Reviewerly/exHarmony huggingface dataset:
Improving adversarial robustness through a curriculum-guided reliable distillation

Adversarial distillation (AD) which combines adversarial training with knowledge distillation has become a powerful procedure to mitigate the effects of adversarial examples to deep neural networks, which aims at distilling a robust student network from a pre-trained robust teacher network. In AD, the teacher's reliability has been a crucial problem and recent work incorporates a self-distillation loss on the student into the AD framework, encouraging the student to partially trust the teacher and gradually trust itself more. However, the only key factor to control this trust level cannot be adaptive enough for different training samples, guiding the student to trust itself inappropriately and inspiring us to pursue a better way to obtain more reliable supervision. In this paper we revisit the performance variation of all training samples from the teacher to student, showing that previous work is not always adaptive with the distillation and can be further refined. Accordingly, a more effective and justified supervision, namely Curricular Adversarial Distillation (CAD), is proposed to help boost the self-distillation process. In CAD, the KL divergences between both the clean and adversarial outputs of teacher and the smoothed labels are calculated for the supervision to address the teacher's unreliability in the self-distillation. Besides, the smoothed labels follow a curriculum-style scheduler, getting smoothed to different degrees at different training stages and helping the teacher continuously be adaptive to the varying adversarial examples from student. Extensive experiments have shown the superiority of our strategy in distilling a robust student network against various attacks.
Analysis and visualization of the parameter space of matrix factorization-based recommender systems

Recommender system is the most successful commercial technology in the past decade. Technical mammoth such as Temu, TikTok and Amazon utilize the technology to generate enormous revenues each year. Although there have been enough research literature on accuracy enhancement of the technology, explainable AI is still a new idea to the field. In 2022, the author of this paper provides a geometric interpretation of the matrix factorization-based methods and uses geometric approximation to solve the recommendation problem. We continue the research in this direction in this paper, and visualize the inner structure of the parameter space of matrix factorization technologies. We show that the parameters of matrix factorization methods are distributed within a hyper-ball. After further analysis, we prove that the distribution of the parameters is not multivariate normal.
A novel signal reconstruction method with sharp feature preservation

Aiming at the fact that traditional signal reconstruction method based on traditional methods such as Fourier and wavelets transformations cannot preserve the sharp features in the original data well, a signal reconstruction method is proposed in this paper based on a class of orthogonal piecewise polynomial function system—named as V-system. Firstly, different types of feature points of the original data are obtained through the feature extraction algorithm; secondly, the feature points are reparametrized to the locations of different knots in the V-system; finally, the fitted signal is obtained by solving the least-squares linear equations with constrains. Because the V-system has basis functions with different smoothness, the reconstructed signal can capture different features in the original data. Numerical experimental results show that the proposed method in this paper not only obtains a reconstructed signal reflecting the global shape of the original data, but also can accurately reproduce sharp features.
MUD-PQFed: Towards Malicious User Detection on model corruption in Privacy-preserving Quantized Federated learning

The use of cryptographic privacy-preserving techniques in Federated Learning (FL) inadvertently induces a security dilemma because tampered local model parameters are encrypted and thus prevented from auditing. This work firstly demonstrates the triviality of performing model corruption attacks against privacy-preserving FL. We consider the scenario where the model updates are quantized to reduce the communication overhead, whilst the adversary can simply provide local parameters out of a legitimate range to corrupt the model. We then propose MUD-PQFed, a protocol that can precisely detect malicious attacks and enforce fair penalties on malicious clients. By deleting the contributions from the detected malicious clients, the global model utility is preserved as compared to the baseline global model in the absence of the corruption attack. Extensive experiments on MNIST, CIFAR-10, and CelebA benchmark datasets validate the efficacy in terms of retaining the baseline accuracy and effectiveness in terms of detecting malicious clients in a fine-grained manner.
DFE-IP: Delegatable functional encryption for inner product

