Reviewerly/exHarmony huggingface dataset:
Enhancing conversational recommender systems via multi-level knowledge modeling with semantic relations

Augmenting conversational recommendation systems (CRS) with prior knowledge is crucial for learning user preferences and understanding contextual semantics. Existing methods are mainly based on knowledge graphs (KG), which rely on relationship information between entities, ignoring the semantic relationship in the dialogue. In addition, due to the limited information reflected by structured knowledge in KG, they still need broader external knowledge support to understand contextual semantics. To address the above issues, we propose a knowledge prompt-based CRS named MKCRS, which utilizes semantic relations to model multi-level knowledge and integrate it into CRS sub-tasks. Specifically, we first derive multi-level knowledge representations related to the current dialogue from external knowledge and then perform deep explicit and implicit modeling to construct knowledge prompts. To implement this framework, we design a semantic fusion model based on named entity phrases and a unified knowledge prompt generator. The former exploits explicit semantic relations between entities and contexts to enhance knowledge representations, while the latter adaptively learns the most important knowledge prompts via implicit connections between knowledge. Finally, based on the generated knowledge prompts, we construct knowledge-enhanced prompt templates for recommendation and dialogue generation tasks. Experimental results on the two datasets demonstrate that MKCRS outperforms the state-of-the-art models.
Learning to complement: Relation complementation network for few-shot class-incremental learning

Real-world industrial scenarios pose a challenging task known as few-shot class-incremental learning (FSCIL), which aims to recognize new classes using a few samples while not forgetting the old classes. Despite the recent advance of FSCIL, most existing methods rely on a single metric for making incremental relation predictions, which is unilateral and lacks stability. In this paper, we remedy this issue from two aspects. Specifically, to make convincing relation predictions, we first propose a relation complementation strategy that aggregates different metric models to investigate the comprehensive relation of classifier weights and test features. Then, to make the proposed strategy well fit the incremental scenarios, we design a pseudo incremental relation complementation learning scheme that constructs the learning tasks by mimicking the data setting in real incremental sessions. Taken together, our proposed method dubbed Relation Complementation Network (RCN) achieves the state-of-the-art performance on miniImageNet, CIFAR100 and CUB200. Our code is available at https://github.com/YeZiLaiXi/KT-RCN.git.
Hybrid technique for crop image compression using discrete wavelet transform and sparse singular vector reconstruction

In this paper, a hybrid compression technique, based on discrete wavelet transform (DWT) and singular vector sparse reconstruction (SVSR), is proposed. In the initial phase, an original image has been divided into four sub-band by using the DWT technique. After that, SVSR is utilized for boosting the compression ratio and to improve reconstruction using piecewise cubic Hermite interpolation (PCHIP). Inverse discrete wavelet transform (IDWT) process has been applied to obtain a compressed crop image. The first time, this developed technique has been applied for compression. The performance of proposed hybrid techniques has been assessed in terms of several evaluation parameters, which are compression ratio (CR), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean square error (MSE) on ten crop image data sets. It is observed from the results that the proposed technique is quite effective in compression of different types of crop images. Further, the experimental results show that the proposed hybrid method achieved significantly higher compression over the existing techniques. The proposed technique with Haar wavelet provides 412.5% and 94.97% higher CR as compared to Singular Value Decomposition (SVD), and SVSR respectively with desirable image quality. Further, other wavelets Db4 provide 396.48% and 88.88% and Bior3.5 also provides 306.74% and 54.74% higher CR as compared to SVD, and SVSR respectively with similar image quality.
Exploring the potential of channel interactions for image restoration

Image restoration aims to reconstruct a clear image from a degraded observation. Convolutional neural networks have achieved promising performance on this task. The usage of Transformer has recently made significant advancements in state-of-the-art performance by modeling long-range dependencies. However, these deep architectures primarily concentrate on enhancing representation learning for the spatial dimension, neglecting the significance of channel interactions. In this paper, we explore the potential of channel interactions for restoring images through our proposal of a dual-domain channel attention mechanism. To be specific, channel attention in the spatial domain allows each channel to amass valuable signals from adjacent channels under the guidance of learned dynamic weights. In order to effectively exploit the significant difference in infrequency between degraded and clean image pairs, we develop the implicit frequency domain channel attention to facilitate the integration of information from different frequencies. Extensive experiments demonstrate that the proposed network, dubbed ChaIR, achieves state-of-the-art performance on 13 benchmark datasets for five image restoration tasks, including image dehazing, image motion/defocus deblurring, image desnowing, and image deraining.
An efficient searchable scheme for encrypted high dimensional data

