Reviewerly/exHarmony huggingface dataset:
NTRU-Based Fully Homomorphic Signature

Although the fully homomorphic signature (FHS) has made great progress, the low efficiency of existing FHS schemes as one main drawback cannot reach the requirements in practical application. It is well known that Number-Theory-Research-Unit (NTRU)-based cryptography is not only considered to be resistant to the quantum computer but also has high efficiency compared with the standard lattice-based cryptographic systems. In this paper, our goal is to construct a simple and highly efficient FHS scheme. To this end, we first devise an NTRU-based homomorphic trapdoor function (HTDF), which is more effective than the one proposed by Gorbunov et al. (STOC 2015). We then convert it into an efficient NTRU-based FHS scheme. Additionally, the HTDF we constructed is not only collision-resistant but is also claw-free, resulting in our FHS scheme is being strongly unforgeable.
Privacy Enhancing and Scalable Federated Learning to Accelerate AI Implementation in Cross-Silo and IoMT Environments

Federated Learning (FL) is a machine learning technique that enables to collaboratively learn valuable information across devices or sites without moving the data. In FL, the model is trained and shared across decentralized locations where data are privately owned. After local training, model updates are sent back to a central server, thus enabling access to distributed data on a large scale while maintaining privacy, security, and data access rights. Although FL is a well-studied topic, existing frameworks are still at an early stage of development. They encounter challenges with respect to scalability, data security, aggregation methodologies, data provenance, and production readiness. In this paper, we propose a novel FL framework that supports functionalities like scalable processing with respect of data, devices, sites and collaborators, monitoring services, privacy, and support for use cases. Furthermore, we integrate multi party computation (MPC) within the FL setup, preventing reverse engineering attacks. The proposed framework has been evaluated in diverse use cases both in cross-device and cross-silo settings. In the former case, in-device FL is leveraged in the context of an AI-driven internet of medical things (IoMT) environment. We demonstrate the framework suitability for a range of AI techniques while benchmarking with conventional centralized training. Furthermore, we prove the feasibility of developing a user-friendly pipeline that enables an efficient implementation of FL in diverse clinical use cases.
Identification of Multilingual Offense and Troll from Social Media Memes Using Weighted Ensemble of Multimodal Features

In recent years, memes have become a common medium of promulgating offensive views by the content polluters in social media. Due to their multimodal nature, memes can easily evade the content regulators’ eyes. The proliferation of these undesired or harmful memes can cause a detrimental impact on social harmony. Therefore, restraining offensive memes on social media is of utmost importance. However, analyzing memes is very complicated as they implicitly express human emotions. Previous studies have not explored the joint modelling of multimodal features and their counteractive unimodal features (i.e., image, text) to classify undesired memes. This paper presents a framework that utilizes the weighted ensemble technique to assign weights to the participating visual, textual and multimodal models. The state-of-the-art visual (i.e., VGG19, VGG16, ResNet50) and textual (i.e., multilingual-BERT, multilingual-DistilBERT, XLM-R) models are employed to make the constituent modules of the framework. Moreover, two fusion approaches (i.e., early fusion and late fusion) are used to combine the visual and textual features for developing the multimodal models. The evaluations have demonstrated that the proposed weighted ensemble technique improves the performance over the investigated unimodal, multimodal, and ensemble models. The result shows that the proposed approach achieves superior outcomes on two multilingual benchmark datasets (MultiOFF and TamilMemes), with 66.73% and 58.59% weighted f1-scores, respectively. Furthermore, the comparative analysis reveals that the proposed approach outdoes other existing works by improving approximately 13% and 2% weighted f1-score gain.
Fast algorithms for core maximization on large graphs

Core maximization, that enlarges the k -core as much as possible by inserting a few new edges into a graph, is particularly useful for social group engagement and network stability improvement. However, the core maximization problem has been theoretically proven to be NP-hard even APX-hard for k ≥ 3. Existing heuristic approaches suffer from the limitation of inefficiency on large graphs. To address this limitation, in this paper, we revisit this challenging yet important problem of core maximization, that is, given a graph G , a number k , and a budget b , to insert b new edges into G such that the corresponding k -core is maximized. We propose a novel algorithm FastCM+ based on several fast search strategies. The core idea is to apply graph partition to divide ( k - 1)-shell into different components. Then, FastCM+ considers each ( k - 1)-shell component independently to convert different layered vertices into k -core, in two manners of completely and partially. Based on the complete/partial conversions, FastCM+ is generalized to further handle ( k - λ)-shell conversions for 2 ≤λ k . Leveraging dynamic programming combinations of different components' potential answers, FastCM+ finds a good-quality answer for edge insertions. Experimental results on eleven datasets demonstrate that our algorithm runs much faster than state-of-the-art methods on large graphs meanwhile achieving better answers.
A Website Fingerprint defense technology with low delay and controllable bandwidth