Functional encryption for inner product (FE-IP) allows an authorized user to obtain the inner product of the vectors embedded in his/her secret key and a ciphertext, respectively. Therefore, it is an elegant tool to implement secure computation over encrypted data. Existing FE-IP schemes mainly considered security and function privacy issues, but did not address the decryption delegation problem. However, in practice, delegating decryption is desirable when an authorized user is unavailable to access the system due to leaving, network problems, etc. In this paper, a delegatable FE-IP (DFE-IP) scheme is proposed to support decryption delegation. Our scheme provides the following important features: (1) a delegator can delegate his/her decryption power to a delegatee; (2) a proxy is not required to re-encrypt ciphertexts; (3) it supports temporary delegation and flexible revocation. We first formalize our DFE-IP scheme including definition and security model, and then describe a concrete construction. Furthermore, the implementation and evaluation are provided to show its efficiency. Finally, its security is formally proven. The novelty is to implement temporary decryption delegation in FE-IP when authorized users are unavailable to access the system.
Consistency-based self-supervised visual tracking by using query-communication transformer

Self-supervised learning (SSL) performs remarkably in visual tracking since it enables the extraction of general representations from unlabeled data and alleviates the need for expensive human annotations. SSL models usually achieve frame-to-frame communications during training by predicting each object location of intermediate frames, however, the possible prediction errors may accumulate and mislead the forward–backward tracking procedure. A novel query-communication transformer (QCT) architecture is proposed in this work to enable more reliable frame-to-frame communications via propagating query information, avoiding the above-mentioned tracking errors on intermediate frames tactfully. Specifically, we introduce the transformer into self-supervised tracking to handle the object template and search frames, i.e., the encoder encodes spatio-temporal context of template and search frames, while the decoder takes the query embedding of previous frame to retrieve the template object information from the encoder output. To further enhance the query embedding, a query interaction module is devised to promote information passing between frames. Moreover, we employ inter-frame correspondence and intra-frame correspondence to construct different views and transformations for better learning the representation from palindromic sequences. We validate our method on the seven challenging benchmarks. The results demonstrate considerable improvements over recent self-supervised algorithms and even some fully-supervised deep trackers.
A cross-lingual transfer learning method for online COVID-19-related hate speech detection

During the COVID-19 pandemic, online social media platforms such as Twitter facilitate the exchange of information among people. However, the prevalence of "infodemic" such as online hate speech has exacerbated social rifts, discrimination, prejudice and even hate crimes. Timely and effective detection of the hate speech will help create a healthy public opinion environment. Most of the current COVID-19-related hate speech research focuses on a single language, such as English. In this paper, we introduce a cross-lingual transfer learning method, aiming to contribute to hate speech detection in low-resource languages. We propose a deep learning based model to classify hate speech with a pre-trained language model for multilingual text embedding. Data augmentation and cross-lingual contrastive learning are then utilized to further improve the performance of cross-lingual knowledge transfer. To evaluate our method, we collected three publicly available annotated COVID-19-related hate speech datasets on Twitter, i.e., two in English and one in German. Furthermore, a Chinese dataset based on Weibo is constructed to expand multilingual data. The experimental results across three languages illustrate the effectiveness of our method for cross-lingual hate speech detection. Test F1-scores of our method for English, Chinese, German as transfer target languages can reach up to 0.728, 0.799 and 0.612 respectively, which are on average better than other baselines.
Improving recommendation diversity and serendipity with an ontology-based algorithm for cold start environments

Abstract Every real-life environments where users interact with items (products, films, research expert profiles) have several development phases. In the Cold-start phase, there are almost no interactions among users and items content-based recommendation systems (RS) can only recommend based on matching the attributes of the items. In the transition state, items start to collect user interactions but still a significant number of items have too small number of interactions, RS does not allow users to discover cold items. In a regular state, where most of the items in the system have enough interactions, the recommendations often suffer from low diversity of the items within a single recommendation. This article proposes a general recommendation algorithm based on Ontological-similarity, which is designed to address all the above problems. Our experiments show that recommendations generated by our approach are consistently better in all environment development phases and increase the success rate of recommendations by almost 50% measured using ontology-aware recall, which is also introduced in this article.
Optical flow-assisted multi-level fusion network for Light Field image angular reconstruction

