Miracle-dz/UniFAR-FTUs huggingface dataset:
Addressing Performance Regressions in DevOps: Can We Escape from System Performance Testing?

Performance regression is an important type of performance issue in software systems. It indicates that the performance of the same features in the new version of the system becomes worse than that of previous versions, such as increased response time or higher resource utilization. In order to prevent performance regressions, current practices often rely on conducting extensive system performance testing before releasing the system into production based on the testing results. However, faced with a great demand for resources and time to perform system performance testing, it is often challenging to adopt such approaches to the practice of fast-paced development and release cycles, e.g., DevOps. This thesis focuses on addressing software performance regressions in DevOps without relying on expensive system performance tests. More specifically, I first propose a series of approaches to helping developers detect performance regressions and locate their root causes by only utilizing the readily-available operational data when the software system is running in the field and used by real end users. I then leverage small-scale performance testing and architectural modeling to estimate the impact of source code changes on the end-to-end performance of the system in order to detect performance regres-sions early in the software development phase. Through various case studies on open-source projects and successful adoptions by our industrial research collaborator, we expect that our study will provide helpful insights for researchers and practitioners who are interested in addressing performance regressions in DevOps without expensive system performance testing,
Multi-Disturbances Suppression for Permanent Magnet Synchronous Motor based on Active Disturbance Rejection Model Predictive Control

To solve the multi-disturbances suppression problem of permanent magnet synchronous motor (PMSM) model predictive control (MPC), an improved control method based on active disturbance rejection control (ADRC) and model predictive current control (MPCC) is proposed. First, the external disturbance caused by load mutation in the speed loop and the internal disturbance caused by the parameter sensitivity of the traditional MPCC method is analyzed which pointed out that the parameter mismatch will lead to the predicted current error and inaccurate delay compensation. To improve the disturbance rejection performance of the system, ADRC is introduced into the speed loop to eliminate the external disturbance in the speed loop. In addition, to improve the parameter robustness of MPCC, an inductance disturbance controller is proposed, including an inductance disturbance observer and inductance extraction algorithm, to update the accurate inductance information of the whole control system in real-time. Finally, the simulation results show that this method can improve the disturbances rejection performance by suppressing the external disturbance effectively in the speed loop and improve the parameter robustness by eliminating the influence of inductance parameter mismatch on the control performance, so can achieve comprehensive suppression of external and internal disturbances.
Augmented Reality (AR) a New Technology for Advertising and Marketing Digital Communications

New digital technologies are greatly affecting people’s lives and are dramatically changing marketing operations. Consequently, advertising, promotion and more generally marketing communications going through a major digital metamorphosis. Further, the rise of Virtual Reality (VR), Augmented Reality (AR), digital marketing and advertising breaks the boundary between the physical world and the digital world. By means of these technologies, advertising may achieve a new level of promotion, branding and consumer involvement. Therefore, it deems essential to investigate the relationship between AR advertising and consumer behavior as well as their overall experience with AR. It would be imperative to explore the relationship between this new digital technology and consumers, so as to predict future trends in the development of advertising. This paper set the basis for such an inquiry by proposing a framework for evaluating a new technology such as AR on its applicability to advertising and digital marketing communications.
Power Side-Channel Attacks and Countermeasures on Computation-in-Memory Architectures and Technologies

To overcome the bottleneck of the classical processor-centric architectures, Computation-in-Memory (CiM) is a promising paradigm where operations are performed directly in memory. Recent works propose the use of CiM to accelerate neural networks or hyperdimensional computing, but also for memory encryption solutions. As CiM facilitates the computation in the analog domain and the output is driven through current sensing, CiM could potentially be highly vulnerable to power side-channel attacks. In this work, we analyze the vulnerability for power side-channel attacks in various CiM implementations based on Static Random Access Memory (SRAM) and emerging nonvolatile memristive technologies. Our results show that a side-channel attacker can recover secret data used in an XOR operation with only a few hundred measurements, where CiM architectures based on emerging memristive technologies are more vulnerable than SRAM-based CiM. Therefore, we propose two different types of countermeasures based on hiding and masking, which are tailored to CiM architectures. The efficiency of our proposed countermeasures is shown by both attacks and leakage assessment methodologies using one million measurement traces.
A FNV Based IPv6 Address Autoconfiguration Scheme for Power IoT Sensory Device

