Reviewerly/exHarmony huggingface dataset:
Federated Learning Meets Blockchain: State Channel-Based Distributed Data-Sharing Trust Supervision Mechanism

With the rapid development of the 5G and 6G technology, it has become an inevitable trend to share the cross-domain scattered data and enhance data value transmission. As a new data-sharing technology with intelligence and privacy computing, federated learning (FL) receives wide attention. It can realize data value delivery and data privacy protection at the same time, however, it lacks supervision in the application process, and the reliability of the calculation process and result transmission cannot be guaranteed. As a distributed ledger technology, blockchain has the trust property but lacks computing power. Therefore, we propose to extend the computing and supervision capabilities of blockchain with state channel, using state channel to create sandboxes and instantiate FL tasks in order to realize the trust supervision mechanism based on sandboxes. In this article, we establish an FL-based distributed data-sharing architecture and on the basis of the architecture we design a state channel-based distributed data-sharing trust supervision mechanism. Through theoretical analysis and experimental verification, the supervision mechanism we designed has an excellent performance in improving system security, resisting malicious attacks, and improving data model quality.
Task2Sim: Towards Effective Pre-training and Transfer from Synthetic Data

Pre-training models on Imagenet or other massive datasets of real images has led to major advances in Computer vision, albeit accompanied with shortcomings related to curation cost, privacy, usage rights, and ethical issues. In this paper, for the first time, we study the transferability of pre-trained models based on synthetic data generated by graphics simulators to downstream tasks from very different domains. In using such synthetic data for pre-training, we find that downstream performance on different tasks are fa-vored by different configurations of simulation parameters (e.g. lighting, object pose, backgrounds, etc.), and that there is no one-size-fits-all solution. It is thus better to tailor syn-thetic pre-training data to a specific downstream task, for best performance. We introduce Task2Sim, a unified model mapping downstream task representations to optimal sim-ulation parameters to generate synthetic pre-training data for them. Task2Sim learns this mapping by training to find the set of best parameters on a set of “seen” tasks. Once trained, it can then be used to predict best simulation pa-rameters for novel “unseen” tasks in one shot, without re-quiring additional training. Given a budget in number of images per class, our extensive experiments with 20 di-verse downstream tasks show Task2Sim's task-adaptive pre-training data results in significantly better downstream per-formance than non-adaptively choosing simulation param-eters on both seen and unseen tasks. It is even competitive with pre-training on real images from Imagenet.
Unsupervised Topic Discovery in User Comments

On social media platforms like Twitter, users regularly share their opinions and comments with software vendors and service providers. Popular software products might get thousands of user comments per day. Research has shown that such comments contain valuable information for stakeholders, such as feature ideas, problem reports, or support inquiries. However, it is hard to manually manage and grasp a large amount of user comments, which can be redundant and of a different quality. Consequently, researchers suggested automated approaches to extract valuable comments, e.g., through problem report classifiers. However, these approaches do not aggregate semantically similar comments into specific aspects to provide insights like how often users reported a certain problem.We introduce an approach for automatically discovering topics composed of semantically similar user comments based on deep bidirectional natural language processing algorithms. Stakeholders can use our approach without the need to configure critical parameters like the number of clusters. We present our approach and report on a rigorous multiple-step empirical evaluation to assess how cohesive and meaningful the resulting clusters are. Each evaluation step was peer-coded and resulted in inter-coder agreements of up to 98%, giving us high confidence in the approach. We also report a thematic analysis on the topics discovered from tweets in the telecommunication domain.
Heterogeneous Multi UAV Mission Planning Based on Ant Colony Algorithm Powered BP Neural Network

With the development of modern science and technology, the field of UAV has also entered the era of high-tech exploration. Among them, the task planning, allocation, path exploration, and algorithm optimization of heterogeneous multi UAV technology are our main concerns. Based on the above situation, this paper proposes a heterogeneous multi UAV task planning technology based on ant colony algorithm powered BP neural network. The planning, research, and design are mainly carried out according to the actual situation of the UAV flight test, and the mathematical programming model is established according to the UAV load degree and maximum flight distance as constraints. This paper focuses on the contribution of the ant colony optimization algorithm to benefit maximization and task minimization. The experimental results show that the BP neural network optimized by the ant colony algorithm can improve the number of iterations and training time. Compared with some comparative algorithms, its performance is better.
Price discovery in the cryptocurrency market: evidence from institutional activity

