Reviewerly/exHarmony huggingface dataset:
Research on Measuring Method of Function Size of Information System

With the rapid development of software industry, a variety of new technologies are produced. Research and development in cloud computing, big data, artificial intelligence, the Internet of Things, blockchain, and other related fields continue to prosper. The increasing lines of code, function points, escalating scale of software systems, and the rising complexity of software development collectively complicate software cost estimation. This article studies how to estimate the size, workload, and cost of an information system using functional scale measurement methods.
Evaluation and Mitigation of Faults Affecting Swin Transformers

In the last decade, a huge effort has been spent on assessing the reliability of Convolutional Neural networks (CNNs), probably the most popular architecture for image classification tasks. However, modern Deep Neural Networks (DNNs) are rapidly overtaking CNNs, as state-of-the-art results for many tasks are achieved with the Transformers, innovative DNN models. Transformers' architecture introduces the concept of attention as an alternative to the classical convolution operation. The aim of this work is to propose a reliability analysis of the Swin Transformer, one of the most accurate DNN used for Image Classification, that greatly improves the results obtained by traditional CNNs. In particular, this paper shows that, similar to CNNs, Transformers are susceptible to single faults affecting weights and neurons. Furthermore, it is shown how output ranging, a well-known technique to reduce the impact of a fault in CNNs, is not as effective for the Transformer. The alternative solution proposed by this work is to introduce a ranging not only on the output, but also on the input and on the weight of the fully connected layers. Results show that, on average, the number of critical faults (i.e., that modify the network's output) affecting neurons decreases by a factor of 1.91, while for faults affecting the network's weights this value decreases by a factor of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\cdot 10^{5}$</tex> .
Scalable Federated Learning Simulations Using Virtual Client Engine in Flower

Federated Learning (FL) is a novel technology that has gained significant visibility among the scientific community and industry. In order to be deployed in real scenarios, Federated Learning must address some critical challenges. One key challenge that Federated Learning poses is system scalability, as it is foreseen that this technology will be deployed in IoT scenarios with billions of clients in the future. In addition, there are many ongoing efforts regarding algorithms and frameworks proposed to foster Federated Learning testing and research. In this work, we review a recent architecture of the open-source framework Flower, called Virtual Client Engine (VCE), which allows to efficiently run simulations with hundreds of users with acceptable accuracy and keeping the computational resource consumption low. We compare relevant classification metrics (accuracy, loss) to classic centralized machine learning approaches and Flower's previous architecture named Edge Client Engine (ECE) to show the improvement in the number of clients while keeping the accuracy high. For a 200 clients case, we achieve an accuracy of around 91% for the MNIST and 88% for the SPEECH evaluation setups, both cases using the VCE Flower architecture.
Quantum Photonics Enhances Continuous Variable Quantum Key Distribution

Continuous-variable quantum key distribution (CV-QKD) technology has the characteristics of high channel capacity and well compatibility with existing optical communication infrastructures, and has become an important implementation method of quantum secure communication. Quantum photonics technology provides a promising solution for the construction of low-cost, miniaturized, portable, scalable and highly stable CV-QKD systems that can be deployed at large scale [1]. Therefore, it is of great significance to research and build a chip-based CV-QKD system with higher integration, higher system performance and higher practical security for the development of high-performance and practical quantum secure communication technology. This paper mainly reports the latest progress of the research of on-chip CV-QKD system in terms of system practical security [2] and integration of high-performance light sources [3].
A GAN-based Approach to Detect AI-Generated Images

The ease with which deep learning can generate fake images has created a pressing need for a robust platform to distinguish between real and fake imagery. However, existing methods in image forensics rely on complex deep learning architectures that are expensive to train and have limited usability due to their large model size. This study examines the difficulty of detecting state-of-the-art image manipulations, both manually and automatically. We introduce G-JOB GAN, a machine learning model based on Generative Adversarial Networks (GAN), which generates highly realistic images and achieves a 95.7% accuracy in detecting realistic generated images. The same architecture of G-JOB Gan can also detect fake images with a similar probability. To verify the results, we compare our results to several similar GAN architectures, including Style GAN, Pro GAN, and the Original GAN. Our model outperforms other GAN models in term of detection accuracy.
Validation of the Full-Wave Projector-Based Hamiltonian Analysis of Port-Driven Microwave Resonators