Light Field (LF) imaging can record both the intensities and directions of light rays in a single exposure, which has received extensive attentions. However, the limited angular resolution becomes the primary bottleneck for the wide-spread applications of LF imaging. To this end, this paper proposes a novel optical flow-assisted multi-level fusion network for LF angular reconstruction. In our method, we propose to infer the multi-angular optical flows to explore long-range dependency of LF sub-aperture images (SAIs) for high-quality angular reconstruction. By aligning the SAIs in multi-angular directions, the geometric consistency of reconstructed dense LF can be preserved. Moreover, a multi-level fusion framework for LF angular reconstruction is introduced, which consists of two stages, namely texture-optical flow feature fusion and parallax structure-information fusion. The former firstly extracts the texture and optical flow features from the reconstructed coarse LF and then fuses these two features by using the proposed texture-optical flow fusion-block. The latter further blends the LF parallax structure information with the fused texture and optical flow features using the proposed parallax structure-information fusion network. Comprehensive experiments on both real-world and synthetic LF scenes demonstrate the superiority of the proposed method for reconstructing high-quality dense LF. Moreover, practical application on depth estimation also validates that our method can recover more texture details, particularly for some occlusion regions.
HABE: hybrid AdaBoost ensemble method of probabilistic matrix factorization recommendation system

The PMF model is effective for addressing high-dimensional, large-scale, sparse, and imbalanced rating data, yet it may suffer from limitations in generalization and prediction accuracy in certain scenarios. To address these limitations, we propose a hybrid AdaBoost ensemble method within the PMF model. In this paper, we use two-stage algorithms in the model. Our approach uses a two-stage algorithm, whereby the first stage involves fuzzy clustering to calculate the scoring matrix of user-items, followed by neural network training to further enhance scoring prediction accuracy. The second stage involves using the rating matrix as the basis learner for training by different neural networks, and the final score prediction result is obtained through ensemble learning. Our proposed model was evaluated on the MovieLens and FilmTrust datasets, and its effectiveness was demonstrated. Due to its well-crafted architecture and robust representation learning capability, our model can be readily applied to various PMF model settings, such as PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models. The experiments show that the mean absolute error(MAE) of the proposed method increases by 1.24% and 0.79% compared with the Bagging-BP-PMF model on two different datasets, and the root mean square error(RMSE) increases by 2.55% and 1.87%, respectively. Finally, our experiments show that our proposed approach performs well in various settings. By utilizing ensemble learning to train the weight of the base learner from different neural networks, our method improves the stability of score prediction. Additionally, our results verify the universality of our approach.
A multimodal dual-fusion entity extraction model for large and complex devices

In the context of large and complex devices with a multi-source heterogeneous data environment, the extraction of network device configuration related entity information from diverse modalities of the Internet of Things data is a crucial and fundamental step towards establishing a domain knowledge graph for global Zero Touch Provisioning of network devices. In this paper, we present a novel multimodal dual-fusion entity extraction model that serves as a foundation for an intelligent and efficient network device configuration process. Firstly, the multimodal data is encoded, followed by the ViLBERT pre-training model to obtain more feature information of entities for multimodal front-end fusion. Next, the attention weights of each modal feature are learned through a multilayer neural network classifier and probabilistic graphical model, facilitating multimodal back-end fusion and reducing information redundancy. Finally, entity recognition is accomplished by employing a cohesive memory module that extracts the essential parameters for device configuration. The simulation results demonstrate that the proposed model performs exceptionally well on the MSCOCO2017 public dataset and the SFZK-Dev data network device dataset, with F1 values of the comprehensive evaluation index of model quality at 94.65% and 96.94%, respectively, indicating high stability. Additionally, the link prediction metrics HITS@1 achieved accuracy levels of 58.21% and 68.04%.
Physiological and Subjective Measures of Anxiety with Repeated Exposure to Virtual Construction Sites at Different Heights

Occupational workers at altitudes are more prone to falls, leading to catastrophic outcomes. Acrophobia, height-related anxiety, and affected executive functions lead to postural instabilities, causing falls. This study investigated the effects of repeated virtual height exposure and training on cognitive processing and height-related anxiety.Twenty-eight healthy volunteers (age 20.48 ± 1.26 years; mass 69.52 ± 13.78 kg) were recruited and tested in seven virtual environments (VE) [ground (G), 2-story altitude (A1), 2-story edge (E1), 4-story altitude (A2), 4-story edge (E2), 6-story altitude (A3), and 6-story edge (E3)] over three days. At each VE, participants identified occupational hazards present in the VE and completed an Attitude Towards Heights Questionnaire (ATHQ) and a modified State-Trait Anxiety Inventory Questionnaire (mSTAIQ). The number of hazards identified and the ATHQ and mSTAIQ scores were analyzed using a 7 (VE; G, A1, A2, A3, E1, E2, E3) x 3 (DAY; DAY 1, DAY 2, DAY 3) factorial repeated measures analysis of variance.The participants identified the lowest number of hazards at A3 and E3 VEs and on DAY 1 compared to other VEs and DAYs. ATHQ scores were lowest at G, A1, and E1 VEs.Cognitive processing is negatively affected by virtual altitudes, while it improves with short-term training. The features of virtual reality, such as higher involvement, engagement, and reliability, make it a better training tool to be considered in ergonomic settings. The findings of this study will provide insights into cognitive dual-tasking at altitude and its challenges, which will aid in minimizing occupational falls.
Graph signal processing on dynamic graphs based on temporal-attention product

