Reviewerly/exHarmony huggingface dataset:
Renovated XTEA Encoder Architecture-Based Lightweight Mutual Authentication Protocol for RFID and Green Wireless Sensor Network Applications

Wireless sensor networks find applications everywhere in day to day activities right from attendance entry systems to healthcare monitoring systems. The evolution of the Internet of Things (IoT) as the Internet of Everything (IoET) makes the wireless sensor network omnipresent and increases the use of Radio Frequency Identification (RFID) for the proper identification of devices and sensor nodes which are mostly battery operated. As technology evolves, security threats also increase rapidly. This mandates a strong and energy-efficient green solution. This work attempted to address these issues by effectively deploying the lightweight encryption scheme called Extended Tiny Encryption Algorithm (XTEA). Though the XTEA is lightweight and famous, it is commonly known for various attacks. Our work patches the security threats in the XTEA by applying domain-specific customization, random number utilization, and undisclosed key renewal techniques. Two custom Renovated XTEA Mutual Authentication Protocol (RXMAP) encoder architectures, namely, RXMAP-1 and RXMAP-2, are proposed based on the replacement of accurate computational blocks with approximate blocks. The proposed RXMAP protocol is evaluated for its computational and storage overhead and verified against various security threats using BAN logic formal verification and informal verification. The proposed encoder architectures are simulated for functional verification, and ASIC implementation is done with a 132 nm process node. ASIC implementation results show that the proposed designs RXMAP-1 and RXMAP-2 occupy 53.11% and 53.31% lesser area compared to XTEA I and 52.97% and 53.18% lesser area compared to XTEA II implementation. The total power consumed by the proposed encoder architectures RXMAP-1 and RXMAP-2 is 68.76% and 71.64% lesser than XTEA II implementation, respectively, while maintaining the equal throughput.
Two decades of cyberattack simulations: A systematic literature review

Cyberattack simulations appear across multiple computer security domains and are interpreted in many different but equally viable ways. However, this makes the topic appear fragmented and inconsistent, making it challenging to identify and communicate relevant research. Therefore, this article contributes to a unified baseline by presenting the results of a systematic literature review. The review targeted attack simulations published between 1999 and 2019, specifically those exploring which specific steps result in successful attacks. The search initially produced 647 articles, later reduced to 11 key contributions. Despite being scattered across application domains, their general aims, contributions, and problem statements were remarkably similar. This was despite them generally not citing each other or a common body of work. However, the attack simulations differed in implementation details, such as modeling techniques, attacker decision-making, and how time is incorporated. How to construct a fully unified view of the entire topic is still somewhat unclear, particularly from the 11 articles. However, the results presented here should help orient practitioners and researchers interested in attack simulations regarding both present and future work. Particularly since, despite the seemingly implausible sample, the cumulative evidence suggests that attack simulations have yet to be pursued as a distinct research topic.
GAXSS: Effective Payload Generation Method to Detect XSS Vulnerabilities Based on Genetic Algorithm

In the fields of social networking, media, and management, web applications on the Internet play a very indispensable role. A large amount of personal privacy information and login tokens make web applications often targeted by hackers. Cross-site scripting attacks are the most common method used to steal data from web applications. To solve the security risks caused by cross-site scripting vulnerabilities, security personnel need to actively discover these vulnerabilities to better defend against the harm. We proposed a novel genetic algorithm-based fuzzing scheme to address this problem. First, a small number of initial attack vectors are generated according to the interactive environment of the web application and then the attack vectors are sequenced into genes. Combined with the grammatical structure features of cross-site scripting and common bypass methods, the gene sequences are iteratively optimized and improved. Finally, the generated high-quality vectors are used to detect potential cross-site scripting threats in the application (we named the implementation of this approach GAXSS). The method we proposed can automatically detect the vulnerability of page interaction points and can obtain better detection results without a large number of test dictionaries, and the time cost is also reasonable. We have conducted vulnerability tests on many common open-source web applications, with a precision rate of 1.0 and an accuracy rate over 0.98. In addition, we also compared GAXSS with other well-known scanners and state-of-the-art detection methods. Its comprehensive performance is better, and it can effectively detect vulnerabilities.
Adaptive sign algorithm for graph signal processing

