Reviewerly/exHarmony huggingface dataset:
Anomaly detection of bridge health monitoring data based on KNN algorithm

Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on KNN distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the abnormal mode according to the KNN method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data.
EspialCog: General, Efficient and Robust Mobile User Implicit Authentication in Noisy Environment

Mobile authentication is a fundamental factor in the protection of user’s private resources. In recent years, motion sensor-based biometric authentication has been widely used for privacy-preserving. However, it faces with the problems including low data collection efficiency, insufficient authentication scenario coverage rate, weak de-noising ability, and poor robustness of models, rendering existing methods difficult to meet the security, privacy, and usability requirements jointly in the real-world scenario. To overcome these difficulties, we propose a system called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EspialCog</small> , which is able to 1) collect the sensor data embedded in mobile devices self-adaptively, unobtrusively and efficiently through the evolutionary stable participation game mechanism (ESPGM) with a high scenario coverage rate; 2) minimize noise from collected data by analyzing three types of abnormalities; and 3) authenticate the ownership of mobile devices in real-time by adopting optimized LSTM model with an enhanced stochastic gradient descent (SGD) algorithm. The simulation experiment on 6000 users shows that the efficiency and coverage rates increase dramatically by deploying our ESPGM. Moreover, we conduct experiments on a large-scale real-world noisy dataset with 1513 users and two other small pure real-world datasets. The experimental results show the high accuracy and favorable robustness of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EspialCog</small> in the noisy environment.
Cost-efficient Distributed optimization In Machine Learning Over Wireless Networks

This paper addresses the problem of distributed training of a machine learning model over the nodes of a wireless communication network. Existing distributed training methods are not explicitly designed for these networks, which usually have physical limitations on bandwidth, delay, or computation, thus hindering or even blocking the training tasks. To address such a problem, we consider a general class of algorithms where the training is performed by iterative distributed computations across the nodes. We assume that the nodes have some background traffic and communicate using the slotted-ALOHA protocol. We propose an iteration-termination criterion to investigate the trade-off between achievable training performance and the overall cost of running the algorithms. We show that, given a total running budget, the training performance becomes worse as either the background communication traffic or the dimension of the training problem increases. We conclude that a co-design of distributed optimization algorithms and communication protocols is essential for the success of machine learning over wireless networks and edge computing.
Inferring structure and parameters of dynamic system models simultaneously using swarm intelligence approaches

Abstract Inferring dynamic system models from observed time course data is very challenging compared to static system identification tasks. Dynamic system models are complicated to infer due to the underlying large search space and high computational cost for simulation and verification. In this research we aim to infer both the structure and parameters of a dynamic system simultaneously by particle swarm optimization (PSO) improved by efficient stratified sampling approaches. More specifically, we enhance PSO with two modern stratified sampling techniques, i.e., Latin hyper cube sampling (LHS) and Latin hyper cube multi dimensional uniformity (LHSMDU). We propose and evaluate two novel swarm-inspired algorithms, LHS-PSO and LHSMDU-PSO, which can be used particularly to learn the model structure and parameters of complex dynamic systems efficiently. The performance of LHS-PSO and LHSMDU-PSO is further compared with the original PSO and genetic algorithm (GA). We chose real-world cancer biological model called Kinetochores to asses the learning performance of LHSMDU-PSO and LHS-PSO in comparison with GA and the original PSO. The experimental results show that LHSMDU-PSO can find promising models with reasonable parameters and structure, and it outperforms LHS-PSO, PSO, and GA in our experiments.
Large-Scale Data Storage Scheme in Blockchain Ledger Using IPFS and NoSQL

The future applications of blockchain are expected to serve millions of users. To provide variety of services to the users, using underlying technology has to consider large-scale storage and assessment behind the scene. Most of the current applications of blockchain are working either on simulators or via small blockchain network. However, the storage issue in the real world is unpredictable. To address the issue of large-scale data storage, the authors have introduced the data storage scheme in blockchain (DSSB). The storage model executes behind the blockchain ledger to store large-scale data. In DSSB, they have used hybrid storage model using IPFS and MongoDB(NoSQL) in order to provide efficient storage for large-scale data in blockchain. In this storage model, they have maintained the content-addressed hash of the transactions on blockchain network to ensure provenance. In DSSB, they are storing the original data (large-scale data) into MongoDB and IPFS. The DSSB model not only provides efficient storage of large-scale data but also provides storage size reduction of blockchain ledger.
iScanU: A Portable Scanner for Undocumented Instructions on RISC Processors

