Reviewerly/exHarmony huggingface dataset:
On the Utility of Indirect Methods for Detecting Faking

Indirect indices for faking detection in questionnaires make use of a respondent’s deviant or unlikely response pattern over the course of the questionnaire to identify them as a faker. Compared with established direct faking indices (i.e., lying and social desirability scales), indirect indices have at least two advantages: First, they cannot be detected by the test taker. Second, their usage does not require changes to the questionnaire. In the last decades, several such indirect indices have been proposed. However, at present, the researcher’s choice between different indirect faking detection indices is guided by relatively little information, especially if conceptually different indices are to be used together. Thus, we examined and compared how well indices of a representative selection of 12 conceptionally different indirect indices perform and how well they perform individually and jointly compared with an established direct faking measure or validity scale. We found that, first, the score on the agreement factor of the Likert-type item response process tree model, the proportion of desirable scale endpoint responses, and the covariance index were the best-performing indirect indices. Second, using indirect indices in combination resulted in comparable and in some cases even better detection rates than when using direct faking measures. Third, some effective indirect indices were only minimally correlated with substantive scales and could therefore be used to partial faking variance from response sets without losing substance. We, therefore, encourage researchers to use indirect indices instead of direct faking measures when they aim to detect faking in their data.
Meaning Representation

Before the study of semantic analysis, this chapter explores meaning representation, a vital component in NLP before the discussion of semantic and pragmatic analysis. It studies four major meaning representation techniques which include: first-order predicate calculus (FOPC), semantic net, conceptual dependency diagram (CDD), and frame-based representation. After that it explores canonical form and introduces Fillmore’s theory of universal cases followed by predicate logic and inference work using FOPC with live examples.
Pragmatic Analysis and Discourse

After the discussion of semantic meaning and analysis, this chapter explores pragmatic analysis in linguistics and discourse phenomena. It also studies coherence and coreference as the key components of pragmatics and discourse critical to NLP, followed by discourse segmentation with different algorithms on Co-reference Resolution including Hobbs Algorithm, Centering Algorithm, Log-Linear Model, the latest machine learning methods, and evaluation schemes. This chapter also serves as the conceptual basis for Workshop#5: Sentiment Analysis and Text Classification in Chap. 14 .
Workshop#7 Building Chatbot with TensorFlow and Transformer Technology (Hour 13–14)

In previous 6 NLP workshops, we studied NLP implementation tools and techniques ranging from tokenization, N-gram generation to semantic and sentiment analysis with various key NLP Python enabling technologies: NLTK, spaCy, TensorFlow and contemporary Transformer Technology. This final workshop explores how to integrate them for the design and implementation of a live domain-based chatbot system on a movie domain. First, it explores the basis of chatbot system and introduce a knowledge domain—the Cornell Large Movie Conversation Dataset. Second, it conducts a step-by-step implementation of movie chatbot system which involve dialog pre-processing, mod-el construction, attention learning implementation, system integration and performance evaluation followed by live tests. Finally, it introduces a mini project for this workshop and present related chatbot datasets with resources in summary.
Integrating geometries of ReLU feedforward neural networks

This paper investigates the integration of multiple geometries present within a ReLU-based neural network. A ReLU neural network determines a piecewise affine linear continuous map, M, from an input space ℝm to an output space ℝn. The piecewise behavior corresponds to a polyhedral decomposition of ℝm. Each polyhedron in the decomposition can be labeled with a binary vector (whose length equals the number of ReLU nodes in the network) and with an affine linear function (which agrees with M when restricted to points in the polyhedron). We develop a toolbox that calculates the binary vector for a polyhedra containing a given data point with respect to a given ReLU FFNN. We utilize this binary vector to derive bounding facets for the corresponding polyhedron, extraction of "active" bits within the binary vector, enumeration of neighboring binary vectors, and visualization of the polyhedral decomposition (Python code is available at https://github.com/cglrtrgy/GoL_Toolbox). Polyhedra in the polyhedral decomposition of ℝm are neighbors if they share a facet. Binary vectors for neighboring polyhedra differ in exactly 1 bit. Using the toolbox, we analyze the Hamming distance between the binary vectors for polyhedra containing points from adversarial/nonadversarial datasets revealing distinct geometric properties. A bisection method is employed to identify sample points with a Hamming distance of 1 along the shortest Euclidean distance path, facilitating the analysis of local geometric interplay between Euclidean geometry and the polyhedral decomposition along the path. Additionally, we study the distribution of Chebyshev centers and related radii across different polyhedra, shedding light on the polyhedral shape, size, clustering, and aiding in the understanding of decision boundaries.
Dense captioning and multidimensional evaluations for indoor robotic scenes

