Reviewerly/exHarmony huggingface dataset:
Guarding Against Universal Adversarial Perturbations in Data-driven Cloud/Edge Services

Although machine learning (ML)-based models are increasingly being used by cloud-based data-driven services, two key problems exist when used at the edge. First, the size and complexity of these models hampers their deployment at the edge, where heterogeneity of resource types and constraints on resources is the norm. Second, ML models are known to be vulnerable to adversarial perturbations. To address the edge deployment issue, model compression techniques, especially model quantization, have shown significant promise. However, the adversarial robustness of such quantized models remains mostly an open problem. To address this challenge, this paper investigates whether quantized models with different precision levels can be vulnerable to the same universal adversarial perturbation (UAP). Based on these insights, the paper then presents a cloud-native service that generates and distributes adversarially robust compressed models deployable at the edge using a novel, defensive post-training quantization approach. Experimental evaluations reveal that although quantized models are vulnerable to UAPs, post-training quantization on the synthesized, adversarially-trained models are effective against such UAPs. Furthermore, deployments on heterogeneous edge devices with flexible quantization settings are efficient thereby paving the way in realizing adversarially robust data-driven cloud/edge services.
Transforming Image Generation from Scene Graphs

Generating images from semantic visual knowledge is a challenging task, that can be useful to condition the synthesis process in complex, subtle, and unambiguous ways, compared to alternatives such as class labels or text descriptions. Although generative methods conditioned by semantic representations exist, they do not provide a way to control the generation process aside from the specification of constraints between objects. As an example, the possibility to iteratively generate or modify images by manually adding specific items is a desired property that, to our knowledge, has not been fully investigated in the literature. In this work we propose a transformer-based approach conditioned by scene graphs that, conversely to recent transformer-based methods, also employs a decoder to autoregressively compose images, making the synthesis process more effective and controllable. The proposed architecture is composed by three modules: 1) a graph convolutional network, to encode the relationships of the input graph; 2) an encoder-decoder transformer, which autoregressively composes the output image; 3) an auto-encoder, employed to generate representations used as input/output of each generation step by the transformer. Results obtained on CIFAR10 and MNIST images show that our model is able to satisfy semantic constraints defined by a scene graph and to model relations between visual objects in the scene by taking into account a user-provided partial rendering of the desired target.
Bilinear CNNs for Blind Quality Assessment of Fine-Grained Images

Most of the existing image quality assessment (IQA) studies focus on discriminable images, whose relative visual quality could be easily determined by human beings (we also call this issue coarse-grained (CG)-IQA). The effective models designed for CG-IQA struggle for quality assessment of the images with subtle differences (often exist in many real applications), which is also called fine-grained (FG) IQA problem. Thus, we make the first, to the best of our knowledge, attempt to build a novel blind IQA (BIQA) model for the images with FG distortion, aiming to fill the gap between objective IQA model and real applications. Specifically, the proposed model mainly consists of a feature extraction module (a sequence of convolution layers), a squeeze-and-excitation module, and a bilinear pooling module, whose objectives are extracting quality-aware features, enhancing features' representation ability, and discriminability. We conduct extensive experiments on a public FG-IQA database, and demonstrate the superiority of the proposed method and the effectiveness of each module.
An SM2-based Traceable Ring Signature Scheme for Smart Grid Privacy Protection

A smart grid can dynamically adjust the amount of electricity supply by smart meters’ personalized needs, reducing energy waste and protecting the environment. However, because the uploaded data could reveal users’ sensitive information, and internal adversaries could poison the power statistics, privacy preservation and security supervision in smart grid systems need to be concerned. To solve these, we propose a traceable ring signature scheme based on SM2 with strong security and anonymity, in addition to utilizing this scheme to build a four-layer smart grid model, separating the duty of statistics and regulations. Specifically, the scheme integrates the advantages of a key-insulated linkable ring signature (LRS) for Monero and an SM2-based ring signature: a key derivation mechanism to make the key more secure and a simple SM2-based ring structure. A trapdoor has been introduced in the “key image” of the signature, which is often used in LRS for linkability, but in our signature, it’s used for traceability. This allows authorized participants to open signatures and reveal the identity of the real signer when exceptions occur. Besides the security and privacy analyses, we also implement the proposed scheme and give some experiments to evaluate the time and space performance. The results show that our new scheme with space of kilobyte level size and time of linear or constant cost can be effectively adapted to the functional requirement of our smart grid model. In addition, the signature can be ported to wireless mobile devices for privacy protection and security management.
Slicer: Verifiable, Secure and Fair Search over Encrypted Numerical Data Using Blockchain