The construction of a new power system based on new energy has recently attracted a lot of attention, posing both a significant obstacle and an incredible potential for the advancement of the conventional power grid. Internet of Things (IoT) plays a crucial role in developing smart grid for its enormous benefit in supplying massive data and enabling connectivity for communication among various different types of power equipment. The sensing layer is of huge importance in the multi-layer structure of power IoT, offering data support for the estimation of power consumption, dynamic power allocation and a variety of other services. However, the design of IPv6 address autoconfiguration scheme in the sensing layer is challenging given the large number of sensory devices and the dynamic paradigm for interconnection. In this paper, a novel IPv6 address autoconfiguration scheme for power IoT sensory device is proposed to cope with the aforementioned problem. Utilizing Fowler-Noll-Vo (FNV) hash algorithm, this proposed approach can achieve a relatively low duplicated address rate in a densely concentrated distribution scenario. The feasibility and efficiency of the suggested configuration scheme are evaluated by the simulation results.
Connecting the .dotfiles: Checked-In Secret Exposure with Extra (Lateral Movement) Steps

Personal software configurations, known as dotfiles, are increasingly being shared in public repositories. To understand the security and privacy implications of this phenomenon, we conducted a large-scale analysis of dotfiles repositories on GitHub. Furthermore, we surveyed repository owners to understand their motivations for sharing dotfiles, and their awareness of the security implications. Our mixed-method approach consisted of two parts: (1) We mined 124,230 public dotfiles repositories and inductively searched them for security and privacy flaws. (2) We then conducted a survey of repository owners (n=1,650) to disclose our findings and learn more about the problems and implications. We found that 73.6 % of repositories leak potentially sensitive information, most commonly email addresses (of which we found 1.2 million), but also RSA private keys, API keys, installed software versions, browsing history, and even mail client inboxes. In addition, we found that sharing is mainly ideological (an end in itself) and to show off ("ricing"), in addition to easing machine setup. Most users are confident about the contents of their files and claim to understand the security implications. In response to our disclosures, a small minority (2.2%) will make their repositories private or delete them, but the majority of respondents will continue sharing their dotfiles after taking appropriate actions. Dotfiles repositories are a great tool for developers to share knowledge and communicate – if done correctly. We provide recommendations for users and platforms to make them more secure. Specifically, tools should be used to manage dotfiles. In addition, platforms should work on more sophisticated tests, to find weaknesses automatically and inform the users or control the damage.
Validation of Machine Learning-Based Lane Lines Detection Methods Using Different Datasets

Advanced Driver Assistance System (ADAS) uses various sensors that exist in the vehicle to collect information about the vehicle and its surroundings. One of the most commonly used ADASs is the one designed to recognize driving lane lines. In this paper, analysis, and validation of freely available state-of-the-art deep learning-based lane lines detection (LLD) methods are done using three different well-known datasets: CULane, TuSimple, and LLAMAS. To perform the validation, as part of this research, two converters of lane line label formats were made. Converters allow testing models that have been previously trained on a dataset that uses different lane line label formats so that their performances can be verified on other datasets with different lane line format types. The experimental results show that the adaptability of the model to the change of the dataset depends on the architecture used as the basis of the model for the LLD, and the similarity of the training set of the initially used dataset and the new, i.e. test dataset. All tested models showed shortcomings when tested on images from datasets on which they were not trained, which confirms that there is still no model that we can confidently say achieves high performance in all possible scenarios.
Power scalability of a single-stage Yb-doped superfluorescent fiber source

A high-power all-fiber broadband superfluorescent source is fabricated by using a distributed side-coupled cladding-pumped Yb-doped fiber. It is found experimentally that besides the optical feedback, the active fiber parameters have a significant effect on the power scaling. By optimizing the fiber length, a record amplified spontaneous emission output of 186.3 W is achieved, and the slope efficiency with respect to launched pump power is 78%. The bandwidths are larger than 20 nm. Furthermore, the power scalability of the superfluorescent source is analyzed numerically. The parasitic laser oscillation can be suppressed further with a large core to cladding ratio.
Hephaistos: A Management System for Massive Order Book Data from Multiple Centralized Crypto Exchanges with an Internal Unified Order Book

Offers to buy and sell cryptocurrencies on exchanges are collected in an order book as pairs of amount and price provided with a timestamp. Contrary to tick data, which only reflects t he l ast t ransaction p rice o n a n e xchange, t he order book reflects t he m arket's a ctual p rice i nformation a nd the available volume. Until now, no system has been presented that can capture many different order books across several markets. This paper presents Hephaistos, a system for processing, harmonizing, and storing massive spot order book data from 22 centralized crypto exchanges and 55 currency pairs. After collecting the data, Hephaistos aggregates several order books in a so-called Unified O rder B ook, w hich i s t he f oundation f or a S mart Order Routing algorithm. As a result an order is splitted across several exchanges, which results in a better execution price. As component of a high-frequency trading system, Hephaistos captures 32 % of the total daily spot trading volume. We provide examples with data from two exchanges that show that the Smart Order Routing algorithm significantly r educes t he slippage.
Sensitive and Personal Data: What Exactly Are You Talking About?