The field of human-computer interaction is expanding, especially within the domain of intelligent technologies. Scene understanding, which entails the generation of advanced semantic descriptions from scene content, is crucial for effective interaction. Despite its importance, it remains a significant challenge. This study introduces RGBD2Cap, an innovative method that uses RGBD images for scene semantic description. We utilize a multimodal fusion module to integrate RGB and Depth information for extracting multi-level features. And the method also incorporates target detection and region proposal network and a top-down attention LSTM network to generate semantic descriptions. The experimental data are derived from the ScanRefer indoor scene dataset, with RGB and depth images rendered from ScanNet's 3D scene serving as the model's input. The method outperforms the DenseCap network in several metrics, including BLEU, CIDEr, and METEOR. Ablation studies have confirmed the essential role of the RGBD fusion module in the method's success. Furthermore, the practical applicability of our method was verified within the AI2-THOR embodied intelligence experimental environment, showcasing its reliability.
Lightweight authentication scheme based on Elliptic Curve El Gamal

Elliptic Curve Cryptography (ECC) is a promising cryptosystem as it presents the ideal choice for implementing public-key cryptography in resource-limited devices, such as those found in envisioned Internet of Things applications, e.g. wireless sensors. Elliptic Curve ElGamal (ECEG) is a renowned member of the ECC family. However, it requires encoding the message before encryption and decoding it after decryption. To better improve the processing time and to avoid sharing a lookup table, we propose, in this document, a new method that exchanges encoding parameters and authenticates endpoints at the same time. So, our new protocol ensures the authentication of communication parties and integrity of ephemeral encoding parameters. The robustness of our solution is proven by the Scyther tool. Experimental tests have been carried out, and the performance of the new methods is provided and compared with RSA and related work. Security and performance analysis show that our new scheme has achieved improved functionality and higher security while maintaining low computational.
A Deep Graph Matching-Based Method for Trajectory Association in Vessel Traffic Surveillance

Vessel traffic surveillance in inland waterways extensively relies on the Automatic Identification Syst em (AIS) and video cameras. While video data only captures the visual appearance of vessels, AIS data serves as a valuable source of vessel identity and motion information, such as position, speed, and heading. To gain a comprehensive understanding of the behavior and motion of known-identity vessels, it is necessary to fuse the AIS-based and video-based trajectories. An important step in this fusion is to obtain the correspondence between moving targets by trajectory association. Thus, we focus solely on trajectory association in this work and propose a trajectory association method based on deep graph matching. We formulate trajectory association as a graph matching problem and introduce an attention-based flexible context aggregation mechanism to exploit the semantic features of trajectories. Compared to traditional methods that rely on manually designed features, our approach captures complex patterns and correlations within trajectories through end-to-end training. The introduced dustbin mechanism can effectively handle outliers during matching. Experimental results on synthetic and real-world datasets demonstrate the exceptional performance of our method in terms of trajectory association accuracy and robustness.
Hierarchical Classification for Large-Scale Learning

Deep neural networks (DNNs) have drawn much attention due to their success in various vision tasks. Current DNNs are used on data with a relatively small number of classes (e.g., 1000 or less) and employ a fully connected layer for classification, which allocates one neuron for each class and thus, per-example, the classification scales as O(K) with the number of classes K. This approach is computationally intensive for many real-life applications where the number of classes is very large (e.g., tens of thousands of classes). To address this problem, our paper introduces a hierarchical approach for classification with a large number of classes that scales as O(K) and could be extended to O(logK) with a deeper hierarchy. The method, called Hierarchical PPCA, uses a self-supervised pretrained feature extractor to obtain meaningful features and trains Probabilistic PCA models on the extracted features for each class separately, making it easy to add classes without retraining the whole model. The Mahalanobis distance is used to obtain the classification result. To speed-up classification, the proposed Hierarchical PPCA framework clusters the image class models, represented as Gaussians, into a smaller number of super-classes using a modified k-means clustering algorithm. The classification speed increase is obtained by Hierarchical PPCA assigning a sample to a small number of the most likely super-classes and restricting the image classification to the image classes corresponding to these super-classes. The fact that the model is trained on each class separately makes it applicable to training on very large datasets such as the whole ImageNet with more than 10,000 classes. Experiments on three standard datasets (ImageNet-100, ImageNet-1k,and ImageNet-10k) indicate that the hierarchical classifier can achieve a superior accuracy with up to a 16-fold speed increase compared to a standard fully connected classifier.
MOC: Multi-modal Sentiment Analysis via Optimal Transport and Contrastive Interactions

