics and simplify the planning tasks prior to the search.
Improved Pattern Selection for PDB Heuristics in Classical Planning (Extended Abstract)

The iPDB approach selects patterns by a local search in the space of pattern collections. This search often gets stuck in local optima, which limits the quality of the resulting heuristic. In this research abstract, we report on current progress to tackle this problem. We investigate variable neighborhood search with encouraging experimental results.
Digital Forensics Tools

In this paper we will we reviewing the basic fundamentals of digital forensics and all go through the various types of forensics investigation teams available to us. We will also discuss about the different types of digital crimes that take place and the various tools present in order to counteract the crimes that are being committed. There will also be a comparative review among all the tools present based on various factors present giving the reader an abstract view about which tool to use for the best type of results.
The length of the longest common subsequence of two independent mallows permutations

The Mallows measure is a probability measure on $S_{n}$ where the probability of a permutation $\pi$ is proportional to $q^{l(\pi)}$ with $q>0$ being a parameter and $l(\pi)$ the number of inversions in $\pi$. We prove a weak law of large numbers for the length of the longest common subsequences of two independent permutations drawn from the Mallows measure, when $q$ is a function of $n$ and $n(1-q)$ has limit in $\mathbb{R}$ as $n\to\infty$.
Motion-Compensated Frame Interpolation Using Cellular Automata-Based Motion Vector Smoothing

Motion-Compensated Frame Interpolation (MCFI) is one of the common temporal-domain tamper operations, and it is used to produce faked video frames for improving the visual qualities of video sequences. The instability of temporal symmetry results in many incorrect Motion Vectors (MVs) for Bidirectional Motion Estimation (BME) in MCFI. The existing Motion Vector Smoothing (MVS) works often oversmooth or revise correct MVs as wrong ones. To overcome this problem, we propose a Cellular Automata-based MVS (CA-MVS) algorithm to smooth the Motion Vector Field (MVF) output by BME. In our work, a cellular automaton is constructed to deduce MV outliers according to a defined local evolution rule. By performing CA-based evolution in a loop iteration, we gradually expose MV outliers and reduce incorrect MVs resulting from oversmoothing as many as possible. Experimental results show the proposed algorithm can improve the accuracy of BME and provide better objective and subjective interpolation qualities when compared with the traditional MVS algorithms.
Second Report of the FAIRsFAIR Synchronisation Force (D5.5)

This is the report of the second FAIRsFAIR Synchronisation Force workshop, organised online as a series of eight sessions from 29th of April until 11th of June 2020. The objective of these sessions was to measure the progress towards implementing the recommendations outlined in the Turning FAIR into Reality report (2018). To do this, FAIRsFAIR brought together representatives of the Working Groups of the EOSC Executive Board, INFRAEOSC5 projects, ESFRI clusters, and the FAIRsFAIR European Group of FAIR Champions to share information on the progress of their FAIR-oriented activities and to discuss commonalities and priorities. The report addresses the EOSC Executive Board and Governing Board and their successors, as well as the European Commission’s EOSC programme (together called “the EOSC Governance” in this report), in addition to the participants and other EOSC-related projects. A table in the document summarises the amount of activity with regards to the 27 recommendations from the Turning FAIR into Reality report (TFiR). It is a snapshot in time and the observations and analysis in the report express an interpretation of the situation - confirmed by the workshop participants.