Quantum information processing technologies are gaining interest due to their potential for performance exceeding what is possible with the best classical computers. One of the most promising platforms is the circuit quantum electrodynamics (cQED) architecture that controls superconducting circuit qubits by coupling them to electromagnetic fields in microwave resonators or waveguides. As these devices become increasingly complex, existing analysis and design approaches that often rely on lumped element circuit approximations can become inadequate due to their lack of accuracy and flexibility. This is leading to a growing interest in developing quantum full-wave numerical modeling methods for the high-fidelity analysis of cQED devices. In this work, we focus on a quantum full-wave modeling approach that uses a projector-based quantization approach that allows a problem to be decomposed into distinct subdomains whose fields can be quantized more easily. This leads to a framework that can naturally incorporate microwave ports, which is not straightforward with other formulations. Here, we consider an explicit problem of a coaxial-fed rectangular waveguide cavity to validate parts of our projector-based formalism. Toward this end, we apply this approach to a classical Hamiltonian analysis of this system that can be used to more easily validate the relevant full-wave aspects of the corresponding quantum formulation. In addition to presenting the formulation of the full-wave classical Hamiltonian of the system, we derive equations of motion and discuss an analytical solution procedure with an input-output theory approach. We validate our formulation by comparing the input-output theory results to standard finite element method (FEM) scattering parameter simulations.
Designing Energy Efficient Neural Networks According to Device Operation Principles

This article compares the accuracy and efficiency of two neural network architectures for estimating the performance metrics of a modified uni-traveling wave carrier (MUTC) photodetector: a fully connected neural network (FCNN) and a recurrent neural network (RNN)-like architecture designed specifically for the unique properties and operation principles of the device. The RNN-like architecture mimics the current flow and wave propagation inside the MUTC PD, with distinct neural network layers used to pair parameters of each PD layer, inform the network about interfaces between layers, mimic current transport between neighboring layers, and teach the network about the order and bi-directional interaction among the paired inputs. Results show that while the RNN-like architecture exhibits slightly higher learning accuracy than the FCNN, the RNN-like architecture consumes less energy and requires less time for training due to using fewer neurons. This study highlights the potential of physics-inspired neural networks in solving problems in electromagnetics and photonics.
ShapeShifter: Protecting FPGAs from Side-Channel Attacks with Isofunctional Heterogeneous Modules

Cloud service providers are interested in deploying multi-tenant Field-Programmable Gate Arrays (FPGAs) for virtualized computation platforms. A primary concern towards such shared FPGA platforms is ensuring the security of critical applications (such as encryption cores) against hardware-based attacks, such as remote power Side-Channel Attacks (SCAs) intended to steal secret assets like encryption keys. To address this issue, we propose ShapeShifter, a novel defense methodology based on design diversity that generates isofunctional variants of the target application at the design stage using different synthesis, placement, and routing procedures. ShapeShifter leverages the dynamic partial reconfiguration feature of modern FPGAs to exchange the variants at run-time, causing dynamic variations in the power trace (vertical obfuscation) and introducing misalignment in the time domain (horizontal obfuscation) to thwart SCAs. ShapeShifter successfully thwarts the Correlation Power Analysis (CPA) attack on an Advanced Encryption Standard (AES) implementation, ensuring unsuccessful key byte recovery for up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10\times$</tex> more traces. It also decreases the CPA value by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.69\times$</tex> , reducing the attacker's confidence in key recovery.
A Model-based Low-frequency Image Quality Enhancement Method

In response to the problems of limited image quality and low resolution caused by the inability of low-frequency millimeter wave data to meet imaging requirements, this article innovatively proposes a low-frequency millimeter wave image quality enhancement method based on diffusion model. This method is based on the image domain diffusion model to enhance the image quality. It is combined with the holographic data domain convolutional neural network to learn the mapping relationship between low-frequency and high-frequency images and echo data between different domains. It improves the quality and resolution of low-frequency millimeter wave images and is verified through experiments, solving the problem of poor low-frequency image quality and low resolution.
Neural Network Quantisation for Faster Homomorphic Encryption

Homomorphic encryption (HE) enables calculating on encrypted data, which makes it possible to perform privacy-preserving neural network inference. One disadvantage of this technique is that it is several orders of magnitudes slower than calculation on unencrypted data. Neural networks are commonly trained using floating-point, while most homomorphic encryption libraries calculate on integers, thus requiring a quantisation of the neural network. A straightforward approach would be to quantise to large integer sizes (e.g. 32 bit) to avoid large quantisation errors. In this work, we reduce the integer sizes of the networks, using quantisation-aware training, to allow more efficient computations. For the targeted MNIST architecture proposed by Badawi et al. [1], we reduce the integer sizes by 33% without significant loss of accuracy, while for the CIFAR architecture, we can reduce the integer sizes by 43%. Implementing the resulting networks under the BFV homomorphic encryption scheme using SEAL, we could reduce the execution time of an MNIST neural network by 80% and by 40% for a CIFAR neural network.
Bitrate-based Video Traffic Classification

