Reviewerly/exHarmony huggingface dataset:
Identity-Based Edge Computing Anonymous Authentication Protocol

With the development of sensor technology and wireless communication technology, edge computing has a wider range of applications.The privacy protection of edge computing is of great significance.In the edge computing system, in order to ensure the credibility of the source of terminal data, mobile edge computing (MEC) needs to verify the signature of the terminal node on the data.During the signature process, the computing power of edge devices such as wireless terminals can easily become the bottleneck of system performance.Therefore, it is very necessary to improve efficiency through computational offloading.Therefore, this paper proposes an identitybased edge computing anonymous authentication protocol.The protocol realizes mutual authentication and obtains a shared key by encrypting the mutual information.The encryption algorithm is implemented through a thresholded identity-based proxy ring signature.When a large number of terminals offload computing, MEC can set the priority of offloading tasks according to the user's identity and permissions, thereby improving offloading efficiency.Security analysis shows that the scheme can guarantee the anonymity and unforgeability of signatures.The probability of a malicious node forging a signature is equivalent to cracking the discrete logarithm puzzle.According to the efficiency analysis, in the case of MEC offloading, the computational complexity is significantly reduced, the computing power of edge devices is liberated, and the signature efficiency is improved.
View Types and Visual Communication Cues for Remote Collaboration

Over the last several years, remote collaboration has been getting more attention in the research community because of the COVID-19 pandemic.In previous studies, researchers have investigated the effect of adding visual communication cues or shared views in collaboration, but there has not been any previous study exploring the influence between them.In this paper, we investigate the influence of view types on the use of visual communication cues.We compared the use of the three visual cues (hand gesture, a pointer with hand gesture, and sketches with hand gesture) across two view types (dependent and independent views), respectively.We conducted a user study, and the results showed that hand gesture and sketches with the hand gesture cues were well matched with the dependent view condition, and using a pointer with the hand gesture cue was suited to the independent view condition.With the dependent view, the hand gesture and sketch cues required less mental effort for collaborative communication, had better usability, provided better message understanding, and increased feeling of co-presence compared to the independent view.Since the dependent view supported the same viewpoint between the remote expert and a local worker, the local worker could easily understand the remote expert's hand gestures.In contrast, in the independent view case, when they had different viewpoints, it was not easy for the local worker to understand the remote expert's hand gestures.The sketch cue had a benefit of showing the final position and orientation of the manipulating objects with the dependent view, but this benefit was less obvious in the independent view case (which provided a further view compared to the dependent view) because precise drawing in the sketches was difficult from a distance.On the contrary, a pointer with the hand gesture cue required less mental effort to collaborate, had better usability, provided better message understanding, and an increased feeling of co-presence in the independent view condition than in the dependent view condition.The pointer cue could be used instead of a hand gesture in the independent view condition because the pointer could still show precise pointing information regardless of the view type.
Replay Attack Detection in Automatic Speaker Verification Using Gammatone Cepstral Coefficients and ResNet-Based Model

This paper presents a method for detecting replay attacks in an automatic speaker verification system. The replay attack is of interest because it is the most straightforward, effective attack, and difficult to detect. Even though many speech features and classifiers have been proposed, the detection performance, such as an equal error rate (EER), accuracy, and balanced accuracy, need to be improved. Therefore, we propose a method for replay attack detection that applies the Gammatone cepstral coefficients with a ResNet-based model. The proposed method was evaluated and compared with existing methods and baselines in the ASVspoof 2019 challenge. The results indicated that the proposed method outperforms our previous method and the baselines in which the EER was 8.4%. In addition, the accuracy and balanced accuracy of the spoofing detection were improved.
Toward Selective Membership Inference Attack against Deep Learning Model

In this paper, we propose a selective membership inference attack method that determines whether certain data corresponding to a specific class are being used as training data for a machine learning model or not. By using the proposed method, membership or non-membership can be inferred by generating a decision model from the prediction of the inference models and training the confidence values for the data corresponding to the selected class. We used MNIST as an experimental dataset and Tensorflow as a machine learning library. Experimental results show that the proposed method has a 92.4% success rate with 5 inference models for data corresponding to a specific class.
Priority Evasion Attack: An Adversarial Example That Considers the Priority of Attack on Each Classifier