Verifiable Searchable Symmetric Encryption (SSE) enables reliable search over encrypted, privacy-preserving data on untrusted clouds. Most existing SSE designs only focus on keyword-file search. However, a more difficult but useful search, range search over encrypted numerical values remains unsolved. Moreover, the fairness of search in the mutual distrusted scenario without public verification, where data users may maliciously deny the results after the local result verification, is not well addressed yet. In this paper, we take the first step to study the public verification problem atop the blockchain for encrypted numerical search. We design a novel verifiable SSE scheme named Slicer based on a Succinct Order-Revealing Encryption (SORE) scheme to achieve range search on numerical data. Our search results are verifiable, updated and privacy-preserving by SSE and maintaining the forward security. We illustrate the security and practicality of our design through rigorous analysis and extensive evaluations respectively.
Social Media Use in Academic Libraries

Social media use has become the norm in information services delivery. It holds the unique advantage of delivering service to users through social interaction. This chapter discussed social media use in academic libraries in a developing country: Nigeria. It examined the concept and definition of social media; it also discussed trends in social media usage in libraries. The chapter further outlined the types of social media tools used in libraries. It discussed the peculiar challenges of social media use in Nigeria and proffered solutions.
Don't be a tattle-tale

We study the problem of answering queries when (part of) the data may be sensitive and should not be leaked to the querier. Simply restricting the computation to non-sensitive part of the data may leak sensitive data through inference based on data dependencies. While inference control from data dependencies during query processing has been studied in the literature, existing solution either detect and deny queries causing leakage, or use a weak security model that only protects against exact reconstruction of the sensitive data. In this paper, we adopt a stronger security model based on full deniability that prevents any information about sensitive data to be inferred from query answers. We identify conditions under which full deniability can be achieved and develop an efficient algorithm that minimally hides non-sensitive cells during query processing to achieve full deniability. We experimentally show that our approach is practical and scales to increasing proportion of sensitive data, as well as, to increasing database size.
Cross-Blockchain Transactions: Systems, Protocols, and Topological Theory

In this chapter, we turn our focus to those applications touching multiple blockchains. Since a blockchain deals with its data in the form of transactions, the real technical question we want to answer is how to handle cross-blockchain transactions (CBTs)? We will first present the state-of-the-art systems, i.e., exchanging cryptocurrencies between Ethereum and Bitcoin, and discuss the challenges of extending existing approaches to a more general context, such as application-specific blockchains (instead of cryptocurrencies) and an arbitrary number of blockchains (instead of two). We then review two recent schools of thought about CBT protocols and discuss their properties in detail. Finally, we sketch an ongoing research effort on building a theoretical foundation for CBTs using topological machinery.
Energy-Adaptive Real-time Sensing for Batteryless Devices

The use of batteryless energy harvesting devices has been recognized as a promising solution for their low maintenance requirements and ability to work in harsh environments. However, these devices have to harvest energy from ambient energy sources and execute real-time sensing tasks periodically while satisfying data freshness constraints, which is especially challenging as the energy sources are often unreliable and intermittent. In this paper, we develop an energy-adaptive real-time sensing framework for batteryless devices. This framework includes a lightweight machine learning-based energy predictor that is capable of running on microcontroller devices and predicting the energy availability and intensity based on energy traces. Using this, the framework adapts the schedule of real-time tasks by effectively taking into account the predicted energy supply and the resulting age of information of each task, in order to achieve continuous sensing operations and satisfy given data freshness requirements. We discuss various design choices for adaptive scheduling and evaluate their performance in the context of batteryless devices. Experimental results show that the proposed adaptive real-time approach outperforms the recent methods based on static and reactive approaches, in both energy utilization and data freshness.
Edge-Cloud Collaborative Computation Offloading for Federated Learning in Smart City

With the continuous transformation and development of information technologies, the smart city is becoming a promising paradigm to deal with the enormous network data. Among them, federated learning technology has emerged as a key tool for intelligent analysis and data processing. It effectively guarantees the privacy and security of users by shifting conventional data storage and model training to local devices. Nevertheless, strong convergence performance necessitates numerous rounds of data exchange, which is uneconomical for edge platforms with limited resources. In view of this, we study the offloading of federated learning models in the edge-cloud collaborative smart city. Firstly, we transform FL models into a helpful structure for improving the efficient aggregation of servers. Then, to lower the energy and time cost of mobile devices throughout the model transmission and aggregation process while maintaining a high resource utilization level of edge servers, we develop an efficient computational offloading mechanism. Finally, the experimental results demonstrate the efficiency of our proposed method.
Could Head Motions Affect Quality When Viewing 360° Videos?

