Reviewerly/exHarmony huggingface dataset:
Multidocument Aspect Classification for Aspect-Based Abstractive Summarization

Multidocument aspect-based summarization (AspSumm) aims to generate focused summaries based on the target aspects from a cluster of relevant documents. Generating such summaries can better satisfy readers’ specific points of interest, as readers may have different concerns about the same articles. However, previous methods usually generate aspect-based summaries based on the given aspects without using the relationship among aspects to assist in the summarization. In this work, we propose a two-stage general framework for multidocument AspSumm. The model first discovers the latent relationship among aspects and then uses relevant sentences selected by aspect discovery to generate abstractive summaries. We exploit latent dependencies among aspects using a tag mask training (TMT) strategy, which increases the interpretability of the model. In addition to improvements in summarization over aspect-based strong baselines, experimental results show that our proposed model can accurately discover multidomain aspects on the WikiAsp dataset.
A multiview clustering framework for detecting deceptive reviews

Online reviews, which play a key role in the ecosystem of nowadays business, have been the primary source of consumer opinions. Due to their importance, professional review writing services are employed for paid reviews and even being exploited to conduct opinion spam. Posting deceptive reviews could mislead customers, yield significant benefits or losses to service vendors, and erode confidence in the entire online purchasing ecosystem. In this paper, we ferret out deceptive reviews originated from professional review writing services. We do so even when reviewers leverage a number of pseudonymous identities to avoid the detection. To unveil the pseudonymous identities associated with deceptive reviewers, we leverage the multiview clustering method. This enables us to characterize the writing style of reviewers (deceptive vs normal) and cluster the reviewers based on their writing style. Furthermore, we explore different neural network models to model the writing style of deceptive reviews. We select the best performing neural network to generate the representation of reviews. We validate the effectiveness of the multiview clustering framework using real-world Amazon review data under different experimental scenarios. Our results show that our approach outperforms previous research. We further demonstrate its superiority through a large-scale case study based on publicly available Amazon datasets.
Detecting Fraudulent Student Communication in a Multiple Choice Online Test Environment

Online evaluation systems, pervasive nowadays, are known to be susceptible to higher fraud risks. This work proposes a novel and robust method to detect potential fraud acts in online multiple-choice question (MCQ) exams. For the first time, the communication probability between the examinees is statistically assessed based on the concordance of responses and answer time against null expectations and is subsequently used to identify potential fraud behavior. The model is sensitive to the direction of communication acts, distinguishing content consumption from production, as well as multiwise communication channels. Online remote tests from engineering courses at Técnico Lisboa are used as a case study. We show that the cumulative contribution of concordant responses between students, when recurrent, offers a way of signaling fraud behavior. Separating content production from consumption reveals the underlying student role played in potential fraud acts. Collusion behavior is assessed against null models of fraud and conformity, and therefore being statistically framed and offering a solid criterion to guide tutors in ascertaining fraud and discouraging communication.
Mixed-Up Experience Replay for Adaptive Online Condition Monitoring

Data-driven predictive maintenance reduces manufacturing downtime, and complex process-sensing relationships encourage the use of Deep Learning to automatically extract features. However, labeled training data is often lacking, and novel fault conditions may occur. Practical deployments must learn from unlabeled data, adapt to emerging conditions, and do so without prior knowledge of when the condition changes. Combining state-of-the-art Self-Supervised Learning (SSL) with Continual Learning (CL) facilitates adaptation as new conditions are observed. This study proposes a framework for adaptive online condition monitoring based on Barlow Twins SSL and novel Mixed-Up Experience Replay (MixER) for unsupervised CL. Tailored for 1D sensing data, Barlow Twins effectively clusters unlabeled data. When combined with MixER, the system outperforms state-of-the-art unsupervised CL on a motor health condition data set, reaching 92.4% classification accuracy. Future work will demonstrate human-in-the-loop integration for real manufacturing environments.
An Adaptive Semantic Mining Framework for Heterogeneous Information Network Embedding