Website Fingerprinting (WF) attacks have shown a serious threat to users' privacy on Tor networks. To resist WF attacks, many defenses have been proposed, of which padding without any delay is the state-of-the-art method. With the extra data overhead, they managed to reduce the accuracy of WF attacks. However, it is challenging to defeat WF attacks effectively while costing low bandwidth and latency. In this paper, we present RanDePad, a traffic protection and obfuscation method. It achieves low delay and controllable bandwidth. RanDePad uses an adaptive random delaying technique to destroy the website traffic's time distribution characteristics. It could adjust the time interval between the real traffic packets without changing its order and limits the required traffic latency. Through a bandwidth evaluation algorithm, RanDePad evaluates the traffic bandwidth and dynamically adjusts the random bandwidth padding scheme to hide traffic space features. It ensures low and controllable bandwidth overhead. We evaluated the performance of RanDePad against WF attacks under different bandwidth overhead settings. Experimental results show that with the same total bandwidth overhead and 5.89% total latency overhead, RanDePad can decrease the state-of-the-art attack DF accuracy from 94.57% to 62.40%, while the Front reduces it to 71.01% and the WTF-PAD to 86.44%. Furthermore, when the accuracy is defended to 70%, our method consumes only 21.66% total bandwidth overhead, which is half of the Front and WTF-PAD. In addition, the single bandwidth overhead of RanDePad is also lower than the existing defenses, where the Font spends three times as much as RanDePad does. The results indicate that the RanDePad performs better than the current state-of-the-art WTF-PAD and FRONT methods.
Code-Bothy: Mixed reality and craft sustainability

Code-Bothy examines traditional bricklaying using mixed reality technology. Digital design demands a re-examination of how we make. The digital and the manual should not be considered as autonomous but as part of something more reciprocal. One can engage with digital modelling software or can reject all digital tools and make and design by hand, but can we work in between? In the context of mixed-reality fabrication, the real and virtual worlds come together to create a hybrid environment where physical and digital objects are visualised simultaneously and interact with one another in real time. Hybridity of the two is compelling because the digital is often perceived as the future/emergent and the manual as the past/obsolescent. The practice of being digital and manual is on the one hand procedural and systematic, on the other textural and indexical. Working digitally and manually is about exploring areas in design and making: manual production and digital input can work together to allow for the conservation of crafts, while digital fabrication can be advanced with the help of manual craftsmanship.
Quantum Extreme Reservoir Computation Utilizing Scale-Free Networks

Today's quantum processors composed of fifty or more qubits have allowed us to enter a computational era where the output results are not easily simulatable on the world's biggest supercomputers. What we have not seen yet, however, is whether or not the complexity achievable with such quantum processors would ever be useful for any practical applications. In the quantum neural network approach, there has been an expectation that the Hilbert space can serve as a computational resource by providing a large feature space; however, it is not understood, nor illustrated in what way the Hilbert space provides that computational power. In this work we introduce a resource-efficient quantum neural network model with a focus on the role of the feature space the quantum processors can give. Then we apply it for classification tasks, showing a quantum advantage in terms of the physical resources needed to realize the neural network. To facilitate the Hilbert space as a large feature space, we utilize scale-free networks that can be generated in a discrete-time crystal model. The virtue of this approach is in both its theoretical simplicity and its practical applicability: our quantum neural network model is simple enough to identify the role of the Hilbert space as the feature space, and, furthermore, can be implemented with current technology. Our model does not require optimization of the quantum processor, and hence once the quantum processor has been set, it can be used for other different classification problems without any changes in the quantum processor itself.
Learning to Explore Distillability and Sparsability: A Joint Framework for Model Compression