Undocumented and faulty CPU instructions can cause undefined behavior and system instability, impairing software efforts such as OS crash recovery and resilience, and system security. Although often not considered, the identification of such undocumented instructions is critical. We present a portable RISC instruction scanner that is able to search for undocumented instructions on a wide range of RISC architectures, empowering users to verify the reliable and secure operation of their systems. We propose two methods to look for undocumented instructions. Both attempt to execute a single instruction word in a controlled manner, regaining control afterwards. Subsequently, we determine if the instruction word is considered valid by the processor, comparing this result to the processor's ISA specification. Our prototype scanner can scan multiple ARMv8 and RISC-V systems. Various inconsistencies were discovered in the QEMU emulator and disassemblers used as ground truth. Furthermore, we found an undocumented instruction on a RISC-V chip.
The Ångström–Prescott Regression Coefficients for Six Climatic Zones in South Africa

The South African Weather Service (SAWS) manages an in situ solar irradiance radiometric network of 13 stations and a very dense sunshine recording network, located in all six macroclimate zones of South Africa. A sparsely distributed radiometric network over a landscape with dynamic climate and weather shifts is inadequate for solar energy studies and applications. Therefore, there is a need to develop mathematical models to estimate solar irradiation for a multitude of diverse climates. In this study, the annual regression coefficients, a and b, of the Ångström–Prescott (AP) model, which can be used to estimate global horizontal irradiance (GHI) from observed sunshine hours, were calibrated and validated with observed station data. The AP regression coefficients were calibrated and validated for each of the six macroclimate zones of South Africa using the observation data that span 2013 to 2019. The predictive effectiveness of the calibrated AP model coefficients was evaluated by comparing estimated and observed daily GHI. The maximum annual relative Mean Bias Error (rMBE) was 0.371%, relative Mean Absolute Error (rMAE) was 0.745%, relative Root Mean Square Error (rRMSE) was 0.910%, and the worst-case correlation coefficient (R2) was 0.910. The statistical validation metrics results show that there is a strong correlation and linear relation between observed and estimated GHI values. The AP model coefficients calculated in this study can be used with quantitative confidence in estimating daily GHI data at locations in South Africa where daily observation sunshine duration data are available.
Novel chaotic random memory indexing steganography on FPGA

Data security has been a major concern for all means of communication lately. As Steganography offers such a unique form of securing data, in this paper, two novel image steganography algorithms are proposed based on generalized chaotic maps. Only one chaotic map is employed in the first algorithm and used for one dimensional memory indexing, while two maps are employed in the second algorithm for two dimensional memory indexing. The pseudorandom numbers used for the cover image pixel’s position selection are the maps' iterative outputs. The generalization technique of logistic maps is employed adding extra degrees of freedom for the design of different chaotic behaviors, thus enhancing the security levels of the proposed algorithms. Four different image sets are employed for methods' validation. Several performance metrics such as Peak Signal to Noise Ratio (PSNR), the Mean Squared Error (MSE), the Structural Similarity Index (SSIM), Normalized Cross Correlation (NCC) and the Image Fidelity (IF) are employed to compare between both methods. The high efficiency of the proposed algorithms compared to other work in literature is validated. The hardware designs of the two algorithms are implemented on Xilinx Virtex 7 FPGA board, achieving 350 MHz frequency.
Proof of Game (PoG): A Proof of Work (PoW)’s Extended Consensus Algorithm for Healthcare Application

Advancement in blockchain technologies during the past decade has attracted tremendous interests from academia, research community and the industry. A blockchain network is a peer-to-peer, decentralized and immutable distributed ledger system for transactional records. With an increase in the number of blockchain-based applications, it becomes a powerful technology for decentralized data processing and consensus mechanisms based blockchain networks. In this work, a single- and multiplayer bit challenging and incentivized consensus mechanisms for blockchain networks are used in proposing a "proof-of-game (PoG)" protocol for resource variant blockchain networks. Bit verifier PoG is designed to be memory dependent and CPU independent mechanism for time efficiency and resource independence. In results, it is observed that the number of blocks mined using this protocol is proportional to the number of participants associated with blocks. Further, it is observed that the priority of a blockchain increases exponentially with an increase in the number of blocks mined, and the number of blocks mined decreases exponentially with an increase in computational challenge.
Hierarchy-Based File Fragment Classification