Granular network traffic classification is gaining high priority, which is crucial for the Internet Service Providers (ISP), Over The Top (OTT) providers' operation and maintenance management and users' Quality of Experience (QoE) improvement. Streaming video category traffic takes up a significant proportion of Internet traffic. Its ground truth sources include the application types, streaming network communication protocols, resolution, refresh rates, video encoding protocols, physical network resource bandwidth, and associated with the video source. However, the credibility of the traffic classification work based on the ground truths above-mentioned is not high since the quality of the video source cannot be guaranteed. The user's perception is poor even when watching higher resolution and refresh rate in a particular scenario. Secondly, different video platforms use different technical standards, which will inevitably cause video quality compression loss in transmission and viewing. The user viewing experience varies greatly, even under the same standard. In this paper, we propose to implement the traffic classification task by calculating the video bitrates of Video on Demand (VoD) and Video Live Streaming (VLS) as accurate classification labels and using machine learning techniques, in which we will examine the real-time bitrates during real-world video transmission compared with the bitrates set by theoretical recommendations to find the differences between the two scenarios.
Convolutional Sparse Coding and Dictionary Learning for Lidar Depth Completion in Automotive Scenarios

The interest in LiDAR sensors for autonomous driving applications has recently increased: solid state architectures have made it possible to reduce sizes and costs while maintaining higher scanning resolution compared to RADAR sensors. In a dynamic automotive scenario, LiDAR depth measurements result in a discrete point cloud which is typically sparse and may contain irregular/missing depth information, moreover, one may further reduce the illuminated pixels to increase the scanning rate and reduce computational cost. Here, a novel application of both a patch based and a convolutional sparse coding (CSC) approach for LiDAR depth completion are presented and validated using the publicly available KITTI dataset of outdoor automotive scenarios. Patch-based sparse coding approach may result inaccurate in representing global image features and edges, especially when the missing data percentage is high. CSC allows to process the data globally while still preserving local information by constructing the dictio-nary as a concatenation of convolutional filters. The dictionary considered for both approaches is either composed of Daubechies Wavelets or learned from depth images of the urban SYNTHIA dataset using K-SVD and Convolutional Dictionary Learning (CDL) strategies. Resulting depth maps using the CSC based approach for various sparsity levels produce smooth images and an enhanced scenario awareness. An analysis based on the Sparse Mean Absolute Error (SMAE) and Weighted Mean Absolute Error (WMAE) indicates that depth and edge preservation improves with respect to patch-based strategies.
Predicting Frags in Tactic Games using Machine Learning Techniques and Intuitive Knowledge

High-quality prediction models can be used for game analysis and help to create challenging and human-like artificial intelligence (AI) agents. In this Predicting Frags in Tactic Games grand challenge, we apply our intuitive knowledge of video game for feature engineering, and propose a sequential floating forward and backward method combined with gradient boosting decision tree (GBDT) algorithm for feature selection. We generate different feature subsets which represent different aspects of the problem to train several GBDT models, and use the ensemble of these models for frag prediction. Our methods can be considered as a collection of heuristic rules and have well interpretability. They can act as heuristic evaluation functions that properly approximate the outcomes, which are critical to accelerating solution discovery in artificial intelligence search algorithms. The effectiveness of our methods was proven by experiments and competition results.
Paint ChatGPT: A Platform for Old Shanghai Calendar Card Generation

With the development of technology, the heritage of Chinese traditional culture has suffered from historical oppression. Recently, the language model ChatGPT has attracted wide attention from all walks of life with its powerful dialogue and analytical understanding. However, it has limitations in image processing. And Image Foundation Models, such as Stable Diffusion, can handle analytical images but are not as flexible as some language models. As a result, we envisioned: To promote the excellent Chinese traditional art and culture, can ChatGPT be combined with DALL-E2 to create a new non-traditional New Year painting? Based on this, we designed and built a system called Paint ChatGPT.
Cross-Level Guided Attention for Human-Object Interaction Detection

Recently, the transformer-based methods have achieved advanced performance result in human-object interaction (HOI) detection task. However, most of them directly utilize the semantically high-level feature from the deep layer's output in pre-trained backbone to get the final HOI detection results, which we consider may prevent the further performance improvement due to the semantic gap between the upstream pre-train task and HOI detection task. In this work, we design a Cross-Level Guided Attention Network (CLAN) for HOI detection. The proposed method utilizes the information from the pre-training task's semantically high-level feature to generate the attention score towards the low-level and primitive feature to get the key signal for HOI detection task. Experiments shows that CLAN can achieve competitive performance results on both V-COCO and HICO-DET benchmarks.
Method to Deploy Lightweight Models with a Novel Pipeline for Efficient Inference

