Reviewerly/exHarmony huggingface dataset:
Blockchain State Sharding With Space-Aware Representations

State sharding is a common solution to the scalability problem in blockchain systems, allowing nodes to hold a partial view of the system state. With sharding, the processing of a transaction might not be completed locally within a node and potentially requires involvement of multiple shards. Such cross-shards transactions have a negative impact on system performance and are frequent with traditional state partition solutions which are often based on a simple mapping of data into shards. By locating together parts of the system state accessed by frequent transactions, the amount of cross-shard transactions can be reduced. On the other hand, the representation of such particular mappings can be memory intensive. In this article, we study traffic-aware sharding that can be described in memory-efficient mappings. We first survey existing mapping schemes in common blockchains. We indicate the tradeoff between the size of the mapping of data to shards and the required transaction processing time and suggest algorithms for finding memory-light sharding of low cross-shard rate. We generalize the approach towards an efficient incremental computation of shards and probabilistic representation of the mapping to shards. We examine the efficiency of the solutions and the required frequency of sharding computation based on real information of the Ethereum network.
E-services and M-services Using IoT: An Assessment of the Kingdom of Bahrain

The Kingdom of Bahrain has been reported as the pioneers of the e-government within the Arabian Gulf region. Such particularly is the pride reflective within the healthcare sector. With e-government ranked first among all Arab countries and thirteen worldwide, e-health paved new heights. Its successes further resonated during the COVID-19 era. Various stakeholders in the healthcare sector moved beyond e-services. Thanks to the Internet of Things, have been able to appreciate m-health. It is certain, that the country practiced strategies to assure itself towards a fast-growing Internet users base, and this is particularly evidenced through its citizens and the expat populations. The aim of this study is to explore how the Kingdom of Bahrain was able to succeed in its e-service, e-government, and e-health services to transform from a bureaucratic model to a new IoT based m-health and m-government paradigm. A content analysis of the Kingdom of Bahrain intellectual resources, i.e., their websites as well as journal and conference papers, etc., this chapter extends an overview of how Bahrain was able to transform to an e/m-government and e/m-health paradigm, along with theoretical and practical implication with suggestions for future research are also put forth in this chapter.
A Replication Study on the Usability of Code Vocabulary in Predicting Flaky Tests

Industrial reports indicate that flaky tests are one of the primary concerns of software testing mainly due to the false signals they provide. To deal with this issue, researchers have developed tools and techniques aiming at (automatically) identifying flaky tests with encouraging results. However, to reach industrial adoption and practice, these techniques need to be replicated and evaluated extensively on multiple datasets, occasions and settings. In view of this, we perform a replication study of a recently proposed method that predicts flaky tests based on their vocabulary. We thus replicate the original study on three different dimensions. First, we replicate the approach on the same subjects as in the original study but using a different evaluation methodology, i.e., we adopt a time-sensitive selection of training and test sets to better reflect the envisioned use case. Second, we consolidate the findings of the initial study by building a new dataset of 837 flaky tests from 9 projects in a different programming language, i.e., Python while the original study was in Java, which comforts the generalisability of the results. Third, we propose an extension to the original approach by experimenting with different features extracted from the Code Under Test. We find that a more robust validation consistently decreases performance on the reported results of the original study, but, fortunately, the model remains capable to decently predict flaky tests. We find re-assuring results that the vocabulary-based models can also be used to predict test flakiness in Python. Finally, we find that the information lying in the Code Under Test has a limited impact on the performance of the vocabulary-based models.
Software-Defined Security-by-Contract for Blockchain-Enabled MUD-Aware Industrial IoT Edge Networks

To ensure the proper functioning and performance of Industrial grade Internet of Things devices (IIoT) in Industry 4.0 networks, it is critical to identify the capabilities and malfunctions of their component devices (e.g., sensors, actuators, and controllers) and detect potential misbehavior arising due to cyber-attacks, and misconfiguration. We envision future IoT devices embed behavioral profiles through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Security-by-Contract</i> (S×C) that are easy to validate and verify against network security policies; manufacturers to provide manufacturer usage description (MUD) profiles as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">manifest</i> for the devices to signal to the network what sort of access and network functionality they require to properly function. We design authentication in the IoT onboarding process, employ blockchains to a verifiable and immutable repository to store this network manifests, that is signed and verifiable with S×C based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">smart contracts</i> by the device manufacturer, or industry authority. The integrated framework combines blockchains and S×C security contracts, MUD-based behavioral fingerprinting, and software-defined-networking for managing the security of IIoT ecosystems. Finally, the proposed scheme is validated in a simulated IoT environment on various performance parameters.
Weak Human Preference Supervision for Deep Reinforcement Learning