Efficient and robust online processing techniques for irregularly structured data are crucial in the current era of data abundance. In this paper, we propose a graph/network version of the classical adaptive Sign algorithm for online graph signal estimation under impulsive noise. The recently introduced graph adaptive least mean squares algorithm is unstable under non-Gaussian impulsive noise and has high computational complexity. The Graph-Sign algorithm proposed in this work is based on the minimum dispersion criterion and therefore impulsive noise does not hinder its estimation quality. Unlike the recently proposed graph adaptive least mean pth power algorithm, our Graph-Sign algorithm can operate without prior knowledge of the noise distribution. The proposed Graph-Sign algorithm has a faster run time because of its low computational complexity compared to the existing adaptive graph signal processing algorithms. Experimenting on steady-state and time-varying graph signals estimation utilizing spectral properties of bandlimitedness and sampling, the Graph-Sign algorithm demonstrates fast, stable, and robust graph signal estimation performance under impulsive noise modeled by alpha stable, Cauchy, Student’s t, or Laplace distributions.
Conflict-aware inference of python compatible runtime environments with domain knowledge graph

Code sharing and reuse is a widespread use practice in software engineering. Although a vast amount of open-source Python code is accessible on many online platforms, programmers often find it difficult to restore a successful runtime environment. Previous studies validated automatic inference of Python dependencies using pre-built knowledge bases. However, these studies do not cover sufficient knowledge to accurately match the Python code and also ignore the potential conflicts between their inferred dependencies, thus resulting in a low success rate of inference. In this paper, we propose PyCRE, a new approach to automatically inferring Python compatible runtime environments with domain knowledge graph (KG). Specifically, we design a domain-specific ontology for Python third-party packages and construct KGs for over 10,000 popular packages in Python 2 and Python 3. PyCRE discovers candidate libraries by measuring the matching degree between the known libraries and the third-party resources used in target code. For the NP-complete problem of dependency solving, we propose a heuristic graph traversal algorithm to efficiently guarantee the compatibility between packages. PyCRE achieves superior performance on a real-world dataset and efficiently resolves nearly half more import errors than previous methods.
DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation

Deep learning has shown impressive performance in semantic segmentation, but it is still unaffordable for resource-constrained mobile devices. While offloading computation tasks is promising, the high traffic demands overwhelm the limited bandwidth. Existing compression algorithms are not fit for semantic segmentation, as the lack of obvious and concentrated regions of interest (RoIs) forces the adoption of uniform compression strategies, leading to low compression ratios or accuracy. This paper introduces STAC, a DNN-driven compression scheme tailored for edge-assisted semantic video segmentation. STAC is the first to exploit DNN’s gradients as spatial sensitivity metrics for spatial adaptive compression and achieves superior compression ratio and accuracy. Yet, it is challenging to adapt this content-customized compression to videos. Practical issues include varying spatial sensitivity and huge bandwidth consumption for compression strategy feedback and offloading. We tackle these issues through a spatiotemporal adaptive scheme, which (1) takes partial strategy generation operations offline to reduce communication load, and (2) propagates compression strategies and segmentation results across frames through dense optical flow, and adaptively offloads keyframes to accommodate video content. We implement STAC on a commodity mobile device. Experiments show that STAC can save up to 20.95% of bandwidth without losing accuracy, compared to the state-of-the-art algorithm.
Few-shot Object Counting with Similarity-Aware Feature Enhancement

This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i.e., described by one or several support images) occurring in the query image. The major challenge lies in that the target objects can be densely packed in the query image, making it hard to recognize every single one. To tackle the obstacle, we propose a novel learning block, equipped with a similarity comparison module and a feature enhancement module. Concretely, given a support image and a query image, we first derive a score map by comparing their projected features at every spatial position. The score maps regarding all support images are collected together and normalized across both the exemplar dimension and the spatial dimensions, producing a reliable similarity map. We then enhance the query feature with the support features by employing the developed point-wise similarities as the weighting coefficients. Such a design encourages the model to inspect the query image by focusing more on the regions akin to the support images, leading to much clearer boundaries between different objects. Extensive experiments on various benchmarks and training setups suggest that we surpass the state-of-the-art methods by a sufficiently large margin. For instance, on a recent large-scale FSC-147 dataset, we surpass the state-of-the-art method by improving the mean absolute error from 22.08 to 14.32 (35%↑). Code has been released in https://github.com/zhiyuanyou/SAFECount.
Histokt: Cross Knowledge Transfer in Computational Pathology

The lack of well-annotated datasets in computational pathology (CPath) obstructs the application of deep learning techniques for classifying medical images. Many CPath workflows involve transferring learned knowledge between various image domains through transfer learning. Currently, most transfer learning research follows a model-centric approach, tuning network parameters to improve transfer results over few datasets. In this paper, we take a data-centric approach to the transfer learning problem and examine the existence of generalizable knowledge between histopathological datasets. First, we create a standardization workflow for aggregating existing histopathological data. We then measure inter-domain knowledge by training ResNet18 models across multiple histopathological datasets, and cross-transferring between them to determine the quantity and quality of innate shared knowledge. Additionally, we use weight distillation to share knowledge between models without additional training. We find that hard to learn, multi-class datasets benefit most from pretraining, and a two stage learning framework incorporating a large source domain such as ImageNet allows for better utilization of smaller datasets. Furthermore, we find that weight distillation enables models trained on purely histopathological features to outperform models using external natural image data.
Improving Non-Autoregressive End-to-End Speech Recognition with Pre-Trained Acoustic and Language Models