The growing prominence and progress of cloud computing has prompted data owners to delegate data management to cloud servers, leading to substantial cost reduction and enhanced accessibility. To address privacy concerns, sensitive data must be encrypted before outsourcing, which impairs usability and renders traditional plaintext-based keyword search techniques obsolete. Several search schemes based on encrypted data have been proposed. However, most of these schemes use inverted indexes and tree indexes, resulting in a linear correlation between the index size and the size of the keyword dictionary, which is insufficient in terms of search efficiency. In this paper, we propose a secure multi-keyword ranking search scheme based on Singular Value Decomposition technology to provide more precise and efficient results for secure multi-keyword ranking searches. Experimental results show that our scheme can save at least 50% of the time compared to the traditional indexing scheme.
Towards an Activity-aware Pufferfish Framework for Local Privacy of Household Smart Water Meter Data

Data from smart water meters can provide valuable insights but it also exposes sensitive information about the behaviour of individuals. This short paper considers local privacy protection for allowing households to selectively hide secrets about their behaviours, as well as sharing their data for analysis. A single water meter signal can be disaggregated into activity channels, with each channel reporting the times and volume of specific activities such as garden irrigation or absences. Adversaries are assumed to have common knowledge of the distinctive distributions of such activities including their likely times of day, temporal- and auto-correlations, and seasonal patterns. Pufferfish privacy is a general purpose framework for secrets to be protected, with mechanisms for protecting those secrets given an adversary's prior knowledge of the generating mechanisms for the data. In this short paper we propose a Pufferfish activity-aware privacy protection framework for water meter data. Using a real-world dataset of hourly water meter readings for 3557 households, we demonstrate how to hide secrets about the times of leaks, absences and garden irrigation whilst preserving utility for accurate analysis of the data of individuals and populations.
Mitigating Privacy Leakage in Anomalous Building Data Streams

The presence of anomalies in building datasets represents a unique privacy challenge. There is a risk of successful identification within the complex and voluminous data streams for a building's occupancy, CO2, temperature, humidity and power consumption. This can leak patterns of usage and other derived information that adversaries can leverage for both cyber and real-world attacks. However, this issue needs to be weighed against the indispensable roles that Building Management Systems (BMS) can play in reducing power consumption and, thus, the emissions for commercial buildings. Our initial work attempts to balance these competing demands, by implementing a decentralized Internet of Things (IoT) architecture against the Data Clearing House (DCH), an established repository housing multitudes of live building data streams. The aim is to detect and smooth the presence of anomalies, which would limit the exposure of sensitive information before it reaches the cloud for further analysis. To this end, we began by analyzing the historical streams for CO2, occupancy and temperature for a selected building within DCH. We then applied a fast and lightweight anomaly detection method using the PyOD python library. A privacy-preserving architecture is then described, where a well-known Differential Privacy (DP) technique was also applied and studied.
Decomposition-based Data Augmentation for Time-series Building Load Data

Building load data, i.e., building electricity demands, are important for many downstream applications such as load forecasting, demand response, and others. Recent applications, in particularly, those based on machine learning models, require a large amounts of data. Unfortunately, many buildings do not have sufficient data. To augment data, recent schemes are relying on generative adversarial networks (GANs). GAN-based schemes can generate new samples for the same distribution, i.e., to enrich data diversity. However, they are not suitable for augmenting the data with insufficient data distributions, e.g., a data shortage caused by insufficient time coverage, a common problem for new buildings. This paper aims to address this problem.
A hybrid quantum annealing method for generating ensemble classifiers

Quantum annealing has been widely used to optimize machine learning such as ensemble classifiers. This ensemble classifier enhances classification performance through the combination of multiple accurate and diverse base classifiers. It benefits from the "perturb and combine" strategy which involves using the random subspace as the prevalent approach to perturb input data. There are several methods to generate a random subspace which include bagging, boosting, clustering, and, more recently, clustering balancing. The main contribution of this research was to improve the accuracy of the ensemble classifier using the hybrid quantum annealing method. The process involved making larger, stronger, and more balanced clusters through 1) the modification of the range of values from K in incremental clustering, 2) the adjustment of the clusters to be stronger and more balanced, and 3) the optimization of pure-class clusters using quantum annealing. The proposed method was tested and evaluated on 10 benchmark datasets from the UCI repository and the results were compared with current approaches. The results showed that the proposed method has better accuracy than the others due to the larger, stronger, and more balanced clusters generated as well as the better trade-off between accuracy and diversity.
DAFNet: Dual-branch Atrous fusion enhanced network