This study analyzes the role of information shares of futures contracts in the price discovery of Bitcoin as well as its impact on the correlation of Bitcoin and traditional assets including S&P500, gold and bond. Time series estimations reveal positive and significant effects of information shares from Bitcoin futures trading activities, suggesting that the information from institutional trading activities contributes to the price discovery of Bitcoin. In addition, the study finds that increases in information shares of institutional trading in Bitcoin futures negatively affect Bitcoin-S&P500 and Bitcoin-gold correlations, but raise the Bitcoin-bond correlation.
GPR1200: A Benchmark for General-Purpose Content-Based Image Retrieval

Even though it has extensively been shown that retrieval specific training of deep neural networks is beneficial for nearest neighbor image search quality, most of these models are trained and tested in the domain of landmarks images. However, some applications use images from various other domains and therefore need a network with good generalization properties - a general-purpose CBIR model. To the best of our knowledge, no testing protocol has so far been introduced to benchmark models with respect to general image retrieval quality. After analyzing popular image retrieval test sets we decided to manually curate GPR1200, an easy to use and accessible but challenging benchmark dataset with a broad range of image categories. This benchmark is subsequently used to evaluate various pretrained models of different architectures on their generalization qualities. We show that large-scale pretraining significantly improves retrieval performance and present experiments on how to further increase these properties by appropriate fine-tuning. With these promising results, we hope to increase interest in the research topic of general-purpose CBIR.
Quantifying scrambling in quantum neural networks

We characterize a quantum neural network's error in terms of the network's scrambling properties via the out-of-time-ordered correlator. A network can be trained by optimizing either a loss function or a cost function. We show that, with some probability, both functions can be bounded by out-of-time-ordered correlators. The gradients of these functions can be bounded by the gradient of the out-of-time-ordered correlator, demonstrating that the network's scrambling ability governs its trainability. Our results pave the way for the exploration of quantum chaos in quantum neural networks.
Data-Driven Agile Requirements Elicitation through the Lenses of Situational Method Engineering

Ubiquitous digitalization has led to the continuous generation of large amounts of digital data, both in organizations and in society at large.In the requirements engineering community, there has been a growing interest in considering digital data as new sources for requirements elicitation, in addition to stakeholders.The volume, dynamics, and variety of data makes iterative requirements elicitation increasingly continuous, but also unstructured and complex, which current agile methods are unable to consider and manage in a systematic and efficient manner.There is also the need to support software evolution by enabling a synergy of stakeholder-driven requirements elicitation and management with data-driven approaches.In this study, we propose extension of agile requirements elicitation by applying situational method engineering.The research is grounded on two studies in the business domains of video games and online banking.
A survey of 3D audio through the browser: practitioner perspectives

This paper examines the current eco-system of tools for implementing dynamic 3D audio through the browser, from the perspective of spatial sound practitioners. It presents a survey of some existing tools to assess usefulness, and ease of use. This takes the forms of case studies, interviews with other practitioners, and initial testing comparisons between the authors. The survey classifies and summarizes their relative advantages, disadvantages and potential use cases. It charts the specialist knowledge needed to employ them or enable others to.The recent and necessary move to online exhibition of works, has seen many creative practitioners grapple with a disparate eco-system of software. Such technologies are diverse in their both their motivations and applications. From formats which overcome the limits of WebGL’s lack of support for Ambisonics, to the creative deployment of Web Audio API (WAA), to third-party tools based on WAA, the field can seem prohibitively daunting for practitioners. The current range of possible acoustic results may be too unclear to justify the learning curve.Through this evaluation of the current available tools, we hope to demystify and make accessible these novel technologies to composers, musicians, artists and other learners, who might otherwise be dissuaded from engaging with this rich territory. This paper is based on a special session at Soundstack 2021.
Removal of self co-articulation and recognition of dynamic hand gestures using deep architectures

Natural gesticulation in-air is multifaceted, as defining a gesture's pattern/size/speed or its stroke information is difficult. The unavailability of vision-based techniques to distinguish the ground truth stroke segments and the intentional movements (self co-articulation) results in capturing everything on the trajectory. But recognizing the gesture patterns (A–Z, a–z, 0–9, 04 operators, and 29 symbols) along with their self co-articulations results in the rise of misclassification. Hence, gestures are separated into 3 sets based on their physical structures using an artificial neural network. Then, set specific pre-processing models are proposed to remove these self co-articulations. As a result, a mean error rate of 0.0112 (ground truth segment removed) and 5.63% of self co-articulations present in the gesture patterns is obtained. A relative improvement of 22% (accuracy — 94.17%) over the existing models is achieved. Then, gestures are clustered into two groups by Mamdani fuzzy interference system using prior stroke information. Using transfer learning, separate pre-trained AlexNet models were utilized to recognize the gesture patterns falling under each group with an average accuracy of 97.05% (precision — 0.9680, recall — 0.9656; F-score — 0.9668). This is a relative improvement of ∼17% over the existing state-of-the-art models in gesture recognition.
Environment-Driven Abstraction Identification for Requirements-Based Testing