Deep neural networks (DNNs) provide excellent services in machine learning tasks such as image recognition, speech recognition, pattern recognition, and intrusion detection. However, an adversarial example created by adding a little noise to the original data can result in misclassification by the DNN and the human eye cannot tell the difference from the original data. For example, if an attacker creates a modified right-turn traffic sign that is incorrectly categorized by a DNN, an autonomous vehicle with the DNN will incorrectly classify the modified right-turn traffic sign as a U-Turn sign, while a human will correctly classify that changed sign as right turn sign. Such an adversarial example is a serious threat to a DNN. Recently, an adversarial example with multiple targets was introduced that causes misclassification by multiple models within each target class using a single modified image. However, it has the weakness that as the number of target models increases, the overall attack success rate decreases. Therefore, if there are multiple models that the attacker wishes to attack, the attacker must control the attack success rate for each model by considering the attack priority for each model. In this paper, we propose a priority adversarial example that considers the attack priority for each model in cases targeting multiple models. The proposed method controls the attack success rate for each model by adjusting the weight of the attack function in the generation process while maintaining minimal distortion. We used MNIST and CIFAR10 as data sets and Tensorflow as machine learning library. Experimental results show that the proposed method can control the attack success rate for each model by considering each model's attack priority while maintaining minimal distortion (average 3.95 and 2.45 with MNIST for targeted and untargeted attacks, respectively, and average 51.95 and 44.45 with CIFAR10 for targeted and untargeted attacks, respectively).
Specification-guided component-based synthesis from effectful libraries

Component-based synthesis seeks to build programs using the APIs provided by a set of libraries. Oftentimes, these APIs have effects, which make it challenging to reason about the correctness of potential synthesis candidates. This is because changes to global state made by effectful library procedures affect how they may be composed together, yielding an intractably large search space that can confound typical enumerative synthesis techniques. If the nature of these effects are exposed as part of their specification, however, deductive synthesis approaches can be used to help guide the search for components. In this paper, we present a new specification-guided synthesis procedure that uses Hoare-style pre- and post-conditions to express fine-grained effects of potential library component candidates to drive a bi-directional synthesis search strategy. The procedure alternates between a forward search process that seeks to build larger terms given an existing context but which is otherwise unaware of the actual goal, alongside a backward search mechanism that seeks terms consistent with the desired goal but which is otherwise unaware of the context from which these terms must be synthesized. To further improve efficiency and scalability, we integrate a conflict-driven learning procedure into the synthesis algorithm that provides a semantic characterization of previously encountered unsuccessful search paths that is used to prune the space of possible candidates as synthesis proceeds. We have implemented our ideas in a tool called and demonstrate its effectiveness on a number of challenging synthesis problems defined over OCaml libraries equipped with effectful specifications.
Seq2Parse: neurosymbolic parse error repair

We present Seq2Parse, a language-agnostic neurosymbolic approach to automatically repairing parse errors. Seq2Parse is based on the insight that Symbolic Error Correcting (EC) Parsers can, in principle, synthesize repairs, but, in practice, are overwhelmed by the many error-correction rules that are not relevant to the particular program that requires repair. In contrast, Neural approaches are fooled by the large space of possible sequence level edits, but can precisely pinpoint the set of EC-rules that are relevant to a particular program. We show how to combine their complementary strengths by using neural methods to train a sequence classifier that predicts the small set of relevant EC-rules for an ill-parsed program, after which, the symbolic EC-parsing algorithm can make short work of generating useful repairs. We train and evaluate our method on a dataset of 1,100,000 Python programs, and show that Seq2Parse is accurate and efficient : it can parse 94% of our tests within 2.1 seconds, while generating the exact user fix in 1 out 3 of the cases; and useful : humans perceive both Seq2Parse-generated error locations and repairs to be almost as good as human-generated ones in a statistically-significant manner.
On sharp stochastic zeroth-order Hessian estimators over Riemannian manifolds

Abstract We study Hessian estimators for functions defined over an $n$-dimensional complete analytic Riemannian manifold. We introduce new stochastic zeroth-order Hessian estimators using $O (1)$ function evaluations. We show that, for an analytic real-valued function $f$, our estimator achieves a bias bound of order $ O ( \gamma \delta ^2 ) $, where $ \gamma $ depends on both the Levi–Civita connection and function $f$, and $\delta $ is the finite difference step size. To the best of our knowledge, our results provide the first bias bound for Hessian estimators that explicitly depends on the geometry of the underlying Riemannian manifold. We also study downstream computations based on our Hessian estimators. The supremacy of our method is evidenced by empirical evaluations.
Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing

Fog manufacturing can greatly enhance traditional manufacturing systems through distributed Fog computation units, which are governed by predictive computational workload offloading methods under different Industrial Internet architectures. It is known that the predictive offloading methods highly depend on accurate prediction and uncertainty quantification of runtime performance metrics, containing multivariate mixed-type responses (i.e., continuous, counting, binary). In this work, we propose a Bayesian sparse regression for multivariate mixed responses to enhance the prediction of runtime performance metrics and to enable the statistical inferences. The proposed method considers both group and individual variable selection to jointly model the mixed types of runtime performance metrics. The conditional dependency among multiple responses is described by a graphical model using the precision matrix, where a spike-and-slab prior is used to enable the sparse estimation of the graph. The proposed method not only achieves accurate prediction, but also makes the predictive model more interpretable with statistical inferences on model parameters and prediction in the Fog manufacturing. A simulation study and a real case example in a Fog manufacturing are conducted to demonstrate the merits of the proposed model.
A high-performance convolution block oriented accelerator for MBConv-Based CNNs

Convolution Neural Network (CNN) models have shown their dominance in computer vision tasks. Recently, a special convolution block, named MBConv block or inverted residual block, is proposed to construct CNNs to meet the real-time requirements on resource-constrained edge-computing platforms. The MBConv block is first proposed by MobileNetV2 and has been widely used to construct lightweight CNNs. However, the MBConv block brings new challenges to the structure of the computing engine, the bandwidth requirement of off-chip memory and the demand for on-chip memory when designing hardware accelerators. In this paper, a convolution Block Oriented Accelerator (BOA) is proposed for the inference of CNNs constructed on MBConv blocks. In BOA, the MBConv-based CNNs are performed block by block using a Block-Based Engine which consists of dedicated computing units for each layer of the MBConv block. To reduce both the bandwidth requirement of off-chip memory and the demand for on-chip memory, a two-level data flow optimization and an amortized weight loading method are proposed. Furthermore, a hierarchical scheduling scheme is proposed to improve the performance and flexibility so that BOA can guarantee all units running in parallel and support various MBConv-based CNNs. Finally, we deploy BOA on Xilinx VC709. We evaluate the accelerator on ImageNet for image classification. The results show that BOA can perform various MBConv-based CNNs and achieve 1.28x - 7.75x speedup on inference latency.
Online time-series forecasting using spiking reservoir

IoT-based automated systems require efficient online time-series analysis and forecasting and there is a growing requirement to enable such processing at the low-cost constrained edge devices. Classical approaches such as Online Autoregressive Integrated Moving Average (Online ARIMA), Seasonal ARIMA (SARIMA) etc. and Artificial Neural Network (ANN) based techniques including Long-short Term Memory (LSTM) do not cater to this niche requirement due to their memory and computation power requirements. Neuromorphic computing and bio-plausible spiking neural networks, being both data and energy efficient, may offer a better solution. In this work, a novel spiking reservoir based network is proposed for online time series forecasting that relies on temporal spike encoding with a feedback driven online learning mechanism. The proposed network is capable of avoiding rapidly fading memory problem. The prediction accuracy of the network (tested on nine time-series datasets) outperforms conventional methods like SARIMA, Online ARIMA, Stacked LSTM, achieving up to 8% higher R2 score while using negligible buffer memory.
A Decentralized Trust Establishment Protocol for Smart IoT Systems

The Internet of Things (IoT) is a widely deployed technology to provide innovative services in many several fields such as industry, logistics, healthcare, and energy management. The integration of artificial intelligence, machine learning, edge/cloud computing, 5G networks, big data analytics, and other emerging technologies in IoT systems is leading to the emergence of Smart Systems that support devices to make autonomous decisions in order to improve and facilitate people’s lives. These smart IoT devices produce a huge amount of sensitive data. Thus, establishing Trust between these devices is vital to ensure the security of the sensed data and for the normal functionality of smart systems. To address these challenges, several Trust establishment protocols have been proposed, but they have some limitations such as centralization, the need for human intervention, and the lack of adaptability to smart devices with computation and storage constraints. In this paper, a decentralized trust establishment protocol is proposed for smart IoT devices and is applicable for different scenarios. The protocol provides decentralized identity management, mutual authentication, access control, and secure communication by combining Blockchain technology with PV-SAS-MCA massage cross-authentication protocol based on Short Authenticated Strings (SAS). To the best of our knowledge, this research is the first to propose a new approach that uses Blockchain as an extra distributed and authenticated channel to exchange Short Authenticated Strings without physical interaction. This helps to allow entities in the Smart IoT environment not only to identify each other but also to exchange their public keys in a secure manner. Moreover, the proposed protocol uses Elliptic Curve Cryptography (ECC) to secure communications with significantly smaller key sizes and lower computation overhead. Experimental results reveal that the time required to complete the authentication process is less than 90 ms which is a superior performance when compared to state-of-the-art decentralized authentication mechanisms.
Cluster with GANs