Heterogeneous information network (HIN) embedding aims to map heterogeneous nodes to the low-dimensional vector space. The existing embedding models cannot determine the optimal length of semantics automatically and reveal full semantic information adaptively for different heterogeneous networks. To address this challenge, an HIN embedding model with adaptive semantic mining is proposed. First, we project heterogeneous nodes into the same space and aggregate the features of target types in the first-degree range. Then, the semantics of different node types is combined through the attention mechanism, and latent meta-paths are mined using the attention coefficients. Finally, multiple feature aggregation layers are stacked with residual blocks. The residual weights control the proportion of semantics transferred between layers to aggregate more distal features selectively. In addition, we designed the Selected DropLink unit to remove links which transfer negative information, which can further improve the resistance of model to over-smoothing. Experiments show that our model can obtain more accurate embedding results and can automatically mine complex semantic connections between heterogeneous nodes without prior definition of meta-path and semantic depth.
FBSE-Based JPEG Image Compression

The purpose of image compression is to minimize the storage space required to store images and maintain the relevant information in them. In this article, we presented a Fourier-Bessel series expansion (FBSE) based image compression technique, which is motivated by the Joint Photographic Experts Group (JPEG), which uses discrete cosine transform (DCT) in the process. FBSE of order-0 has structural similarity with DCT and provides real representation for real signals. The proposed methodology has used FBSE in place of DCT for image compression. The article compares the compression ratio of both the methodology and results have shown better compression performance of FBSE-based image compression.
Code Review Automation: Strengths and Weaknesses of the State of the Art

The automation of code review has been tackled by several researchers with the goal of reducing its cost. The adoption of deep learning in software engineering pushed the automation to new boundaries, with techniques <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">imitating</i> developers in generative tasks, such as commenting on a code change as a reviewer would do or addressing a reviewer’s comment by modifying code. The performance of these techniques is usually assessed through quantitative metrics, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , the percentage of instances in the test set for which correct predictions are generated, leaving many open questions on the techniques’ capabilities. For example, knowing that an approach is able to correctly address a reviewer’s comment in 10% of cases is of little value without knowing what was asked by the reviewer: What if in all successful cases the code change required to address the comment was just the removal of an empty line? In this paper we aim at characterizing the cases in which three code review automation techniques tend to succeed or fail in the two above-described tasks. The study has a strong qualitative focus, with ~105 man-hours of manual inspection invested in manually analyzing correct and wrong predictions generated by the three techniques, for a total of 2,291 inspected predictions. The output of this analysis are two taxonomies reporting, for each of the two tasks, the types of code changes on which the experimented techniques tend to succeed or to fail, pointing to areas for future work. A result of our manual analysis was also the identification of several issues in the datasets used to train and test the experimented techniques. Finally, we assess the importance of researching in techniques specialized for code review automation by comparing their performance with ChatGPT, a general purpose large language model, finding that ChatGPT struggles in commenting code as a human reviewer would do.
FAPP: Extremely Fast Approach to Boosting Image Matching Precision

Feature matching is a critical problem in the field of computer vision, which serves as the foundation for many high-level computer vision applications. This paper aims to improve the accuracy of feature matching by eliminating mismatches in putative matches. To achieve this goal, we propose an extremely fast approach to boosting image matching precision (FAPP). The key idea behind FAPP is that correct feature matches have similar Euclidean distances, and the sine values of the angles between correct feature matches and the horizontal axis are also similar. Consequently, putative matches can be represented as two-dimensional coordinate points (sine value, Euclidean distance), which makes correct and incorrect feature matches have different degrees of clustering. Furthermore, the coordinate points are divided into grid spaces so that the coordinate points are distributed in different grid areas. Through adaptive parameter estimation, we determine a threshold for the number of correct feature matches within each grid, thereby eliminating false feature matches. In addition, to validate the effectiveness of FAPP, we conducted experiments on two public datasets and compared the results with several existing classical methods. The experimental outcomes demonstrate the superior performance of FAPP over the existing classical methods. Furthermore, the method has been applied to 3D reconstruction with good results. Source code: https://github.com/caomw/fapp.
A Semantics-enhanced Topic Modelling Technique: Semantic-LDA

