Reviewerly/exHarmony huggingface dataset:
A benchmark generator framework for evolving variant-rich software

Software often needs to exist in different variants, which account for varying customer requirements, environments, or non-functional aspects, such as energy consumption. Unfortunately, the number of variants can grow exponentially with the number of features. As such, developing and evolving variant-rich systems is challenging, since they do not only evolve "in time" as single systems, but also "in space" with new variants. Fortunately, many different methods and tools for variant-rich systems have been proposed over the last decades, especially in the field of software product line engineering. However, their level of evaluation varies significantly, threatening their relevance for practitioners and that of future research. Many tools have only been evaluated on ad hoc datasets, minimal examples, or unrealistic and limited evolution scenarios, missing large parts of the actual evolution lifecycle of variant-rich systems. Our long-term goal is to provide benchmarks to increase the maturity of evaluation of methods and tools for evolving variant-rich systems. However, providing manually curated and sufficiently detailed benchmarks that cover the whole evolution lifecycle of variant-rich systems is challenging. We present the framework vpbench, which simulates the evolution of a variant-rich system and thereby generates an evolution enriched with metadata explaining the evolution. The generated benchmarks, i.e., the evolution histories and metadata, can serve as ground truth to check the results of tools applied on it. We formalize the claims we make about the generator and the generated benchmarks as requirements. The design of vpbench comprises modular generators and evolution operators that automatically evolve real codebases. We implement simple and advanced evolution operators—e.g., relying on code transplantation to incorporate features from real projects. We demonstrate how vpbench addresses its claimed requirements, also considering multiple degrees of realism, extensibility and language-independence of the generated benchmarks.
Real-time propeller fault detection for multirotor drones based on vibration data analysis

This article presents a Fault Detection (FD) method to deal with propeller faults on multirotor drones in real-time. Several solutions have been proposed in the literature, however, they depend on additional sensors and/or dedicated hardware to deal with heavy computational complexity. So, they cannot be implemented in off-the-shelf commercial devices, i.e., without the aid of additional on-board sensors and/or extra computational power. The proposed method, instead, requires the on-board Inertial Measurement Unit (IMU) data only: by combining Finite Impulse Response (FIR), together with sparse classifiers, only a subset of the features is actually needed online and the FD is thus feasible in real-time. Design and tests are based on real flight data from a hexarotor, equipped with a conventional ArduPilot-based controller. The classification accuracy in testing is up to 93.37% (98.21%) with a binary tree (Linear Support Vector Machine (LSVM)). Moreover, the space and time complexity of the proposed method is low: on a PixHawk Cube flight controller, it requires less than 2% of the cycle time, and can then run in real-time. Finally, the proposed fault detection solution is model-free and it can be easily generalized to other multirotor vehicles.
Synthetic data generation method for data-free knowledge distillation in regression neural networks

Knowledge distillation is the technique of compressing a larger neural network, known as the teacher, into a smaller neural network, known as the student, while still trying to maintain the performance of the larger neural network as much as possible. Existing methods of knowledge distillation are mostly applicable for classification tasks. Many of them also require access to the data used to train the teacher model. To address the problem of knowledge distillation for regression tasks under the absence of original training data, previous work has proposed a data-free knowledge distillation method where synthetic data are generated using a generator model trained adversarially against the student model. These synthetic data and their labels predicted by the teacher model are then used to train the student model. In this study, we investigate the behavior of various synthetic data generation methods and propose a new synthetic data generation strategy that directly optimizes for a large but bounded difference between the student and teacher model. Our results on benchmark and case study experiments demonstrate that the proposed strategy allows the student model to learn better and emulate the performance of the teacher model more closely.
Literacy, profile, and determinants of Bitcoin, Ethereum, and Litecoin: Survey results

AbstractAbstractThis study attempts to assess individuals' Bitcoin, Ethereum and Litecoin literacy, and then identifies different profiles based on the literacy levels of these cryptocurrencies, and further investigates whether financial literacy explains Bitcoin, Ethereum and Litecoin literacy. Using a sample from higher learning institutions in Malaysia, the results indicate that individuals exhibit higher knowledge on Bitcoin compared to Ethereum and Litecoin. Cluster analysis revealed two clusters, where the high-cryptocurrency-literacy cluster (high in Bitcoin, Ethereum, and Litecoin literacy) has a distinct demographic profile and higher financial literacy than the low-cryptocurrency-literacy cluster (low in Bitcoin, Ethereum, and Litecoin literacy). Financial literacy is shown to explain Bitcoin and Ethereum literacy, but not Litecoin literacy.Keywords: Bitcoin literacycryptocurrency literacyEthereum literacyfinancial literacyLitecoin literacyMalaysia Disclosure statementNo potential conflict of interest was reported by the author(s).
MUSEDA: Multilingual Unsupervised and Supervised Embedding for Domain Adaption