Generative adversarial networks (GANs) and clustering algorithms are both very popular unsupervised methodologies in machine learning. In this work, we propose a novel strategy that uses GANs to improve clustering and vice verse. We start by providing a simple but yet powerful scheme for improving clustering methods that rely on the latent space of GANs. Then, we turn to demonstrate how the output of clustering techniques can be employed for significantly improving the output quality of GANs. We empirically demonstrate the improvement that is achieved by our proposed framework both for clustering and the generation quality of GANs measured by the inception score.
Robust clustering of Ethereum transactions using time leakage from fixed nodes

Ethereum has received increasing attention as the first blockchain platform to support smart contracts. Data mining has become an important tool for analyzing Ethereum transactions. However, existing methods have the disadvantage of covering partial transactions and being vulnerable to privacy-enhancing techniques. In this paper, we propose a scheme for transaction correlation with the node as an entity, which can cover all transactions while being resistant to privacy-enhancing techniques. Utilizing timestamps relayed from N fixed nodes to describe the network properties of transactions, we cluster transactions that enter the network from the same source node. Experimental results show that our method can determine with 97% precision whether two transactions enter the network from the same source node.
Non-Local Temporal Difference Network for Temporal Action Detection

As an important part of video understanding, temporal action detection (TAD) has wide application scenarios. It aims to simultaneously predict the boundary position and class label of every action instance in an untrimmed video. Most of the existing temporal action detection methods adopt a stacked convolutional block strategy to model long temporal structures. However, most of the information between adjacent frames is redundant, and distant information is weakened after multiple convolution operations. In addition, the durations of action instances vary widely, making it difficult for single-scale modeling to fit complex video structures. To address this issue, we propose a non-local temporal difference network (NTD), including a chunk convolution (CC) module, a multiple temporal coordination (MTC) module, and a temporal difference (TD) module. The TD module adaptively enhances the motion information and boundary features with temporal attention weights. The CC module evenly divides the input sequence into N chunks, using multiple independent convolution blocks to simultaneously extract features from neighboring chunks. Therefore, it realizes the information delivered from distant frames while avoiding trapping into the local convolution. The MTC module designs a cascade residual architecture, which realizes the multiscale temporal feature aggregation without introducing additional parameters. The NTD achieves a state-of-the-art performance on two large-scale datasets, 36.2% mAP@avg and 71.6% mAP@0.5 on ActivityNet-v1.3 and THUMOS-14, respectively.
Museum Facilitator Practice as Infrastructure Design Work for Public Computing

In this paper, we emphasize the importance of looking beyond technology itself and including interactional and experiential elements in our research gaze in informal computing education in science museums. We argue that, in these contexts, facilitation can be understood as design work that is both complex and challenging. We identify how focusing on infrastructuring—the process by which an exhibit's support systems emerge, shift, and are sustained in practice—can help develop a richer understanding of the complexity of this work. In this study, we examine facilitators' experiences of facilitating and supporting a computational exhibit in a science museum. We identify how facilitators' expertise, roles, and responsibilities shape their facilitation work. Through analysis of video-recorded interactions at the exhibit and interviews with facilitators, we showcase how facilitators' in-the-moment design moves addressed breakdowns of the exhibit's infrastructure. These design moves emerged from the complex interaction of each facilitator's epistemological views of computing and museum education, values, past experiences, and disciplinary background, as well as the museum culture and other institutional constraints. This analysis represents an important challenge to technocentric stances in informal computing education with implications for informal educators and managers, as well as designers and design researchers more broadly.
Deep learning-based infrared image deblurring

Blurry images are not only visually unappealing, but they also degrade the performance of computer vision applications dramatically. As a result, motion deblurring for the thermal infrared picture plays a critical role in infrared systems. In recent years, convolutional neural network-based image deblurring methods have yielded promising performance with remarkable results and low computational cost. Inspired by these works, in this paper, we investigate an end-to-end deblurring model for single blurred thermal IR image by adopting the multi-input approach. Our model achieve PSNR and SSIM scores of 31.83 and 0.6435 when evaluating on our blur-sharp thermal infrared image pair dataset. Furthermore, the lightweight nature of our model allows it to operate at 140 FPS when inferring on Tesla V100 GPU.