Topic modelling is a beneficial technique used to discover latent topics in text collections. But to correctly understand the text content and generate a meaningful topic list, semantics are important. By ignoring semantics, that is, not attempting to grasp the meaning of the words, most of the existing topic modelling approaches can generate some meaningless topic words. Even existing semantic-based approaches usually interpret the meanings of words without considering the context and related words. In this article, we introduce a semantic-based topic model called semantic-LDA that captures the semantics of words in a text collection using concepts from an external ontology. A new method is introduced to identify and quantify the concept–word relationships based on matching words from the input text collection with concepts from an ontology without using pre-calculated values from the ontology that quantify the relationships between the words and concepts. These pre-calculated values may not reflect the actual relationships between words and concepts for the input collection, because they are derived from datasets used to build the ontology rather than from the input collection itself. Instead, quantifying the relationship based on the word distribution in the input collection is more realistic and beneficial in the semantic capture process. Furthermore, an ambiguity handling mechanism is introduced to interpret the unmatched words, that is, words for which there are no matching concepts in the ontology. Thus, this article makes a significant contribution by introducing a semantic-based topic model that calculates the word–concept relationships directly from the input text collection. The proposed semantic-based topic model and an enhanced version with the disambiguation mechanism were evaluated against a set of state-of-the-art systems, and our approaches outperformed the baseline systems in both topic quality and information filtering evaluations.
Concept Drift Adaptation by Exploiting Drift Type

Concept drift is a phenomenon where the distribution of data streams changes over time. When this happens, model predictions become less accurate. Hence, models built in the past need to be re-learned for the current data. Two design questions need to be addressed in designing a strategy to re-learn models: which type of concept drift has occurred, and how to utilize the drift type to improve re-learning performance. Existing drift detection methods are often good at determining when drift has occurred. However, few retrieve information about how the drift came to be present in the stream. Hence, determining the impact of the type of drift on adaptation is difficult. Filling this gap, we designed a framework based on a lazy strategy called Type-Driven Lazy Drift Adaptor (Type-LDA). Type-LDA first retrieves information about both how and when a drift has occurred, then it uses this information to re-learn the new model. To identify the type of drift, a drift type identifier is pre-trained on synthetic data of known drift types. Furthermore, a drift point locator locates the optimal point of drift via a sharing loss. Hence, Type-LDA can select the optimal point, according to the drift type, to re-learn the new model. Experiments validate Type-LDA on both synthetic data and real-world data, and the results show that accurately identifying drift type can improve adaptation accuracy.
Utility-aware Privacy Perturbation for Training Data

Data perturbation under differential privacy constraint is an important approach of protecting data privacy. However, as the data dimensions increase, the privacy budget allocated to each dimension decreases and thus the amount of noise added increases, which eventually leads to lower data utility in training tasks. To protect the privacy of training data while enhancing data utility, we propose a Utility-aware training data Privacy Perturbation scheme based on attribute Partition and budget Allocation (UPPPA). UPPPA includes three procedures: the quantification of attribute privacy and attribute importance, attribute partition, and budget allocation. The quantification of attribute privacy and attribute importance based on information entropy and attribute correlation provide an arithmetic basis for attribute partition and budget allocation. During the attribute partition, all attributes of training data are classified into high and low classes to achieve privacy amplification and utility enhancement. During the budget allocation, a γ-privacy model is proposed to balance data privacy and data utility so as to provide privacy constraint and guide budget allocation. Three comprehensive sets of real-world data are applied to evaluate the performance of UPPPA. Experiments and privacy analysis show that our scheme can achieve the tradeoff between privacy and utility.
Quantum speed limit time, non-Markovianity and quantum phase transition in extended <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg" display="inline" id="d1e362"><mml:mrow><mml:mi>X</mml:mi><mml:mi>Y</mml:mi></mml:mrow></mml:math> spin chain with Dzyaloshinsky–Moriya interaction

The effects of Dzyaloshinsky–Moriya (DM) interaction on quantum speed limit time (QSLT) of a single qubit and non-Markovianity of a coupled driven extended XY spin chain are investigated. The known quantum critical points of the model are left unaffected when DM interaction points in the direction of applied field. Anti-parallel DM interaction to the magnetic field leads to a new critical point of the model whose position is a function of the strength of the three-site interaction. Overall, the dynamics of the two quantities varies differently in a given phase of the model from point to point in the parameter space. Our findings also show that the anti-parallel DM interaction results in fast quantum evolution which might be beneficial for certain quantum computation purposes.
Automata for knowledge assessment based on the structure of observed learning outcome taxonomy