Deep learning shows excellent performance usually at the expense of heavy computation. Recently, model compression has become a popular way of reducing the computation. Compression can be achieved using knowledge distillation or filter pruning. Knowledge distillation improves the accuracy of a lightweight network, while filter pruning removes redundant architecture in a cumbersome network. They are two different ways of achieving model compression, but few methods simultaneously consider both of them. In this paper, we revisit model compression and define two attributes of a model: distillability and sparsability, which reflect how much useful knowledge can be distilled and how many pruned ratios can be obtained, respectively. Guided by our observations and considering both accuracy and model size, a dynamically distillability-and-sparsability learning framework (DDSL) is introduced for model compression. DDSL consists of teacher, student and dean. Knowledge is distilled from the teacher to guide the student. The dean controls the training process by dynamically adjusting the distillation supervision and the sparsity supervision in a meta-learning framework. An alternating direction method of multiplier (ADMM)-based knowledge distillation-with-pruning (KDP) joint optimization algorithm is proposed to train the model. Extensive experimental results show that DDSL outperforms 24 state-of-the-art methods, including both knowledge distillation and filter pruning methods.
Multi-view multi-label learning with view feature attention allocation

In multi-view multi-label learning, instances can be described by a variety of view features, and they are also associated with a set of labels. Most of the existing multi-view multi-label learning methods extract common and private features from views. However, these methods ignore the fact that the extracted features have different importance for multi-label prediction. To address this issue, this paper proposes a multi-view multi-label learning framework with view feature attention allocation. First, the common and private features between different views are obtained. Then, the original features are compared with the common features to obtain the similarity. Next, the attention weight matrix can be obtained by multiplying the similarity with the synergistic feature matrix. Finally, the acquired attention is used to reconstruct the synergistic feature matrix that indicates the semantic information of the view for multi-label prediction. To verify the effectiveness of the proposed algorithm, experiments are conducted on seven multi-view multi-label datasets, and five advanced algorithms are taken for performance comparison. The extensive experimental results demonstrate the superiority of our algorithm.
Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Computer aided synthesis planning, suggesting synthetic routes for molecules of interest, is a rapidly growing field. The machine learning methods used are often dependent on access to large datasets for training, but finite experimental budgets limit how much data can be obtained from experiments. This suggests the use of schemes for data collection such as active learning, which identifies the data points of highest impact for model accuracy, and which has been used in recent studies with success. However, little has been done to explore the robustness of the methods predicting reaction yield when used together with active learning to reduce the amount of experimental data needed for training. This study aims to investigate the influence of machine learning algorithms and the number of initial data points on reaction yield prediction for two public high-throughput experimentation datasets. Our results show that active learning based on output margin reached a pre-defined AUROC faster than random sampling on both datasets. Analysis of feature importance of the trained machine learning models suggests active learning had a larger influence on the model accuracy when only a few features were important for the model prediction.
Vehicle Control System Based on Dynamic Traffic Gesture Recognition

In view of the lack of information processing speed and road condition recognition ability suitable for complex road conditions in the current autonomous driving technology and to solve the problem of rapid and accurate dynamic recognition of “traffic police gestures” in the current unmanned driving technology, a real-time interactive vehicle control system combining hardware and software is designed in this paper. The car takes Arduino UNO micro-controller as the core, realizes one-way data transmission from the camera to the computer through RTSP. After being processed by gesture recognition model based on YOLOv5 network structure, the corresponding instructions are sent back to the car to control the car’s movement. Through the design and debugging, the information interaction between the camera, the computer and the car is completed. The car can move according to the designated traffic police gesture, which improves the degree of car automation and has certain significance to promote the further development of the auto automation industry.
CNN-Based Attack Defense for Device-Free Localization

Device-free localization technology aims to find a target by analyzing the signal strength difference between transmitter and receiver deployed in the target area in advance. Up to now, device-free localization technology has been applied to a wide range of applications and scenarios, such as intrusion detection, environment modeling, and activity recognition. However, some sensors remain at potential risk that signal strength values of sensors have been tampered, or even devices sensors are physically damaged, which leads to inaccurate location results or a whole system crash. To solve the abovementioned problems, we design a CNN-based attack defense method for device-free localization, which can discover falsified signal strength values and error-prone devices. Firstly, we simulate a partial sensor attack or dropout in the device-free localization scenario. Then, we transform the localization problem into an image classification problem and use the convolutional neural networks (CNN) technique for abnormal detection. The experiment result shows that our algorithm can maintain high localization accuracy even under most sensor compromised and disconnected circumstances.
The Recommendation System of Innovation and Entrepreneurship Education Resources in Universities Based on Improved Collaborative Filtering Model