Based on the assumption of isomorphism, approaches for generating high-quality, low-cost multilingual word embeddings (MLWEs) have been critical mechanisms for facilitating knowledge transfer between languages, notably between resource-rich and resource-lean languages. However, recent studies have discovered that the isomorphism assumption is not widely applicable, and that approaches based on this assumption face significant limitations, leading to stagnation in multilingual natural language processing (MNLP). Instead of pursuing the higher quality of MLWEs, we propose MUSEDA (Multilingual Unsupervised and Supervised Embedding for Domain Adaption), a framework for building multilingual word embeddings for domain transfer learning, including pivot features (domain-keyword) mining and weighted embedding alignment in supervised or unsupervised methods. Without using any additional knowledge and parallel data, pivot words can be mined from current data and introduced into the embedding alignment as the weight factor. We applied our framework for real-world tasks from different domains: sentiment classification, news categorization, and named entity recognition on lean-source languages, including Arabic, Vietnamese, etc., and the experimental results demonstrate that the proposed method has surpasses the baseline approaches. In addition, to alleviate the insufficient corpus in certain domains of a single source language, we propose a many-to-many (M2M) mode to further enhance experimental performance by integrating multi-source language into a large common space.
A novel 2+1 three-dimensional measurement for temporal phase unwrapping

A novel 2+1 three-dimensional (3D) measurement for temporal phase unwrapping is proposed. While two high-frequency complimentary sinusoidal fringes and one low-frequency sinusoidal fringe are projected onto the measured object respectively, the corresponding deformed patterns can be captured. Then the high-frequency wrapped phase can be extracted from the two high-frequency complimentary deformed patterns by high-accuracy computer-generated moiré profilometry (HCGMP). Due to its shared direct current (DC) component with the high-frequency deformed pattern, the low-frequency wrapped phase can still be extracted from the only one low-frequency deformed pattern by HCGMP. Because the extracted wrapped phase excludes the phase contribution of the reference plane, a sufficiently high low-frequency fringe projection can also make the low-frequency wrapped phase become its continuous phase without phase unwrapping. And then, the high-frequency wrapped phase is unwrapped, guided by the low-frequency unwrapped phase. Finally, the 3D profile of the measured object can be reconstructed successfully. Compared to the conventional 2+1 algorithm, the proposed algorithm can accomplish temporal phase unwrapping and improve the 3D measuring accuracy. It has a good application prospect in real-time 3D measurement. The experiment results show the feasibility and effectiveness of the proposed algorithm.
Optimized laser models with Heisenberg-limited coherence and sub-Poissonian beam photon statistics

Recently it has been shown that it is possible for a laser to produce a stationary beam with a coherence (quantified as the mean photon number at spectral peak) which scales as the fourth power of the mean number of excitations stored within the laser, this being quadratically larger than the standard or Schawlow-Townes limit [1]. Moreover, this was analytically proven to be the ultimate quantum limit (Heisenberg limit) scaling under defining conditions for CW lasers, plus a strong assumption about the properties of the output beam. In Ref. [2], we show that the latter can be replaced by a weaker assumption, which allows for highly sub-Poissonian output beams, without changing the upper bound scaling or its achievability. In this Paper, we provide details of the calculations in Ref. [2], and introduce three new families of laser models which may be considered as generalizations of those presented in that work. Each of these families of laser models is parameterized by a real number, $p$, with $p=4$ corresponding to the original models. The parameter space of these laser families is numerically investigated in detail, where we explore the influence of these parameters on both the coherence and photon statistics of the laser beams. Two distinct regimes for the coherence may be identified based on the choice of $p$, where for $p>3$, each family of models exhibits Heisenberg-limited beam coherence, while for $p<3$, the Heisenberg limit is no longer attained. Moreover, in the former regime, we derive formulae for the beam coherence of each of these three laser families which agree with the numerics. We find that the optimal parameter is in fact $p\approx4.15$, not $p=4$.
Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques

Heart disease remains the major cause of death, despite recent improvements in prediction and prevention. Risk factor identification is the main step in diagnosing and preventing heart disease. Automatically detecting risk factors for heart disease in clinical notes can help with disease progression modeling and clinical decision-making. Many studies have attempted to detect risk factors for heart disease, but none have identified all risk factors. These studies have proposed hybrid systems that combine knowledge-driven and data-driven techniques, based on dictionaries, rules, and machine learning methods that require significant human effort. The National Center for Informatics for Integrating Biology and Beyond (i2b2) proposed a clinical natural language processing (NLP) challenge in 2014, with a track (track2) focused on detecting risk factors for heart disease risk factors in clinical notes over time. Clinical narratives provide a wealth of information that can be extracted using NLP and Deep Learning techniques. The objective of this paper is to improve on previous work in this area as part of the 2014 i2b2 challenge by identifying tags and attributes relevant to disease diagnosis, risk factors, and medications by providing advanced techniques of using stacked word embeddings. The i2b2 heart disease risk factors challenge dataset has shown significant improvement by using the approach of stacking embeddings, which combines various embeddings. Our model achieved an F1 score of 93.66% by using BERT and character embeddings (CHARACTER-BERT Embedding) stacking. The proposed model has significant results compared to all other models and systems that we developed for the 2014 i2b2 challenge.
Review and Comparison of US, EU, and UK Regulations on Cyber Risk/Security of the Current Blockchain Technologies: Viewpoint from 2023

Abstract The first cryptocurrency was invented in 2008/09, but the Blockchain-Web3 concept is still in its infancy, and the cyber risk is constantly changing. Cybersecurity should also be adapting to these changes to ensure security of personal data and continuation of operations. This article starts with a comparison of existing cybersecurity standards and regulations from the National Institute of Standards and Technology (NIST) and the International Organisation for Standardisation (ISO)—ISO27001, followed by a discussion on more specific and recent standards and regulations, such as the Markets in Crypto-Assets Regulation (MiCA), Committee on Payments and Market Infrastructures and the International Organisation of Securities Commissions (CPMI-IOSCO), and more general cryptography (and post-quantum cryptography), in the context of cybersecurity. These topics are followed up by a review of recent technical reports on cyber risk/security and a discussion on cloud security questions. Comparison of Blockchain cyber risk is also performed on the recent EU standards on cyber security, including European Cybersecurity Certification Scheme (EUCS)—cloud, and US standards—The National Vulnerability Database (NVD) Common Vulnerability Scoring System (CVSS). The study includes a review of Blockchain endpoint security, and new technologies e.g., IoT. The research methodology applied is a review and case study analysing secondary data on cybersecurity. The research significance is the integration of knowledge from the United States (US), the European Union (EU), the United Kingdom (UK), and international standards and frameworks on cybersecurity that can be alighted to new Blockchain projects. The results show that cybersecurity standards are not designed in close cooperation between the two major western blocks: US and EU. In addition, while the US is still leading in this area, the security standards for cryptocurrencies, internet-of-things, and blockchain technologies have not evolved as fast as the technologies have. The key finding from this study is that although the crypto-market has grown into a multi-trillion industry, the crypto-market has also lost over 70% since its peak, causing significant financial loss for individuals and cooperation’s. Despite this significant impact to individuals and society, cybersecurity standards and financial governance regulations are still in their infancy, specifically in the UK.
A Novel Discrete Differential Evolution with Varying Variables for the Deficiency Number of Mahjong Hand

The deficiency number of one hand, i.e., the number of tiles needed to change in order to win, is an important factor in the game Mahjong, and plays a significant role in the development of artificial intelligence (AI) for Mahjong. However, it is often difficult to compute due to the large amount of possible combinations of tiles. In this paper, a novel discrete differential evolution (DE) algorithm is presented to calculate the deficiency number of the tiles. In detail, to decrease the difficulty of computing the deficiency number, some pretreatment mechanisms are first put forward to convert it into a simple combinatorial optimization problem with varying variables by changing its search space. Subsequently, by means of the superior framework of DE, a novel discrete DE algorithm is specially developed for the simplified problem through devising proper initialization, a mapping solution method, a repairing solution technique, a fitness evaluation approach, and mutation and crossover operations. Finally, several experiments are designed and conducted to evaluate the performance of the proposed algorithm by comparing it with the tree search algorithm and three other kinds of metaheuristic methods on a large number of various test cases. Experimental results indicate that the proposed algorithm is efficient and promising.
Bitcoin: a new proof-of-work system with reduced variance