Abstractions are significant domain terms that have assisted in requirements elicitation and modeling. To extend the assistance towards requirements validation, we present in this paper an automated approach to identifying the abstractions for supporting requirements-based testing. We select relevant Wikipedia pages to serve as a domain corpus that is independent from any specific software system. We further define five novel patterns based on part-of-speech tagging and dependency parsing, and frame our candidate abstractions in the form of <key, value> pairs for better testability. We evaluate our approach with six software systems in two application domains: Electronic health records and Web conferencing. The results show that our abstractions are more accurate than those generated by two of the state-of-the-art techniques. Initial findings also indicate our abstractions’ capabilities of revealing bugs and matching the environmental assumptions created manually.
Autoguess: A Tool for Finding Guess-and-Determine Attacks and Key Bridges

The guess-and-determine technique is one of the most widely used techniques in cryptanalysis to recover unknown variables in a given system of relations. A subset of the unknown variables is guessed such that the remaining unknowns can be deduced using the relations. Applications include state recovery for stream ciphers and key-bridging in key-recovery attacks on block ciphers. Since the attack complexity depends on the number of guessed variables, it is essential to find small guess bases. In this paper, we present Autoguess, an easy-to-use tool to search for a minimal guess basis. We propose several new modeling techniques to harness SAT/SMT, MILP, and Gröbner basis solvers. We demonstrate their usefulness in guess-and-determine attacks on stream ciphers and block ciphers, as well as finding key-bridges for block ciphers. Moreover, integrating our CP models for the key-bridging technique into the previous CP-based frameworks to search for distinguishers, we propose a unified and general CP model to find key-recovery-friendly distinguishers for both linear and nonlinear key schedules.
One-Shot Voice Conversion For Style Transfer Based On Speaker Adaptation

One-shot style transfer is a challenging task, since training on one utterance makes model extremely easy to over-fit to training data and causes low speaker similarity and lack of expressiveness. In this paper, we build on the recognition-synthesis framework and propose a one-shot voice conversion approach for style transfer based on speaker adaptation. First, a speaker normalization module is adopted to remove speaker-related information in bottleneck features extracted by ASR. Second, we adopt weight regularization in the adaptation process to prevent over-fitting caused by using only one utterance from target speaker as training data. Finally, to comprehensively decouple the speech factors, i.e., content, speaker, style, and transfer source style to the target, a prosody module is used to extract prosody representation. Experiments show that our approach is superior to the state-of-the-art one-shot VC systems in terms of style and speaker similarity; additionally, our approach also maintains good speech quality.
Edge Aware Network for Image Dehazing

The learning-based methods have recently shown their advantages in the image dehazing task. However, most existing learning-based methods do not pay much attention to the restoration in the edges of the hazy image, resulting in the edge blur of the dehazing results. To mitigate this issue, in this letter, we propose a novel Edge Aware Network (EA-Net) for image dehazing, which can simultaneously model edge features and contextual features into a single network for restoring haze-free image with sharp edges. Firstly, we extract the multi-scales contextual features of hazy image by a progressive fusion way. Furthmore, the abundant edge features are inferred by a edge subnetwork with Edge Feature Extraction Module(EFEM). Finally, we present an Edge Attention (EA) mechanism to couple the edge features with contextual features at various resolutions for sufficiently leveraging these complementary features. Due to the rich edge information and feature fusion strategy, the fused features can make the haze-free image to be clearer, especially at the edges, which is very important for the high-level vision tasks. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.
A Mixed-Integer linear programming based training and feature selection method for artificial neural networks using piece-wise linear approximations

Artificial Neural Networks (ANNs) may suffer from suboptimal training and test performance related issues not only because of the presence of high number of features with low statistical contributions but also due to their non-convex nature. This study develops piecewise-linear formulations for the efficient approximation of the non-convex activation and objective functions in artificial neural networks for optimal, global and simultaneous training and feature selection in regression problems. Such formulations include binary variables to account for the existence of the features and piecewise-linear approximations, which in turn, after one exact linearization step, calls for solving a mixed-integer linear programming problem with a global optimum guarantee because of convexity. Suggested formulation is implemented on two industrial case studies. Results show that efficient approximations are obtained through the usage of the method with only a few number of breakpoints. Significant feature space reduction is observed bringing about notable improvement in test accuracy.
A Novel Weakly Supervised Ensemble Learning Framework for Automated Pixel-Wise Industry Anomaly Detection