Measuring quality accurately and quickly (preferably in real time) when streaming 360$^\circ$∘ videos is essential to enhance the user experience. Most quality-of-experience metrics have primarily used viewport quality as a simple surrogate for such experiences at a given time. While this baseline approach has been later augmented by some researchers using pupil and gaze tracking, head tracking has not been considered in enough detail. This article tackles whether head motions can influence the perception of 360$^\circ$∘ videos. Inspired by the latest research, this article conceptualizes a head-motion-aware metric for measuring viewport quality. A comparative study against existing head-motion-unaware metrics reveals sizeable differences. Motivated by this, we invite the community to research this topic further and substantiate the new metric’s validity.
Fuzzy Based Principal’s Leadership Skills Assessment Model for Secondary Schools

The present study suggests the use of a fuzzy inference system (FIS) model to assess the leadership skills of secondary school principals. To assess the principals’ leadership skills, teachers’ perceptions of these skills concerning various items are collected by using a five-point Likert scale. Further, these values are categorized into linguistic variables that are used as input in a fuzzy inference system to get an overall principal leadership skills score. The implementation of our model for a secondary school in Pakistan highlighted weaknesses in leadership skills, which may need suitable planning to enhance these skills. This FIS model acts as a device to help the policymakers in judging several components of leadership in those institutes in which they are serving. This study implies that school principals would be more effective if they possess leadership skills. The skills would enable them to turn schools into effective schools
Overview of Application in Data Mining Techniques to QAR Data Ansys

To ensure flight safety, the Civil Aviation Administration of China (CAAC) requires commercial aircraft to be fitted with a Quick Access Recorder (QAR), which continuously records the aircraft's raw flight data and captures thousands of flight parameters. Currently there is limited awareness and application of QAR data. With the continuous development of data analysis technology, data mining methods have been applied to QAR data processing, providing strong support and guarantee for aviation safety. This paper introduces the basic theory of data mining, reviews the applications of data mining in civil aviation operational safety, flight status assessment and identification, and general aviation respectively based on the clustering results of the literature in the past 12 years, and finally summarises the current problems faced by QAR data mining and the future development direction.
HEKWS: Privacy-Preserving Convolutional Neural Network-based Keyword Spotting with a Ciphertext Packing Technique

Keyword spotting (KWS) is a key technology in smart devices. However, privacy issues in these devices have been constantly raised. To solve this problem, this paper applies homomorphic encryption (HE) to a previous small-footprint convolutional neural network (CNN)-based KWS algorithm. This allows for a trustless system in which a command word can be securely identified by a remote cloud server without exposing client data. To alleviate the burden on an edge device of a client, a novel packing technique is proposed that reduces the number of ciphertexts for an input keyword to one. Our HE-based KWS shows a prediction accuracy of 72% for Google's Speech Commands Dataset with 12 labels. This is almost identical to the accuracy of the non-HE-based implementation that has the same CNN layers and approximates a rectified linear unit in the same manner. On a workstation, it takes 19 seconds to process one keyword on average, which can be improved in the future through parallelization, HE parameter optimization, and/or the use of custom hardware accelerators.
From the Experience of Working in the Remote Learning Mode

The article discusses from a broad point of view issues related to the methods and methods of distance education, as well as issues related to the teaching of mathematics in modern conditions. Both positive and negative factors associated with distance education are considered. In their judgments, the authors rely on the experience of working at the Faculty of Information Technologies of the Moscow State University of Psychology and Education.
Vectorial Bent-Negabent Functions—Their Constructions and Bounds