The current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed preference between pairs of trajectory segments. However, the judgment of preferences between trajectories is not dynamic and still requires human input over thousands of iterations. In this study, we proposed a weak human preference supervision framework, for which we developed a human preference scaling model that naturally reflects the human perception of the degree of weak choices between trajectories and established a human-demonstration estimator through supervised learning to generate the predicted preferences for reducing the number of human inputs. The proposed weak human preference supervision framework can effectively solve complex RL tasks and achieve higher cumulative rewards in simulated robot locomotion-MuJoCo games-relative to the single fixed human preferences. Furthermore, our established human-demonstration estimator requires human feedback only for less than 0.01% of the agent's interactions with the environment and significantly reduces the cost of human inputs by up to 30% compared with the existing approaches. To present the flexibility of our approach, we released a video (https://youtu.be/jQPe1OILT0M) showing comparisons of the behaviors of agents trained on different types of human input. We believe that our naturally inspired human preferences with weakly supervised learning are beneficial for precise reward learning and can be applied to state-of-the-art RL systems, such as human-autonomy teaming systems.
Area and power efficient post-quantum cryptosystem for IoT resource-constrained devices

Internet of Things (IoT) connects a myriad of small devices over a huge network, encompassing many different and varied applications and environments. As the IoT network continues to grow, providing end-to-end security over IoT is becoming a paramount issue. To mitigate existing and future security risks within IoT, two important factors should be considered. First, some resource-constrained edge devices have an insufficient area to contain the security part. Second, the advent of quantum computers threatens the security of current public-key cryptography algorithms. In response to these challenges, lattice-based cryptography (LBC) has emerged as a promising technique for IoT security in the quantum era. The feasibility of LBC integration onto resource-constrained devices has been demonstrated in previous research. Multiplication is the main operation in Ring-BinLWE, a type of LBC. In this paper, a new multiplication method is proposed, which is called In-place modular Reduction and anti-circular Rotation Column-based Multiplication (In-place Rot-Col-Mul), and new Ring-BinLWE architecture is designed. In-place Rot-Col-Mul performs a column-based multiplication in which one rotation is executed per cycle. The design was implemented on TSMC-65nm technology and FPGA platforms. ASIC implementation results show a respective improvement in power and area over the state-of-the-art design by 48.42% and 57.8%, respectively.
Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN

Aircraft is a means of transportation and weaponry, which is crucial for civil and military fields to detect from remote sensing images. However, detecting aircraft effectively is still a problem due to the diversity of the pose, size, and position of the aircraft and the variety of objects in the image. At present, the target detection methods based on convolutional neural networks (CNNs) lack the sufficient extraction of remote sensing image information and the post-processing of detection results, which results in a high missed detection rate and false alarm rate when facing complex and dense targets. Aiming at the above questions, we proposed a target detection model based on Faster R-CNN, which combines multi-angle features driven and majority voting strategy. Specifically, we designed a multi-angle transformation module to transform the input image to realize the multi-angle feature extraction of the targets in the image. In addition, we added a majority voting mechanism at the end of the model to deal with the results of the multi-angle feature extraction. The average precision (AP) of this method reaches 94.82% and 95.25% on the public and private datasets, respectively, which are 6.81% and 8.98% higher than that of the Faster R-CNN. The experimental results show that the method can detect aircraft effectively, obtaining better performance than mature target detection networks.
The unique effects of Covid-19 – A qualitative study of the factors that influence teachers’ acceptance and usage of digital tools