In the huge number of online university education resources, it is difficult for learners to quickly locate the resources they need, which leads to “information trek.” Traditional information recommendation methods tend to ignore the characteristics of learners, who are the main subjects of education. In order to improve the recommendation accuracy, a recommendation algorithm based on improved collaborative filtering model is proposed in this paper. Firstly, according to the student behavior data, consider the behavior order to create the behavior graph and behavior route. Then, the path of text type is vectorized by the Keras Tokenizer method. Finally, the similarity between multidimensional behavior path vectors is calculated, and path collaborative filtering recommendations are performed for each dimension separately. The MOOC data of a university in China are introduced to experimentally compare the algorithm of the article as well as the control group algorithm. The results show that the proposed algorithm takes better values in evaluation indexes, thus verifying that this algorithm can improve the effectiveness of innovation and entrepreneurship education resources recommendation in universities.
Suppressing Shot Noise Using Quadratic Variable Step-Size Quantization for the Initial Acquisition of Camera-Raw Image Data

Digital images from today's premium digital cameras have a higher dynamic range than what humans can perceive. Maximumly keeping the image information captured by sensors while reducing the file size of the output raw image data is highly desirable. In this paper, we show that the extractable image information contained in high-quality HDR images taken by premium cameras can be preserved using a bit-depth of only 8 to 9 bits/pixel when the shot-noise-introduced extra entropy is suppressed. The quadratic variable step-size quantization is used to suppress the shot-noise-introduced entropy. The ratio of the quantization step-size of the quadratic quantizer over the shot noise amplitude is introduced as a parameter to measure the relative quantization noise level, and is used to design the quadratic quantizers that maximally suppress the shot noise. Finally, quadratically quantized real CFA camera-raw image data are losslessly compressed. Compared with the lossless compression on linearly quantized data, at least more than 50% bitrate savings are achieved. Such high bitrate savings without loss of extractable image information are very attractive. Thus, it is highly worthwhile to explore, in the future, hardware implementations of quadratic quantization in image sensors.
An Effective Two-way Metapath Encoder over Heterogeneous Information Network for Recommendation

Heterogeneous information networks (HINs) are widely used in recommender system research due to their ability to model complex auxiliary information beyond historical interactions to alleviate data sparsity problem. Existing HIN-based recommendation studies have achieved great success via performing graph convolution operators between pairs of nodes on predefined metapath induced graphs, but they have the following major limitations. First, existing heterogeneous network construction strategies tend to exploit item attributes while failing to effectively model user relations. In addition, previous HIN-based recommendation models mainly convert heterogeneous graph into homogeneous graphs by defining metapaths ignoring the complicated relation dependency involved on the metapath. To tackle these limitations, we propose a novel recommendation model with two-way metapath encoder for top-N recommendation, which models metapath similarity and sequence relation dependency in HIN to learn node representations. Specifically, our model first learns the initial node representation through a pre-training module, and then identifies potential friends and item relations based on their similarity to construct a unified HIN. We then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different metapaths. Finally, the representations on different meta-paths are aggregated through the attention fusion layer to yield rich representations. Extensive experiments on three real datasets demonstrate the effectiveness of our method.
Tensor-network discriminator architecture for classification of quantum data on quantum computers

We demonstrate the use of matrix product state (MPS) models for discriminating quantum data on quantum computers using holographic algorithms, focusing on classifying a translationally invariant quantum state based on $L$ qubits of quantum data extracted from it. We detail a process in which data from single-shot experimental measurements are used to optimize an isometric tensor network, the tensors are compiled into unitary quantum operations using greedy compilation heuristics, parameter optimization on the resulting quantum circuit model removes the post-selection requirements of the isometric tensor model, and the resulting quantum model is inferenced on either product state or entangled quantum data. We demonstrate our training and inference architecture on a synthetic dataset of six-site single-shot measurements from the bulk of a one-dimensional transverse field Ising model (TFIM) deep in its antiferromagnetic and paramagnetic phases. We experimentally evaluate models on Quantinuum's H1-2 trapped ion quantum computer using entangled input data modeled as translationally invariant, bond dimension 4 MPSs across the known quantum phase transition of the TFIM. Using linear regression on the experimental data near the transition point, we find predictions for the critical transverse field of $h=0.962$ and $0.994$ for tensor network discriminators of bond dimension $\chi=2$ and $\chi=4$, respectively. These predictions compare favorably with the known transition location of $h=1$ despite training on data far from the transition point. Our techniques identify families of short-depth variational quantum circuits in a data-driven and hardware-aware fashion and robust classical techniques to precondition the model parameters, and can be adapted beyond machine learning to myriad applications of tensor networks on quantum computers, such as quantum simulation and error correction.