Mobile devices are pervasively used for a variety of tasks, including the processing of sensitive data in mobile apps. While in most cases access to this data is legitimate, malware often targets sensitive data and even benign apps collect more data than necessary for their task. Therefore, researchers have proposed several frameworks to detect and track the use of sensitive data in apps, so as to disclose and prevent unauthorized access and data leakage. Unfortunately, a review of the literature reveals a lack of consensus on what sensitive data is in the context of technical frameworks like Android. Authors either provide an intuitive definition or an ad-hoc definition, derive their definition from the Android permission model, or rely on previous research papers which do or do not give a definition of sensitive data. In this paper, we provide an overview of existing definitions of sensitive data in literature and legal frameworks. We further provide a sound definition of sensitive data derived from the definition of personal data of several legal frameworks. To help the scientific community further advance in this field, we publicly provide a list of sensitive sources from the Android framework, thus starting a community project leading to a complete list of sensitive API methods across different frameworks and programming languages.
BAILIF: A Blockchain Agnostic Interoperability Framework

Blockchain technology has the potential to revolutionize the energy sector by enabling peer-to-peer energy trading, demand-side flexibility trading, and renewable energy certificate trading, among other decentralised energy trading use cases. However, the lack of interoperability between blockchain networks and platforms is a significant challenge that leads to data and information silos. To address this challenge, a Blockchain Agnostic Interoperability Framework (BAILIF) is proposed, which provides a decentralized notary service and a cross-chain attestation and verification protocol. BAILIF adheres to the core principles of blockchain, such as decentralization, transparency, and trust, and can be adopted in other decentralized ecosystems where blockchain interoperability is required. A proof of concept for a distributed energy trading application demonstrates the solution's feasibility. The results showed that BAILIF could achieve a throughput of up to 666 transactions per second, indicating its potential to enable seamless data sharing across blockchain platforms and promote the adoption of renewable energy sources.
On Comparing Mutation Testing Tools through Learning-based Mutant Selection

Recently many mutation testing tools have been proposed that rely on bug-fix patterns and natural language models trained on large code corpus. As these tools operate fundamentally differently from the grammar-based traditional approaches, a question arises of how these tools compare in terms of 1) fault detection and 2) cost-effectiveness. Simultaneously, mutation testing research proposes mutant selection approaches based on machine learning to mitigate its application cost. This raises another question: How do the existing mutation testing tools compare when guided by mutant selection approaches? To answer these questions, we compare four existing tools – μBERT (uses pre-trained language model for fault seeding), IBIR (relies on inverted fix-patterns), DeepMutation (generates mutants by employing Neural Machine Translation) and PIT (applies standard grammar-based rules) in terms of fault detection capability and cost-effectiveness, in conjunction with standard and deep learning based mutant selection strategies. Our results show that IBIR has the highest fault detection capability among the four tools; however, it is not the most cost-effective when considering different selection strategies. On the other hand, μBERT having a relatively lower fault detection capability, is the most cost-effective among the four tools. Our results also indicate that comparing mutation testing tools when using deep learning-based mutant selection strategies can lead to different conclusions than the standard mutant selection. For instance, our results demonstrate that combining μBERT with deep learning-based mutant selection yields 12% higher fault detection than the considered tools.
A Survey of Distributed Algorithms for Aggregate Games

Aggregation game is a kind of non-cooperative Nash game, in which the revenue of each player is determined by his behavior and the aggregate function of all players’ behaviors. In recent years, it has been closely linked with large-scale network distributed algorithms, and gradually attracted people’s attention. This paper systematically studies the current achievements of the main distributed algorithms of network aggregation game and prospects the future research.
Attacking Memristor-Mapped Graph Neural Network by Inducing Slow-to-Write Errors

Graph neural networks (GNNs) are becoming popular in various real-world applications. However, hardware-level security is a concern when GNN models are mapped to emerging neuromorphic technologies such as memristor-based crossbars. These security issues can lead to malfunction of memristor-mapped GNNs. We identify a vulnerability of memristor-mapped GNNs and propose an attack mechanism based on the identified vulnerability. The proposed attack tampers memristor-mapped graph-structured data of a GNN by injecting adversarial edges to the graph and inducing slow-to-write errors in crossbars. We show that 10% adversarial edge injection induces 1.11× longer write latency, eventually leading to a 44.33% error in node classification. Experimental results for the proposed attack also show that there is a 5.72× increase in the success rate compared to a software-based baseline.
Analysing Transfer Learning Efficacy with Different Feature Sets for Occupancy Detection