The objective of this study is to examine the factors that influence teachers' acceptance of digital tools for undertaking distance teaching during the Covid-19 pandemic. Based on the variables of the technology acceptance model, we have conducted interviews with 15 secondary school teachers with varying degrees of professional experiences and combinations of subjects, from the federal state of Baden-Wuerttemberg in Germany and analyzed the same. The results indicate that, other than user motivation, three areas, namely "regulations and specifications," "technological infrastructure," and "heterogeneity of students and teachers," affect the adoption of digital tools. The Covid-19 pandemic, which inevitably led teachers to embrace digital tools, positively influenced the perception and immediate usefulness of digital tools. We assert that no other variable would have been able to universally influence technology usage and acceptance to such an extent as to replicate the findings of our study and simultaneously highlight the uniqueness of the current situation and the necessity for examining its impact.
FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation

Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos. Especially, the prediction based approach is one of the most studied methods to detect anomalies by predicting frames that include abnormal events in the test set after learning with the normal frames of the training set. However, a lot of prediction networks are computationally expensive owing to the use of pre-trained optical flow networks, or fail to detect abnormal situations because of their strong generative ability to predict even the anomalies. To address these shortcomings, we propose spatial rotation transformation (SRT) and temporal mixing transformation (TMT) to generate irregular patch cuboids within normal frame cuboids in order to enhance the learning of normal features. Additionally, the proposed patch transformation is used only during the training phase, allowing our model to detect abnormal frames at fast speed during inference. Our model is evaluated on three anomaly detection benchmarks, achieving competitive accuracy and surpassing all the previous works in terms of speed.
A novel self-learning feature selection approach based on feature attributions

Feature selection has shown its effectiveness in improving the accuracy and generalization of machine learning models, especially for those tasks with high-dimensional data. In this article, a novel self-learning feature selection (SLFS) approach based on feature attributions is proposed as a wrapper method, which has higher search efficiency for optimal feature subsets with three main improvements. First, we regard feature selection as a combinatorial optimization problem and propose a unified local search framework for wrapper methods by analyzing meta-heuristic algorithms in feature selection. Second, for the binary search space of feature selection, we propose two types of neighborhood structures, namely, ring-type and line-type structures, for the local search framework. Third, we focus on feature attribution methods, such as SHAP (SHapley Additive exPlanations) (Lundberg & Lee, 2017), which can interpret each feature’s importance to predictions. In each iteration, we adopt SHAP values and other attributes from previous subsets to guide the next selection of new subsets. To validate the performance of our SLFS approach, we collected 16 classification datasets from the UCI repository for comparison with other meta-heuristic wrapper approaches in terms of fitness, accuracy, F1 scores and selection ratios. The experimental results show that the SLFS approach can be used to obtain an optimal subset with fewer iterations and a small population, and SHAP values play a role in improving search efficiency.
Grey Literature in Software Engineering: A critical review

Grey Literature (GL) recently has grown in Software Engineering (SE) research since the increased use of online communication channels by software engineers. However, there is still a limited understanding of how SE research is taking advantage of GL. This research aimed to understand how SE researchers use GL in their secondary studies. We conducted a tertiary study of studies published between 2011 and 2018 in high-quality software engineering conferences and journals. We then applied qualitative and quantitative analysis to investigate 446 potential studies. From the 446 selected studies, 126 studies cited GL but only 95 of those used GL to answer a specific research question representing almost 21% of all the 446 secondary studies. Interestingly, we identified that few studies employed specific search mechanisms and used additional criteria for assessing GL. Moreover, by the time we conducted this research, 49% of the GL URLs are not working anymore. Based on our findings, we discuss some challenges in using GL and potential mitigation plans. In this paper, we summarized the last 10 years of software engineering research that uses GL, showing that GL has been essential for bringing practical new perspectives that are scarce in traditional literature. By drawing the current landscape of use, we also raise some awareness of related challenges (and strategies to deal with them).
RestoreNet-Plus: Image restoration via deep learning in optical synthetic aperture imaging system

The synthetic aperture technology can improve the resolution effectively in the optical imaging system. In fact, the imaging blur, turbulence aberration and noise can affect the imaging quality of optical synthetic aperture imaging system seriously. Several non-blind methods are applied generally to recover the degraded maps with the prior information. However, the restoration effect is not stable enough and satisfactory. As a data-driven approach, the deep learning framework possesses advantages in solving this problem. In this paper we propose an improved network, RestoreNet-Plus, for the image restoration of optical synthetic aperture imaging system. After the proofs of numerical simulation and experiment results, RestoreNet-Plus is a better alternative compared with other methods, owing to its better restoration ability, strong denoising ability and capacity for turbulence correction error.
Differential privacy trajectory data protection scheme based on R-tree