Signal processing is an important research topic. This paper aims to provide a general framework for signal processing on arbitrary dynamic graphs. We propose a new graph transformation by defining a temporal-attention product. This product transforms the sequence of graph time slices with arbitrary topology and number of nodes into a static graph, effectively capturing graph signals' spatio-temporal dynamic evolution process. The temporal-attention product graph provides a solid mathematical foundation to model the time-dependent graph signal processes as martingales. The weighted adjacency matrix obtained by temporal-attention products is a block tridiagonal matrix, which has been extensively studied. Therefore, it is general and convenient to perform graph signal processing on this new static graph. We apply two real datasets to illustrate the effectiveness of spectral graph wavelet transform based on temporal-attention product. For one of the datasets with no graph structure, we learn the graph weights through a neural network.
A data-driven approach for understanding invalid bug reports: An industrial case study

Bug reports created during software development and maintenance do not always describe deviations from a system’s valid behavior. Such invalid bug reports may consume significant resources and adversely affect the prioritization and resolution of valid bug reports. There is a need to identify preventive actions to reduce the inflow of invalid bug reports. Existing research has shown that manually analyzing invalid bug report descriptions provides cues regarding preventive actions. However, such a manual approach is not cost-effective due to the time required to analyze a sufficiently large number of bug reports needed to identify useful patterns. Furthermore, the analysis needs to be repeated as the underlying causes of invalid bug reports change over time. In this study, we propose and evaluate the use of Latent Dirichlet Allocation (LDA), a topic modeling approach, to support practitioners in suggesting preventive actions to avoid the creation of similar invalid bug reports in the future. In an industrial case study, we first manually analyzed descriptions of invalid bug reports to identify common patterns in their descriptions. We further investigated to what extent LDA can support this manual process. We used expert-based validation to evaluate the relevance of identified common patterns and their usefulness in suggesting preventive measures. We found that invalid bug reports have common patterns that are perceived as relevant, and they can be used to devise preventive measures. Furthermore, the identification of common patterns can be supported with automation. Using LDA, practitioners can effectively identify representative groups of bug reports (i.e., relevant common patterns) from a large number of bug reports and analyze them further to devise preventive measures.
Theoretical analysis and implementation of abstract argumentation frameworks with domain assignments

A representational limitation of current argumentation frameworks is their inability to deal with sets of entities and their properties, for example to express that an argument is applicable for a specific set of entities that have a certain property and not applicable for all the others. In order to address this limitation, we recently introduced Abstract Argumentation Frameworks with Domain Assignments (AAFDs), which extend Abstract Argumentation Frameworks (AAFs) by assigning to each argument a domain of application, i.e., a set of entities for which the argument is believed to apply. We provided formal definitions of AAFDs and their semantics, showed with examples how this model can support various features of commonsense and non-monotonic reasoning, and studied its relation to AAFs. In this paper, aiming to provide a deeper insight into this new model, we present more results on the relation between AAFDs and AAFs and the properties of the AAFD semantics, and we introduce an alternative, more expressive way to define the domains of arguments using logical predicates. We also offer an implementation of AAFDs based on Answer Set Programming (ASP) and evaluate it using a range of experiments with synthetic datasets.
Modelling temporal goals in runtime goal models

Achieving real-time agility and adaptation with respect to changing requirements in existing IT infrastructure can pose a complex challenge. We describe a goal-oriented approach to manage this complexity. We argue that a goal-oriented perspective can form an effective basis for devising and deploying responses to changed requirements at runtime. We offer an extended vocabulary of goal types by presenting two novel conceptions: differential goals and integral goals, which we formalize in both linear-time and branching-time settings. We describe goal lifecycles and interactions and the extended notion of context for the representation of rapidly changing, complex operating environments. We then illustrate the working of the approach by presenting a detailed scenario of adaptation in a Kubernetes setting, in the face of a Distributed Denial-of-Service (DDoS) attack.