Occupancy detection is one of the key elements in improving the energy performance of buildings. Due to their nature, occupancy detection models could be trained on old building data and adapted to new buildings for faster onboarding. We explore and analyse the transfer learning framework applied to occupancy detection. We use a combination of Long-short Term Memory neural network and convolutional neural network architectures and test the transfer learning framework on three datasets. The results show that the transferred models perform better than non-transferred models in almost all metric and dataset combinations.
High Resolution 3D InISAR Imaging of Space Targets Based on PFA Algorithm with Single Baseline

High resolution Three-Dimensional (3D) image of space targets can be achieved under large relative rotational angle, providing precondition for high-precision recognition of space targets. Under large relative rotational angle, the large Migration Through Resolution Cells (MTRC) and the highly Doppler Frequency Modulation (DFM) arise in the Two-Dimensional (2D) Inverse Synthetic Aperture Radar (ISAR) imagery, which results that the existing 3D Interferometric ISAR (InISAR) imaging methods suffer from poor imaging quality. To address this issue, we propose a high resolution InISAR imaging method under large relative rotational angle with single baseline, which is excellent in providing high resolution 3D image of space targets and simplifying system structure compared with the traditional InISAR image methods. Firstly, Polar Format Algorithm (PFA) is performed to eliminate the impact of MTRC, and the second-order Local Polynomial Fourier Transform (LPFT) is applied to reduce the azimuth defocuing. Then, the cross-range scaling technique is adopted to obtain the accurate vertical and horizontal coordinates of the scatterers. Finally, the height coordinates of the targets can be obtained by interferometric processing. Simulational experiments demonstrate that the proposed method can achieve high resolution 3D imaging for space targets under large relative rotational angle with low hardware complexity.
A Novel Fire Detection Method Based on Deep Neural Network and Image Processing

The development of computer vision and deep neural networks has enabled the accurate classification of objects like fire. Fire can pose a serious threat to humans, so the prevention of fire becomes a major concern for society. In this paper, a trained ResNet-50 model is combined with an RGB-based analysis of fire to achieve higher accuracy. In short, the ResNet-50 model categorizes images quickly, while the RGB model can be adjusted based on the actual environment. They complement each other. In images, the RGB model correctly detects the fire, whereas the ResNet-50 model achieves 96.2% accuracy.
A GAN Based Data Augmentation Method for Road Pothole Detection

The detection accuracy of road potholes is not high due to the small number of positive samples. In order to expand the training dataset of the detection network and improve its detection accuracy, this paper proposes a road pothole data augmentation method combining generative adversarial network and image fusion technology. In this method, the clear forged pothole images with different morphometry are generated separately through SinGAN network, and the pothole image and road image are synthesized by Poisson image fusion. A mask image generation method for Poisson image fusion is also presented to further improve the edge smoothness of the fused part. The results of experiments have shown that the image samples generated by this method can significantly improve the accuracy of the road pothole detection network, and verified the effectiveness of this method.
Dynamic Two-way time transfer between moving platforms for netted radar applications

Bi- and multistatic radar systems have larger sensitivity when compared to the monostatic configuration however, at the expense of increased system complexity. Because the different parts that compose the resulting radar system are not co-located, a synchronisation method for remote nodes is mandatory. For moving platforms, the synchronisation link requires a clock steering algorithm which tolerates ever-changing baselines as well as a method to measure the relative positioning throughout the time between a transmitter-receiver pair. It is intended to research, develop and build a versatile networked radar system which allow moving nodes to take advantage of the fact that its different nodes may adapt their relative geometry to the convenience of the situation. This paper reviews frequency and time transfer methods, and introduce the problematic associated with a dynamic two-way time transfer.
Investigation Of Effective Medium Theory Concerning Applications For Skin Cancer Detection

Skin cancer detection proves to be complicated and highly dependent on the examiner’s skills. Millimeter-wave technologies seem to be a promising aid for the detection of skin cancer. The different water content of the skin area affected by cancer compared to healthy skin changes its reflective property. Due to limited available resources on the dielectric properties of skin cancer, especially in comparison to surrounding healthy skin, accurate simulations and evaluations are quite challenging. Therefore, comparing different results for different approaches and starting points can be difficult. In this paper, the Effective Medium Theory is applied to model skin cancer, which provides permittivity values dependent on the water content.