Semantic segmentation is one of the crucial research directions of computer vision. The accuracy, fine-grained, and speed of segmentation results restrict the practical application of semantic segmentation. In order to take full advantage of the spatial information in the high-level semantic features and the global vision in the low-level semantics features. This paper proposes a novel network structure framework DAFNet based on the pyramid structure. In view of the poor segmentation effect of PSPNet on the original basis, by designing a dual-path structure, adding an atrous space enhancement module and a cross-feature fusion module, DAFNet has improved the segmentation accuracy of the original network. In addition, in order to have better segmentation speed and make the model have a lighter application scenario range, this paper utilizes Mobilenetv2 as the backbone network. Experimental results show that DAFNet has achieved satisfactory results on the Pascal VOC 2012 val dataset.
Applications of Artificial Intelligence in Public Procurement—Case Study of Nigeria

Governments worldwide are exploring the applications of artificial intelligence (AI) technologies to improve public services. Organizations may utilize AI, machine learning, deep learning, and big data together to acquire relevant business insights, increase efficiencies, make better decisions, and advance their objectives. Governments worldwide are rapidly developing and deploying artificial intelligence derived from machine learning to improve operations, public services, compliance, and security activities. Government procurement in Nigeria has had several issues, including corruption. Corruption from both government officials and contractors has been an issue of significant concern. This chapter looks at how several government agencies in developed countries around the world are using artificial intelligence (AI) in their contracting procedures and some of the capabilities that are being considered for future use. This study looks at ways in which the Nigerian government could implement AI in its contracting to maximize efficiency and reduce the rate of corruption. AI has the potential to profoundly change how government agencies handle contracting, from vendor selection through contract closeout and everything in between. However, AI is not error-proof, and issues regarding technology implementation are also reviewed in this chapter.
Embracing ambiguity: Improving similarity-oriented tasks with contextual synonym knowledge

Contextual synonym knowledge is crucial for those similarity-oriented tasks whose core challenge lies in capturing semantic similarity between entities in their contexts, such as entity linking and entity matching. However, most Pre-trained Language Models (PLMs) lack synonym knowledge due to inherent limitations of their pre-training objectives such as masked language modeling (MLM). Existing works which inject synonym knowledge into PLMs often suffer from two severe problems: (i) Neglecting the ambiguity of synonyms, and (ii) Undermining semantic understanding of original PLMs, which is caused by inconsistency between the exact semantic similarity of the synonyms and the broad conceptual relevance learned from the original corpus. To address these issues, we propose PICSO, a flexible framework that supports the injection of contextual synonym knowledge from multiple domains into PLMs via a novel entity-aware Adapter which focuses on the semantics of the entities (synonyms) in the contexts. Meanwhile, PICSO stores the synonym knowledge in additional parameters of the Adapter structure, which prevents it from corrupting the semantic understanding of the original PLM. Extensive experiments demonstrate that PICSO can dramatically outperform the original PLMs and the other knowledge and synonym injection models on four different similarity-oriented tasks. In addition, experiments on GLUE prove that PICSO also benefits general natural language understanding tasks. Codes and data will be public.
Seeking empathy or suggesting a solution? Effects of chatbot messages on service failure recovery

Abstract Chatbots as prominent form of conversational agents are increasingly implemented as a user interface for digital customer-firm interactions on digital platforms and electronic markets, but they often fail to deliver suitable responses to user requests. In turn, individuals are left dissatisfied and turn away from chatbots, which harms successful chatbot implementation and ultimately firm’s service performance. Based on the stereotype content model, this paper explores the impact of two universally usable failure recovery messages as a strategy to preserve users’ post-recovery satisfaction and chatbot re-use intentions. Results of three experiments show that chatbot recovery messages have a positive effect on recovery responses, mediated by different elicited social cognitions. In particular, a solution-oriented message elicits stronger competence evaluations, whereas an empathy-seeking message leads to stronger warmth evaluations. The preference for one of these message types over the other depends on failure attribution and failure frequency. This study provides meaningful insights for chatbot technology developers and marketers seeking to understand and improve customer experience with digital conversational agents in a cost-effective way.
Sourcing albedo data for bifacial PV systems in complex landscapes