Multi-modal sentiment analysis (MSA) aims to utilize information from various modalities to improve the classification of emotions. Most existing studies employ attention mechanisms for modality fusion, overlooking the heterogeneity of different modalities. To address this issue, we propose an approach that leverages optimal transport for modality alignment and fusion, specifically focusing on distributional alignment. However, solely relying on the optimal transport module may result in a deficiency of intra-modal and inter-sample interactions. To tackle this deficiency, we introduce a double-modal contrastive learning module. Specifically, we propose a model MOC (Multi-modal sentiment analysis via Optimal transport and Contrastive interactions), which integrates optimal transport and contrastive learning. Through empirical comparisons on three established multi-modal sentiment analysis datasets, we demonstrate that our approach achieves state-of-the-art performance. Additionally, we conduct extended ablation studies to validate the effectiveness of each proposed module.
Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution

The Gray Wolf Optimizer (GWO) is an established algorithm for addressing complex optimization tasks. Despite its effectiveness, enhancing its precision and circumventing premature convergence is crucial to extending its scope of application. In this context, our study presents the Cauchy Gray Wolf Optimizer (CGWO), a modified version of GWO that leverages Cauchy distributions for key algorithmic improvements. The innovation of CGWO lies in several areas: First, it adopts a Cauchy distribution-based strategy for initializing the population, thereby broadening the global search potential. Second, the algorithm integrates a dynamic inertia weight mechanism, modulated non-linearly in accordance with the Cauchy distribution, to ensure a balanced trade-off between exploration and exploitation throughout the search process. Third, it introduces a Cauchy mutation concept, using inertia weight as a probability determinant, to preserve diversity and bolster the capability for escaping local optima during later search phases. Furthermore, a greedy strategy is employed to incrementally enhance solution accuracy. The performance of CGWO was rigorously evaluated using 23 benchmark functions, demonstrating significant improvements in convergence rate, solution precision, and robustness when contrasted with conventional algorithms. The deployment of CGWO in solving the engineering challenge of pressure vessel design illustrated its superiority over traditional methods, highlighting its potential for widespread adoption in practical engineering contexts.
Motif-SocialRec: A Multi-channel Interactive Semantic Extraction Model for Social Recommendation

To capture complex interaction semantics beyond pairwise relationships for social recommendation, a novel recommendation model, namely Motif-SocialRec, is proposed under the perspective of motif. It efficiently describes interaction pattern from multi-channel with different motifs. In the model, we depict a series of local structures by motif, which can describe the high-level interactive semantics in the fused network from three views. By employing hypergraph convolution network, representations that preserve potential semantic patterns can be learned. Additionally, we enhance the learned representations by establishing self-supervised learning tasks on different scales to further explore the inherent characteristics of the network. Finally, a joint optimization model is constructed by integrating the primary and auxiliary tasks to produce recommendation predictions. Results of extensive experiments on four real-world datasets show that Motif-SocialRec significantly outperforms baselines in terms of different evaluation metrics.
A Graph Convolution Neural Network for User-Group Aided Personalized Session-Based Recommendation

Session-based recommendation systems aim to predict the next user interaction based on the items with which the user interacts in the current session. Currently, graph neural network-based models have been widely used and proven more effective than others. However, these session-based models mainly focus on the user-item and item-item relations in historical sessions while ignoring information shared by similar users. To address the above issues, a new graph-based representation, User-item Group Graph, which considers not only user-item and item-item but also user-user relations, is developed to take advantage of natural sequential relations shared by similar users. A new personalized session-based recommendation model is developed based on this representation. It first generates groups according to user-related historical item sequences and then uses a user group preference recognition module to capture and balance between group-item preferences and user-item preferences. Comparison experiments show that the proposed model outperforms other state-of-art models when similar users are effectively grouped. This indicates that grouping similar users can help find deep preferences shared by users from the same group and is instructive in finding the most appropriate next item for the current user.
WERECE: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement

The era of educational big data has sparked growing interest in extracting and organizing educational concepts from massive amounts of information. Outcomes are of the utmost importance for artificial intelligence–empowered teaching and learning. Unsupervised educational concept extraction methods based on pre-trained models continue to proliferate due to ongoing advances in semantic representation. However, it remains challenging to directly apply pre-trained large language models to extract educational concepts; pre-trained models are built on extensive corpora and do not necessarily cover all subject-specific concepts. To address this gap, we propose a novel unsupervised method for educational concept extraction based on word embedding refinement (i.e., word embedding refinement–based educational concept extraction (WERECE)). It integrates a manifold learning algorithm to adapt a pre-trained model for extracting educational concepts while accounting for the geometric information in semantic computation. We further devise a discriminant function based on semantic clustering and Box–Cox transformation to enhance WERECE’s accuracy and reliability. We evaluate its performance on two newly constructed datasets, EDU-DT and EDUTECH-DT. Experimental results show that WERECE achieves an average precision up to 85.9%, recall up to 87.0%, and F1 scores up to 86.4%, which significantly outperforms baselines (TextRank, term frequency–inverse document frequency, isolation forest, K-means, and one-class support vector machine) on educational concept extraction. Notably, when WERECE is implemented with different parameter settings, its precision and recall sensitivity remain robust. WERECE also holds broad application prospects as a foundational technology, such as for building discipline-oriented knowledge graphs, enhancing learning assessment and feedback, predicting learning interests, and recommending learning resources.
Entanglement and Symmetry Structure of N(= 3) Quantum Oscillators with Disparate Coupling Strengths in a Common Quantum Field Bath

In this paper, we study the entanglement structure of a system of N quantum oscillators with distinctive coupling strengths, all linearly coupled to a common massless scalar quantum field. This study is helpful in characterizing the notion of an entanglement domain and its symmetry features, which is useful for understanding the interplay between different levels of structure in many-body quantum systems. The effect of the quantum field on the system is derived via the influence functional and the correlation functions are obtained from the solutions of the evolutionary operator of the reduced density matrix. They are then used to construct the covariance matrix, which forms the basis for our analysis of the structure of quantum entanglement in this open system. To make the physical features explicit, we consider a system of three quantum coupled oscillators placed at the vertices of an equilateral triangle with disparate pairwise couplings. We analyze the entanglement between one oscillator and the other two with equal (symmetric) and unequal (asymmetric) coupling strengths. As a physical illustration, we apply the results for these two different configurations to address some basic issues in macroscopic quantum phenomena from the quantum entanglement perspective.
Research on Panorama Generation from a Multi-Camera System by Object-Distance Estimation

Panoramic imagery from multi-camera systems often suffers the problem of geometric mosaicking errors due to eccentric errors between the optical centers of cameras and variations in object-distances within the panoramic environment. In this paper, an inverse rigorous panoramic imaging model was derived completely for a panoramic multi-camera system. Additionally, we present an estimation scheme aimed at extracting object-distance information to enhance the seamlessness of panoramic image stitching. The essence of the scheme centers around our proposed object-space-based image matching algorithm called the Panoramic Vertical Line Locus (PVLL). As a result, panoramas were generated using the proposed inverse multi-cylinder projection method, utilizing the estimated object-distance information. The experiments conducted on our developed multi-camera system demonstrate that the root mean square errors (RMSEs) in the overlapping areas of panoramic images are no more than 1.0 pixel. In contrast, the RMSEs of the conventional traditional methods are typically more than 6 pixels, and in some cases, even exceed 30 pixels. Moreover, the inverse imaging model has successfully addressed the issue of empty pixels. The proposed method can effectively meet the accurate panoramic imaging requirements for complex surroundings with varied object-distance information.
PoShapley-BCFL: A Fair and Robust Decentralized Federated Learning Based on Blockchain and the Proof of Shapley-Value

Recently, blockchain-based Federated learning (BCFL) has emerged as a promising technology for promoting data sharing in the Internet of Things (IoT) without relying on a central authority, while ensuring data privacy, security, and traceability. However, it remains challenging to design an decentralized and appropriate incentive scheme that should promise a fair and efficient contribution evaluation for participants while defending against low-quality data attacks. Although Shapley-Value (SV) methods have been widely adopted in FL due to their ability to quantify individuals' contributions, they rely on a central server for calculation and incur high computational costs, making it impractical for decentralized and large-scale BCFL scenarios. In this paper, we designed and evaluated PoShapley-BCFL, a new blockchain-based FL approach to accommodate both contribution evaluation and defense against inferior data attacks. Specifically, we proposed PoShapley, a Shapley-value-enabled blockchain consensus protocol tailored to support a fair and efficient contribution assessment in PoShapley-BCFL. It mimics the Proof-of-Work mechanism that allows all participants to compute contributions in parallel based on an improved lightweight SV approach. Following using the PoShapley protocol, we further designed a fair-robust aggregation rule to improve the robustness of PoShapley-BCFL when facing inferior data attacks. Extensive experimental results validate the accuracy and efficiency of PoShapley in terms of distance and time cost, and also demonstrate the robustness of our designed PoShapley-BCFL.