The process of adaptive assessment of individuals' knowledge by knowledge space theory has been implemented in deterministic finite automata. However, such automata are not suitable for polytomous situations, which limits the application of automata for knowledge assessment. To overcome this limitation, this paper combines the structure of observed learning outcome (SOLO) taxonomy with the response scale and proposes an automata knowledge assessment process that can assess individuals' knowledge in polytomous situations. Specifically, an automaton suitable for assessing knowledge in the SOLO taxonomy situation is proposed, and a questioning rule is provided based on the quasi-order relation between items. Then, relying on the automaton state update and the questioning rule, an automata knowledge assessment process for knowledge assessment in the SOLO taxonomy situation is given. Finally, empirical research and simulation experiments show that it is necessary to provide a questioning rule for the automaton and that the automata knowledge assessment process proposed in this paper is feasible.
Contrastive fine-tuning for low-resource graph-level transfer learning

Due to insufficient supervision and the gap between pre-training pretext tasks and downstream tasks, transferring pre-trained graph neural networks (GNNs) to downstream tasks in low-resource scenarios remains challenging. In this paper, a Contrastive Fine-tuning (Con-tuning) framework is proposed for low-resource graph-level transfer learning, and a graph-level supervised contrastive learning (SCL) task is designed within the framework as the first attempt to introduce SCL for fine-tuning processes of pre-trained GNNs. The SCL task compensates for the insufficient supervision problem in low-resource scenarios and narrows the gap between pretext tasks and downstream tasks. To further reinforce the supervision signal in the SCL task, we devise a graphon theory based labeled graph generator to extract the generalized knowledge of a specific class of graphs. Based on this knowledge, graph-level templates are generated for each class and used as contrastive samples in the SCL task. Then, the proposed Con-tuning framework jointly learns the SCL task and downstream tasks to effectively fine-tune the pre-trained GNNs for downstream tasks. Extensive experiments with eight real-world datasets show that Con-tuning framework enables pre-trained GNNs to achieve better performance on graph-level downstream tasks in low-resource settings.
Using outlier elimination to assess learning-based correspondence matching methods

Recently, deep learning (DL) technology has been widely used in correspondence matching. The learning-based models are usually trained on benign image pairs with partial overlaps. Since DL model is usually data-dependent, non-overlapping images may be used as poison samples to fool the model and produce false registrations. In this study, we propose an outlier elimination-based assessment method (OEAM) to assess the registrations of learning-based correspondence matching method on partially overlapping and non-overlapping images. OEAM first eliminates outliers based on spatial paradox. Then OEAM implements registration assessment in two streams using the obtained core correspondence set. If the cardinality of the core set is sufficiently small, the input registration is assessed as a low-quality registration. Otherwise, it is assessed to be of high quality, and OEAM improves its registration performance using the core set. OEAM is a post-processing technique imposed on learning-based method. The comparison experiments are implemented on outdoor (YFCC100M) and indoor (SUN3D) datasets using four deep learning-based methods. The experimental results on registrations of partially overlapping images show that OEAM can reliably infer low-quality registrations and improve performance on high-quality registrations. The experiments on registrations of non-overlapping images demonstrate that learning-based methods are vulnerable to poisoning attacks launched by non-overlapping images, and OEAM is robust against poisoning attacks crafted by non-overlapping images.
Combined effects of temperature and confinement on the Shannon entropy of two-dimensional hydrogenic impurity states in the GaAs semiconductor quantum well

Understanding the quantum properties of two-dimensional (2-D) hydrogenic impurity states in the GaAs quantum well is essential for their applications in the semiconductor devices. Here, we report the combined effects of temperature and quantum confienment on the Shannon entropy of 2-D hydrogenic impurity states in the GaAs quantum well. First, by calculating the position and momentum space Shannon entropy (Sr and Sp), as well as the Shannon entropy sum (St), for the hydrogenic impurity states in the GaAs quantum well at room temperature, we show fascinating quantum confinement properties in the two conjugated spaces. Then, the effects of temperature on the Shannon entropy for different hydrogenic impurity states are examined and compared. Intriguingly, we discover that the Shannon entropy sum exhibits the property of translation invariance under the influence of temperature. Finally, with the radius of the quantum well fixed at a constant, we observe that the value of the Shannon entropy for a given quantum state is highly sensitive to the variations in temperature. While the size of circular quantum well is constrained by design parameters, such as the fabrication process and intended application, temperature adjustment emerges as a viable means to control the quantum properties of 2-D hydrogenic impurities in the quantum well. This research presents a pioneering method that employs temperature as a versatile tool for modulating the Shannon entropy of hydrogenic impurity states in the quantum well, and has potential applications in the field of semiconductor physics, material physics, chemical physics, etc.