Boolean bent functions which at the same time have a flat nega-Hadamard transform are called bent-negabent functions. The known families of these functions mostly stem from the Maiorana-McFarland class of bent functions and their vectorial counterparts have not been considered in the literature. In this article, we introduce the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vectorial bent-negabent</i> functions and show that in general for a vectorial bent-negabent function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F\colon {\mathbb {F}} _{2}^{2m} \rightarrow {\mathbb {F}} _{2}^{k}$ </tex-math></inline-formula> we necessarily have that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k \leq m-1$ </tex-math></inline-formula> . We specify a class of vectorial bent-negabent functions of maximal output dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m-1$ </tex-math></inline-formula> by using a set of linear complete mappings. On the other hand, we propose several methods (one of which is generic) of specifying vector spaces of nonlinear complete mappings which then induce vectorial bent-negabent functions (whose dimension is not maximal) having a certain number of component functions outside the completed Maiorana-McFarland class. Finally, we derive an upper bound on the maximum number of bent-negabent components for mappings <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F\colon {\mathbb {F}} _{2}^{2m} \rightarrow {\mathbb {F}} _{2}^{k}$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m \leq k \leq 2m$ </tex-math></inline-formula> , and identify some families of these functions reaching this upper bound.
MOTCOM: The Multi-Object Tracking Dataset Complexity Metric

There exists no comprehensive metric for describing the complexity of Multi-Object Tracking (MOT) sequences. This lack of metrics decreases explainability, complicates comparison of datasets, and reduces the conversation on tracker performance to a matter of leader board position. As a remedy, we present the novel MOT dataset complexity metric (MOTCOM), which is a combination of three sub-metrics inspired by key problems in MOT: occlusion, erratic motion, and visual similarity. The insights of MOTCOM can open nuanced discussions on tracker performance and may lead to a wider acknowledgement of novel contributions developed for either less known datasets or those aimed at solving sub-problems. We evaluate MOTCOM on the comprehensive MOT17, MOT20, and MOTSynth datasets and show that MOTCOM is far better at describing the complexity of MOT sequences compared to the conventional density and number of tracks. Project page at https://vap.aau.dk/motcom .
Multidimensional Belief Quantification for Label-Efficient Meta-Learning

Optimization-based meta-learning offers a promising direction for few-shot learning that is essential for many real-world computer vision applications. However, learning from few samples introduces uncertainty, and quantifying model confidence for few-shot predictions is essential for many critical domains. Furthermore, few-shot tasks used in meta training are usually sampled randomly from a task distribution for an iterative model update, leading to high labeling costs and computational overhead in meta-training. We propose a novel uncertainty-aware task selection model for label efficient meta-learning. The proposed model formulates a multidimensional belief measure, which can quantify the known uncertainty and lower bound the unknown uncertainty of any given task. Our theoretical result establishes an important relationship between the conflicting belief and the incorrect belief The theoretical result allows us to estimate the total uncertainty of a task, which provides a principled criterion for task selection. A novel multi-query task formulation is further developed to improve both the computational and labeling efficiency of meta-learning. Experiments conducted over multiple real-world few-shot image classification tasks demonstrate the effectiveness of the proposed model.
PSU-Net: Paired Spatial U-Net for Hand Segmentation with Complex Backgrounds

Hand segmentation, as a key part of human-computer interaction, palmprint recognition and gesture recognition, is prone to interference from complex backgrounds subsequently resulting in poor segmentation accuracy. In this paper, a paired spatial U-Net (PSU-Net) hand image segmentation network is proposed. Firstly, we improve the traditional dilated pyramid pooling into the paired spatial pyramid pooling (PSPP) module. Through low-dimensional feature pairing, the PSPP can exploit the low-dimensional feature information, thus enhancing the network's ability to capture edge detail information. Then we design the global attention fusion module (GAF), which can efficiently combine low-dimensional spatial details and high-dimensional semantic information to solve blurred edges in complex backgrounds. Some experimental results on HOF, GTEA and Egohands databases show the proposed approach has quite good performance. The mIOU of PSU-Net can achieve 76.19% on HOF dataset, while the mIOU of DeeplabV3 is 74.45%.
Classification efficiency of LassoNet model in image recognition

LassoNet is a neural network framework proposed by Robert Tibshirani et al. and published in the "Journal of Machine Learning Research" in 2021. The model generalizes the existing Lasso regression and its feature sparsity to a feedforward neural network, and performs feature selection and parameter learning at the same time under the premise of unknown optimal number of selected features. In order to verify whether the classification efficiency of LassoNet is efficient, LassoNet is first compared with four shallow learning methods (logistic regression, Fisher linear discriminant, random forest and support vector machine) and three deep learning methods (CNN, Inception and Residual Module), respectively. For the classification of high-dimensional and large-sample datasets in five different fields, the experimental results show that LassoNet has a significant classification effect, which is significantly better than the general shallow learning method, and is comparable to the deep learning method. It can be seen that LassoNet has strong versatility and It is easy to use, but it takes a lot of time to run. In the follow-up work, the feedforward neural network can be optimized or replaced to further improve the classification efficiency.