Automatic anomaly detection has always been a very important and challenging subject, which strongly affects product quality. This paper, proposes a novel weakly supervised ensemble learning based framework for automated pixel-wise anomaly detection, which enables the model to be trained using only a small quantity of labelled samples, instead of hundreds or thousands or more, meanwhile it maintains excellent performance in practical applications. Specifically, first, synthesized defects with random shapes, texture and brightness are injected into fault-free samples for preliminary training. Then homographic adaptation is introduced, and Siamese homography method is employed to further improve the training performance by limiting the repeatability and consistency of results in different Siamese network channels. While in model inference module, a multi-modal boosting method with adaptive weights is utilized instead of prediction on purely original sample images. Experiments are performed on several typical open industry anomaly detection datasets, and experimental results show that our method achieves better performance when compared with other classic superior methods. Typically, the segmentation metric mIOU can reach 85.79%, 78.38% and 76.65% on these datasets, specifically.
Mobile Device Fingerprint Identification Using Gyroscope Resonance

Device fingerprint is information about the target computing device for the purpose of identification. The fingerprinting algorithm that how to assimilate the target into an identifier has been well studied for more than 20 years. However, the recent obfuscation method against device fingerprinting makes collecting this information more difficult. In order to solve these problems, this paper proposes a new type of fingerprinting method that relies on the resonant frequency response of the gyroscope. Our method first generates an ultrasonic as the trigger signal and obtains the sensor’s output through the application program or the web browser as the response. After the sensor output being normalized during the data preprocessing, resonance features are extracted after frequency domain analysis. Finally, the mobile device is identified through these features matching. In the experiment, we test many types of mobile phones to demonstrate that our method is feasible while users are unaware of this process. For instance, we find that some features are stable when the posture of the mobile device or the time change. There are 10 features based on resonance frequency that can help us improve the classification accuracy to 96.5%. The comprehensive experiments demonstrate that our method can effectively distinguish different types of mobile devices. Even if it is the same model of mobile devices with the same type of gyroscopes, this method still has a good performance.
A pricing model for Container-as-a-Service, based on hedonic indices

Container-as-a-service (CaaS) is a business model that facilitates software developers in organizing, running, managing, and deploying applications using container-based virtualization. The CaaS market is a fast-growing one, leading infrastructure-as-a-service (IaaS) providers into offering CaaS solutions built on top of their IaaS platforms. Given the increasing competition among providers, pricing strategies play a key role in determining the business prospects of CaaS. In this work we propose a methodology to analyze the pricing strategies of the providers, based on a hedonic regression. Following that, we also develop a hedonic price index, based on data collected from popular providers, by identifying a wide set of architectural requirements influencing the pricing strategy. We further analyze the correlations between these requirements and define the subset to be included in the regression analysis, interpreting the results in view of the underlying CaaS market characteristics. The model implementation and the data set are freely available on the web under an open-source license.
Divided We Stand, United We Fall: Security Analysis of Some SCA+SIFA Countermeasures Against SCA-Enhanced Fault Template Attacks

Protection against Side-Channel (SCA) and Fault Attacks (FA) requires two classes of countermeasures to be simultaneously embedded in a cryptographic implementation. It has already been shown that a straightforward combination of SCA and FA countermeasures are vulnerable against FAs, such as Statistical Ineffective Fault Analysis (SIFA) and Fault Template Attacks (FTA). Consequently, new classes of countermeasures have been proposed which prevent against SIFA, and also includes masking for SCA protection. While they are secure against SIFA and SCA individually, one important question is whether the security claim still holds at the presence of a combined SCA and FA adversary. Security against combined attacks is, however, desired, as countermeasures for both threats are included in such implementations.
Floodgate

Incast occurs frequently in datacenter networks where a large number of senders send data to a single receiver simultaneously, which makes the last hop the network bottleneck. Incast can hurt flows' performance. However, congestion control protocols are not effective at handling incast. One key insight is that it is too late to handle incast packets after they have already piled up at the last hop. Instead, we should avoid incast as early as possible. Inspired by flood control in Hydrologic Engineering, we propose Floodgate, a novel switch-based per-hop flow control to handle incast. Floodgate is compatible with existing congestion control protocols. We integrate it with practical congestion control approaches such as DCQCN, TIMELY, and HPCC. We evaluate Floodgate both in our implementations and large-scale simulations. Compared with state of the art, Floodgate reduces the buffer occupancy by a factor of 6.6x, as well as the queuing delay. Therefore, the average FCT and tail latency are greatly reduced.