With the popularization of mobile devices with positioning functions, location-based service (LBS) plays a vital role in people's daily lives. The problem of privacy leakage becomes more and more critical. Although the application technology of LBS is developing rapidly, the corresponding privacy protection technology is growing slowly. At present, differential privacy technology has received attention from many researchers, but it is not easy to reasonably apply it to trajectory privacy protection. Most trajectory privacy protection models only focus on the spatial location of mobile users without considering the temporal characteristics of the trajectory, which destroys the spatial–temporal characteristics of the trajectory. Therefore, to beat this difficult problem, a differential privacy trajectory data protection scheme based on R-tree is suggested. Firstly, the trajectory similarity tree structure is proposed on the basis of R-tree index structure to realize the trajectory data's spatial storage and query processing. Secondly, the DPTS-tree (Differential Privacy Trajectory Similarity tree, DPTS-tree) is constructed with differential privacy technology, adding noise to the statistical values of mobile users in the nodes, which can greatly improve the ability to resist arbitrary background knowledge attacks and achieve the purpose of protecting data privacy. Then, to resist other information inference attacks in the trajectory data, when constructing the DPTS-tree, random noise is added to location data and other data in the trajectory. Finally, the algorithm is subjected to consistency constraints to address the problem that the added independent noise may cause data inconsistency. Experiments show that our algorithm can effectively protect sensitive and private information of users and ensure the usability of trajectory data.
Evaluating the Communication Efficiency in Federated Learning Algorithms

In the era of advanced technologies, mobile devices are equipped with computing and sensing capabilities that gather excessive amounts of data. These amounts of data are suitable for training different learning models. Cooperated with Deep Learning (DL) advancements, these learning models empower numerous useful applications, e.g., image processing, speech recognition, healthcare, vehicular network, and many more. Traditionally, Machine Learning (ML) approaches require data to be centralised in cloud-based data-centres. However, this data is often large in quantity and privacy-sensitive, preventing logging into these data-centres for training the learning models. In turn, this results in critical issues of high latency and communication inefficiency. Recently, in light of new privacy legislation in many countries, the concept of Federated Learning (FL) has been introduced. In FL, mobile users are empowered to learn a global model by aggregating their local models without sharing the privacy-sensitive data. Usually, these mobile users have slow network connections to the data-centre where the global model is maintained. Moreover, in a complicated and extensive scale network, heterogeneous devices with various energy constraints are involved. This raises the challenge of communication cost when implementing FL at a large scale. To this end, in this research, we begin with the fundamentals of FL, and then we highlight the recent FL algorithms and evaluate their communication efficiency with detailed comparisons. Furthermore, we propose a set of solutions to alleviate the existing FL problems from a communication perspective and a privacy perspective.
Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems

As an important means to solve information overload, recommender systems have been widely applied in many fields, such as e-commerce and advertising. However, recent studies have shown that recommender systems are vulnerable to poisoning attacks; that is, injecting a group of carefully designed user profiles into the recommender system can severely affect recommendation quality. Despite the development from shilling attacks to optimization-based attacks, the imperceptibility and harmfulness of the generated data in most attacks are arduous to balance. To this end, we propose a triple adversarial learning for influence based poisoning attack (TrialAttack), a flexible end-to-end poisoning framework to generate non-notable and harmful user profiles. Specifically, given the input noise, TrialAttack directly generates malicious users through triple adversarial learning of the generator, discriminator, and influence module. Besides, to provide reliable influence for TrialAttack training, we explore a new approximation approach for estimating each fake user's influence. Through theoretical analysis, we prove that the distribution characterized by TrialAttack approximates to the rating distribution of real users under the premise of performing an efficient attack. This property allows the injected users to attack in an unremarkable way. Experiments on three real-world datasets show that TrialAttack's attack performance outperforms state-of-the-art attacks, and the generated fake profiles are more difficult to detect compared to baselines.