While Transformers have achieved promising results in end-to-end (E2E) automatic speech recognition (ASR), their autoregressive (AR) structure becomes a bottleneck for speeding up the decoding process. For real-world deployment, ASR systems are desired to be highly accurate while achieving fast inference. Non-autoregressive (NAR) models have become a popular alternative due to their fast inference speed, but they still fall behind AR systems in recognition accuracy. To fulfill the two demands, in this paper, we propose a NAR CTC/attention model utilizing both pre-trained acoustic and language models: wav2vec2.0 and BERT. To bridge the modality gap between speech and text representations obtained from the pre-trained models, we design a novel modality conversion mechanism, which is more suitable for logographic languages. During inference, we employ a CTC branch to generate a target length, which enables the BERT to predict tokens in parallel. We also design a cache-based CTC/attention joint decoding method to improve the recognition accuracy while keeping the decoding speed fast. Experimental results show that the proposed NAR model greatly outperforms our strong wav2vec2.0 CTC baseline (15.1% relative CER reduction on AISHELL-1). The proposed NAR model significantly surpasses previous NAR systems on the AISHELL-1 benchmark and shows a potential for English tasks.
Improving quantum simulation efficiency of final state radiation with dynamic quantum circuits

Reference arXiv:1904.03196 recently introduced an algorithm (QPS) for simulating parton showers with intermediate flavor states using polynomial resources on a digital quantum computer. We make use of a new quantum hardware capability called dynamical quantum computing to improve the scaling of this algorithm to significantly improve the method precision. In particular, we modify the quantum parton shower circuit to incorporate mid-circuit qubit measurements, resets, and quantum operations conditioned on classical information. This reduces the computational depth from $\mathcal{O}(N^5\log_2(N)^2)$ to $\mathcal{O}(N^3\log_2(N)^2)$ and the qubit requirements are reduced from $\mathcal{O}(N\log_2(N))$ to $\mathcal{O}(N)$. Using "matrix product state" statevector simulators, we demonstrate that the improved algorithm yields expected results for 2, 3, 4, and 5-steps of the algorithm. We compare absolute costs with the original QPS algorithm, and show that dynamical quantum computing can significantly reduce costs in the class of digital quantum algorithms representing quantum walks (which includes the QPS).
Integrating Artificial Intelligence and Augmented Reality in Robotic Surgery: An Initial dVRK Study Using a Surgical Education Scenario

Robot-assisted surgery has become progressively more and more popular due to its clinical advantages. In the meanwhile, the artificial intelligence and augmented reality in robotic surgery are developing rapidly and receive lots of attention. However, current methods have not discussed the coherent integration of AI and AR in robotic surgery. In this paper, we develop a novel system by seamlessly merging artificial intelligence module and augmented reality visualization to automatically generate the surgical guidance for robotic surgery education. Specifically, we first leverage reinforcement leaning to learn from expert demonstration and then generate 3D guidance trajectory, providing prior context information of the surgical procedure. Along with other information such as text hint, the 3D trajectory is then overlaid in the stereo view of dVRK, where the user can perceive the 3D guidance and learn the procedure. The proposed system is evaluated through a preliminary experiment on surgical education task peg-transfer, which proves its feasibility and potential as the next generation of robot-assisted surgery education solution.
Language Adaptive Cross-Lingual Speech Representation Learning with Sparse Sharing Sub-Networks

Unsupervised cross-lingual speech representation learning (XLSR) has recently shown promising results in speech recognition by leveraging vast amounts of unlabeled data across multiple languages. However, standard XLSR model suffers from language interference problem due to the lack of language specific modeling ability. In this work, we investigate language adaptive training on XLSR models. More importantly, we propose a novel language adaptive pretraining approach based on sparse sharing sub-networks. It makes room for language specific modeling by pruning out unimportant parameters for each language, without requiring any manually designed language specific component. After pruning, each language only maintains a sparse sub-network, while the sub-networks are partially shared with each other. Experimental results on a downstream multilingual speech recognition task show that our proposed method significantly outperforms baseline XLSR models on both high resource and low resource languages. Besides, our proposed method consistently outperforms other adaptation methods and requires fewer parameters.
Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA

Temporal Graph Neural Networks (TGNNs) are powerful models to capture temporal, structural, and contextual information on temporal graphs. The generated temporal node embeddings outperform other methods in many downstream tasks. Real-world applications require high performance inference on real-time streaming dynamic graphs. However, these models usually rely on complex attention mechanisms to capture relationships between temporal neighbors. In addition, maintaining vertex memory suffers from intrinsic temporal data dependency that hinders task-level parallelism, making it inefficient on general-purpose processors. In this work, we present a novel model-architecture co-design for inference in memory-based TGNNs on FPGAs. The key modeling optimizations we propose include a light-weight method to compute attention scores and a related temporal neighbor pruning strategy to further reduce computation and memory accesses. These are holistically coupled with key hardware optimizations that leverage FPGA hardware. We replace the temporal sampler with an on-chip FIFO based hardware sampler and the time encoder with a look-up-table. We train our simplified models using knowledge distillation to ensure similar accuracy vis-á-vis the original model. Taking advantage of the model optimizations, we propose a principled hardware architecture using batching, pipelining, and prefetching techniques to further improve the performance. We also propose a hardware mechanism to ensure the chronological vertex updating without sacrificing the computation parallelism. We evaluate the performance of the proposed hardware accelerator on three real-world datasets. The proposed model reduces the computation complexity by 84% and memory accesses by 67% with less than 0.33% accuracy loss. Compared with CPU/GPU, our FPGA accelerator achieves 16.4/2.3× speedup in latency and 0.27% improvement in accuracy compared with the state-of-the-art inference algorithm. To the best of our knowledge, this is the first work that performs model-architecture co-design on memory-based Temporal Graph Neural Networks.
Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning

It is of great challenge, though promising, to coordinate collective robots for hunting an evader in a decentralized manner purely in light of local observations. In this paper, this challenge is addressed by a novel hybrid cooperative pursuit algorithm that combines reinforcement learning with the artificial potential field method. In the proposed algorithm, decentralized deep reinforcement learning is employed to learn cooperative pursuit policies that are adaptive to dynamic environments. The artificial potential field method is integrated into the learning process as predefined rules to improve the data efficiency and generalization ability. It is shown by numerical simulations that the proposed hybrid design outperforms the pursuit policies either learned from vanilla reinforcement learning or designed by the potential field method. Furthermore, experiments are conducted by transferring the learned pursuit policies into real-world mobile robots. Experimental results demonstrate the feasibility and potential of the proposed algorithm in learning multiple cooperative pursuit strategies.
Over-the-Air Federated Learning via Second-Order Optimization

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users’ data locally with privacy and security guarantees. However, FL could result in task-oriented data traffic flows over wireless networks with limited radio resources. To design communication-efficient FL, most of the existing studies employ the first-order federated optimization approach that has a slow convergence rate. This however results in excessive communication rounds for local model updates between the edge devices and edge server. To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation. This is achieved by exploiting the waveform superposition property of a multi-access channel to implement the distributed second-order optimization algorithm over wireless networks. The convergence behavior of the proposed algorithm is further characterized, which reveals a linear-quadratic convergence rate with an accumulative error term in each iteration. We thus propose a system optimization approach to minimize the accumulated error gap by joint device selection and beamforming design. Numerical results demonstrate the system and communication efficiency compared with the state-of-the-art approaches.
Prosospeech: Enhancing Prosody with Quantized Vector Pre-Training in Text-To-Speech

Expressive text-to-speech (TTS) has become a hot research topic recently, mainly focusing on modeling prosody in speech. Prosody modeling has several challenges: 1) the extracted pitch used in previous prosody modeling works have inevitable errors, which hurts the prosody modeling; 2) different attributes of prosody (e.g., pitch, duration and energy) are dependent on each other and produce the natural prosody together; and 3) due to high variability of prosody and the limited amount of high-quality data for TTS training, the distribution of prosody cannot be fully shaped. To tackle these issues, we propose ProsoSpeech, which enhances the prosody using quantized latent vectors pre-trained on large-scale unpaired and low-quality text and speech data. Specifically, we first introduce a word-level prosody encoder, which quantizes the low-frequency band of the speech and compresses prosody at-tributes in the latent prosody vector (LPV). Then we introduce an LPV predictor, which predicts LPV given word sequence. We pre-train the LPV predictor on large-scale text and low-quality speech data and fine-tune it on the high-quality TTS dataset. Finally, our model can generate expressive speech conditioned on the predicted LPV. Experimental results show that ProsoSpeech can generate speech with richer prosody compared with baseline methods.