Bifacial photovoltaic (PV) systems harvest additional light reflected from the ground. The proportion of light reflected from the ground (albedo) is a critical parameter in estimating the expected performance of these systems. It is important that albedo data are available at the system design stage are accurate and relevant, and that the confidence in these data can be quantified. This work investigates the challenges of different albedo data sources and proposes solutions towards more reliable datasets. Satellite sources, such as the NASA Moderate Resolution Imaging Spectrometer (MODIS) products, provide data that can be used to model effective albedo anywhere in the world. Nevertheless, there are numerous sources of error that can add uncertainty in bifacial gain, reducing confidence and increasing financial risk. For smaller sites (<∼10 MW) located in varied landscapes, the scaling error caused by the low spatial resolution can be large. By analysing case studies in complex agricultural landscapes, we investigate the accuracy of different albedo measurement approaches. Monitoring stations with reference cells and pyranometers were deployed to validate satellite data and to realise a site measurement campaign for albedo measurements. Since these sites are much smaller than a MODIS pixel, we have evaluated alternative approaches, including “typical landscapes” with adjustments for local latitude and weather, and a novel method of combining MODIS data with higher resolution satellite data. The latter offers a practical solution with good agreement with the ground-based station.
Knowledge graph completion method based on hyperbolic representation learning and contrastive learning

Knowledge graph completion employs existing triples to deduce missing data, thereby enriching and enhancing graph completeness. Recent research has revealed that using hyperbolic representation learning in knowledge graph completion yields superior expressive and generalization capabilities. However, the long-tail problem and the presence of hyperbolic metrics make it challenging to effectively learn low-frequency entities or relations, resulting in embedding space distortion and impacting the original semantic relationships. Therefore, this paper proposes a knowledge graph completion method (Att-CL) that integrates hyperbolic representation learning and contrastive learning. In this approach, knowledge is embedded into a hyperbolic space, and samples with limited hierarchical characteristics and insufficient feature information are enhanced by introducing adversarial noise. The loss function of the embedded samples is backpropagated into embedding vectors, perturbations are adjusted in the gradient direction to promote smoothness and locality, and hyperparameters are introduced for fine-tuning the adversarial strength in the construction of adversarial samples for data augmentation to enhance model robustness. To mitigate data distortion due to hyperbolic metrics, a penalty term is introduced in the contrastive loss function to control the distances of the embedding vectors from the origin, thereby reducing the impact of the metrics and further improving the model's completion ability. Experimental results on the WN18RR and FB15K-237 benchmark datasets demonstrate significant improvements in metrics such as MRR, Hits@1, and Hits@3 compared to traditional knowledge graph completion models, providing ample evidence of the model's effectiveness.
CVFL: A Chain-like and Verifiable Federated Learning Scheme with Computational Efficiency Based on Lagrange Interpolation Functions

Data privacy and security concerns have attracted significant attention, leading to the frequent occurrence of data silos in deep learning. To address this issue, federated learning (FL) has emerged. However, simple federated learning frameworks still face two security risks during the training process. Firstly, sharing local gradients instead of private datasets among users does not completely eliminate the possibility of data leakage. Secondly, malicious servers could obtain inaccurate aggregation parameters by forging or simplifying the aggregation process, ultimately leading to model training failures. To address these issues and achieve high-performance training models, we have designed a verifiable federated learning scheme called CVFL, where users exist in a serial manner to resist inference attacks and further protect the privacy of user dataset information through serial encryption. We ensure the secure aggregation of models through a verification protocol based on Lagrange interpolation functions. The serial transmission of local gradients effectively reduces the communication burden on cloud servers, and our verification protocol avoids the computational overhead caused by a large number of encryption and decryption operations without sacrificing model accuracy. Experimental results on the MNIST dataset demonstrate that, after 10 epochs of training with 100 users, our solution achieves a model accuracy of 90.63% for MLP architecture under IID data distribution and 87.47% under non-IID data distribution. For CNN architecture, our solution achieves a model accuracy of 96.72% under IID data distribution and 93.53% under non-IID data distribution. Experimental evaluations corroborate the practical performance of the presented scheme with high accuracy and efficiency.
Image-Synthesis-Based Backdoor Attack Approach for Face Classification Task

Although deep neural networks (DNNs) are applied in various fields owing to their remarkable performance, recent studies have indicated that DNN models are vulnerable to backdoor attacks. Backdoored images were generated by adding a backdoor trigger in original training images, which activated the backdoor attack. However, most of the previously used attack methods are noticeable, not natural to the human eye, and easily detected by certain defense methods. Accordingly, we propose an image-synthesis-based backdoor attack, which is a novel approach to avoid this type of attack. To overcome the aforementioned limitations, we set a conditional facial region such as the hair, eyes, or mouth as a trigger and modified that region using an image synthesis technique that replaced the region of original image with the region of target image. Consequently, we achieved an attack success rate of up to 88.37% using 20% of the synthesized backdoored images injected in the training dataset while maintaining the model accuracy for clean images. Moreover, we analyzed the advantages of the proposed approach through image transformation, visualization of activation regions for DNN models, and human tests. In addition to its applicability in both label flipping and clean-label attack scenarios, the proposed method can be utilized as an attack approach to threaten security in the face classification task.