Lightweight neural network models are commonly designed in real-time scenarios to meet the requirement of fast processing. During the deployment of the inference flow, lightweight models are frequently built by existing frameworks such as TensorFlow and OpenVINO. Since these frameworks are heavy and always need to create a call stack from the program entry to model execution, causing a lot of time consumption. The inference speed cannot be effectively improved especially with high latency requirement. To address the problem, we propose a novel lightweight model deployment pipeline to promote efficient inference on hardware. Our method optimizes primitives of executable operations to take full advantages of the hardware. The executable graph is finally created, significantly reducing the time of stack calling. Experimental results demonstrate that our method is superior to TensorFlow and OpenVINO in the aspect of the inference speed, and can be applied for the construction of lightweight models.
XGC-VQA: A Unified Video Quality Assessment Model for User, Professionally, and Occupationally-Generated Content

With the rapid growth of Internet video data amounts and types, a unified Video Quality Assessment (VQA) is needed to inspire video communication with perceptual quality. To meet the real-time and universal requirements in providing such inspiration, this study proposes a VQA model from a classification of User Generated Content (UGC), Professionally Generated Content (PGC), and Occupationally Generated Content (OGC). In the time domain, this study utilizes non-uniform sampling, as each content type has varying temporal importance based on its perceptual quality. In the spatial domain, centralized downsampling is performed before the VQA process by utilizing a patch splicing/sampling mechanism to lower complexity for real-time assessment. The experimental results demonstrate that the proposed method achieves a median correlation of 0.7 while limiting the computation time below 5s for three content types, which ensures that the communication experience of UGC, PGC, and OGC can be optimized altogether.
A Fast Gradient Boosting Based Approach for Predicting Frags in Tactic Games

Predicting the probability of scoring a frag in a tactical video game is a challenging task. It is hard for humans to evaluate the real-time game situation and predict whether a player can score in his/her turn. In this paper, we present a fast gradient boosting based approach to this problem consisting of data analysis, feature engineering, and model construction. Firstly, we analyze the game data and identify the key factors that influence the probability of frag scoring. Then, we extract relevant features from game states metadata and map metadata in the feature engineering stage. Finally, we train and predict the probability of scoring a frag using a gradient boosting based method. Our proposed approach achieves an AUC score of 0.8008 on the whole test set, and only takes 156 seconds for 10-fold cross-validation, demonstrating its effectiveness and efficiency.
Towards End-to-End Verified TEEs via Verified Interface Conformance and Certified Compilers

Trusted Execution Environments (TEE) are ubiq-uitous. They form the highest privileged software component of the platform with full access to the system and associated devices. However, vulnerabilities have been found in deployed TEEs allowing an attacker to gain complete control. Despite the progress made in fully-verified software systems, few deployed TEEs are fully-verified, due to the high cost of verification. Instead of aiming for full-functional correctness, this paper proposes a formal framework and approach that leverages com-partmentalization at the source level to bring security-relevant properties verified at the source level down to the binary via existing certified compilers. The benefit of our approach is the relative low cost of verification: developers can use existing automated program verification tools and certified compilers. Our case studies demonstrate how security properties verified on two open-source TEEs at the source level can be pushed down to the compiled code by using an off-the-shelf certified compiler.
Simultaneous Super Resolution and Moving Object Detection from Low Resolution Surveillance Videos

Video surveillance has become a significant method to preserve public security specifically in the forensic field where high resolution videos are vital. However, the security video footage captured is often found to be low in clarity and deteriorated with noise, blurs and uneven illumination which can cause false detections and wrong interpretations. In light of this relevant fact, this work proposes a novel technique that does video super resolution with simultaneous detection of the moving objects using a tensor based convex optimization problem. The ideas of multilinear algebra are utilized in the super resolution framework to implement re-weighted tensor nuclear norm minimization for separating the highly correlated scene background. The spatio-temporal continuity featured by the sparse foreground components is grabbed by the Tensor Total Variation (TTV) regularization to achieve precise object detection along with <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l1</inf> regularization. The brilliance of the proposed approach is in the joint formulation and implementation of video super resolution and simultaneous moving object detection from the available deteriorated videos. This is solved using the Augmented Lagrangian Method (ALM) with alternating direction scheme and the results validate the superior performance of the proposed method against the compared approaches.