Abstract Since its inception, bitcoin has used the popular consensus protocol proof-of-work (PoW). PoW has a well-known flaw: it distributes all rewards to a single miner (or pool) who inserts a new block. Consequently, the variance of rewards and the mining enterprise risk are extremely high. In 2016, Shi proposed addressing this problem with a theoretical algorithm. We introduce an easily-implemented PoW variant that improves Shi’s idea. The network must not find a single nonce but a few to insert a block. This simple change allows for a fairer distribution of rewards and also has the effect of regularizing the insertion time of blocks. This method would facilitate the emergence of small pools or autonomous miners.
Steganographic Method in Selected Areas of the Stego-Carrier in the Spatial Domain

The main goal of this paper is the proposal of a key-based steganographic system in which the ratio of capacity and image quality metrics that represents the stego object while reducing the detectability of hidden content was improved. The main contribution of the proposed steganographic system is a new algorithm for selecting stego areas. The area selection algorithm is based on clustering the pixels of the cover object into a predetermined number of clusters. The goal of this selection of areas (clusters) is to group as many homogeneous parts of the image as possible in order to cover these areas with as few rectangular shapes as possible. Since the data on the defined rectangles represent the key of the system, the capacity of the additional secret channel is minimized in this way. On the obtained stego-carriers, an embedding of test random content is performed in order to estimate its detectability. By combining the proposed area selection method with the Minimal Decimal Difference steganographic method, a system was created with an optimal trade-off between detectability of secret content, quality and capacity of the carrier, and the length of the stego-key. Finally, a comparison of the obtained results with relevant adaptive steganographic methods is presented. The proposed concept obtains its verification in one practical system for secure file transfer of controlled cryptographic strength.
A probabilistic trust model for cloud services using Bayesian networks

With the industry’s fast adoption of cloud computing, it’s been hard to keep trust between cloud service providers and people who use their services. In fact, trust management in cloud computing has become very challenging given the urgent need for cloud service requesters to choose efficient, trustworthy, and non-risky services. One of the most important factors that can be considered in the applicant’s trust or distrust of a service is the different quality of services related to the service. Therefore, approaches are needed to assess the trustworthiness of cloud services with respect to their quality of service (QoS). Given the uncertainty that exists for cloud services, it is more realistic to model their QoS parameters as random variables and also consider different dependencies between them. In this paper, a new trust model for cloud services is proposed using a Bayesian network, which is a probabilistic graphical model that can be used as one of the best methods to control uncertainty. Using Bayesian networks makes it possible to infer more accurate QoS values, which leads to the selection of highly trustworthy services by several cloud service requesters. To evaluate the proposed trust model, experimental results are performed using a real-world cloud service quality feedback, collected by the $$CloudArmor$$ project. The results of the experiments show that the proposed trust model is highly accurate and significantly reduces the estimation error.
A systematic review of methods for aligning, mapping, merging taxonomies in information sciences

Purpose The purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics. Design/methodology/approach The authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on “taxonomies.” Findings They synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions. Research limitations/implications The main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research. Originality/value There is no existing comprehensive review on the alignment of “taxonomies”. Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.
Side-channel Elimination via Partial Control-flow Linearization

Partial control-flow linearization is a code transformation conceived to maximize work performed in vectorized programs. In this article, we find a new service for it. We show that partial control-flow linearization protects programs against timing attacks. This transformation is sound: Given an instance of its public inputs, the partially linearized program always runs the same sequence of instructions, regardless of secret inputs. Incidentally, if the original program is publicly safe, then accesses to the data cache will be data oblivious in the transformed code. The transformation is optimal: Every branch that depends on some secret data is linearized; no branch that depends on only public data is linearized. Therefore, the transformation preserves loops that depend exclusively on public information. If every branch that leaves a loop depends on secret data, then the transformed program will not terminate. Our transformation extends previous work in non-trivial ways. It handles C constructs such as “goto,” “break,” “switch,” and “continue,” which are absent in the FaCT domain-specific language (2018). Like Constantine (2021), our transformation ensures operation invariance but without requiring profiling information. Additionally, in contrast to SC-Eliminator (2018) and Lif (2021), it handles programs containing loops whose trip count is not known at compilation time.