File fragment classification is an essential problem in digital forensics. Although several attempts had been made to solve this challenging problem, a general solution has not been found. In this work, we propose a hierarchical machine-learning-based approach with optimized support vector machines (SVM) as the base classifiers for file fragment classification. This approach consists of more general classifiers at the top level and more specialized fine-grain classifiers at the lower levels of the hierarchy. We also propose a primitive taxonomy for file types that can be used to perform hierarchical classification. We evaluate our model with a dataset of 14 file types, with 1000 fragments measuring 512 bytes from each file type derived from a subset of the publicly available Digital Corpora, the govdocs1 corpus. Our experiment shows comparable results to the present literature, with an average accuracy of 67.78% and an F1-measure of 65% using 10-fold cross-validation. We then improve on the hierarchy and find better results, with an increase in the F1-measure of 1%. Finally, we make our assessment and observations, then conclude the paper by discussing the scope of future research.
Research on fuzzy English automatic recognition and human-computer interaction based on machine learning

Fuzzy English recognition is affected by many factors, which leads to certain accuracy problems in intelligent recognition results. In order to improve the automatic recognition efficiency of fuzzy English, based on machine learning technology, this study constructs a neural network model. At the same time, this paper analyzes the research status and existing problems of handwritten character recognition, analyzes the model, and adopts multiple modules for automatic English recognition. In addition, the system is built on the basis of algorithms and model support, which makes fuzzy English recognition intelligent. Finally, in order to study the algorithm and model performance, the fuzzy English recognition is carried out through experiments. The research shows that the model constructed in this paper has certain recognition effect, which can be applied to practice, and can provide theoretical reference for subsequent related research.
ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification

Identifying prescription medications is a frequent task for patients and medical professionals; however, this is an error-prone task as many pills have similar appearances (e.g. white round pills), which increases the risk of medication errors. In this paper, we introduce ePillID, the largest public benchmark on pill image recognition, composed of 13k images representing 8184 appearance classes (two sides for 4092 pill types). For most of the appearance classes, there exists only one reference image, making it a challenging low-shot recognition setting. We present our experimental setup and evaluation results of various baseline models on the benchmark. The best baseline using a multi-head metric-learning approach with bilinear features performed remarkably well; however, our error analysis suggests that they still fail to distinguish particularly confusing classes.
Using a Pre-Trained Language Model for Medical Named Entity Extraction in Chinese Clinic Text

The implementation of name entity recognition (NER) in Chinese clinic text is challenging. These methods have several limitations, such as the complexity of the medical text structure, the vast difference in entity length, and identical entities with different entity categories in different contexts. To address these problems, we propose a combination model of both pre-trained bi-directional long short-term memory (Bi- LSTM) and the conditional random field (CRF) model. Due to the specification of medical texts, we do not employ Chinese word segmentation tools. A character-level feature is introduced as an input feature, which is subsequently mapped into char embeddings by using an embedding layer of the bi-directional encoder representation from transformers (BERT) model. A BiLSTM layer and a CRF are utilized to encode the char embeddings and output the final label. The experiments are conducted with CNMER2019 to evaluate the performance and compared with several previous models. The results show that the proposed model outperformed other models and achieved better performance with NER in Chinese clinic text.
The sociocultural dimension of the Software Process Improvement Manifesto: Pilot validation by experts

Abstract The Software Process Improvement (SPI) Manifesto is based on three basic values: people, business focus and organisational change underpinning the philosophy of SPI. In turn, these values bring up to date certain SPI principles serving as a foundation for action in software development. The authors of this paper carried out a pilot expert validation of the sociocultural dimension of the sociocultural, technical, economic, environmental, political, legal, ethical and demographic analysis of the SPI Manifesto. Further, the authors report on the rationale and results of the pilot validation of both the survey instrument and the qualitative responses generated by the field experts, targeting to enlighten and reinforce the importance of the sociocultural dimension of the SPI Manifesto in research and development. The related literature review findings and the pilot research study strengthen this target. The pilot study with experts in particular provided stronger indications that the sociocultural dimension is considered of high importance by between 62% and 88% of the respondents, who were IT and computing professionals and software practitioners from academia and industry.
Assembly or Optimized C for Lightweight Cryptography on RISC-V?

A major challenge when applying cryptography on constrained environments is the trade-off between performance and security. In this work, we analyzed different strategies for the optimization of several candidates of NIST's lightweight cryptography standardization project on a RISC-V architecture. In particular, we studied the general impact of optimizing symmetric-key algorithms in assembly and in plain C. Furthermore, we present optimized implementations, achieving a speed-up of up to 81% over available implementations, and discuss general implementation strategies.
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration

We undertake a precise study of the asymptotic and non-asymptotic propertiesof stochastic approximation procedures with Polyak-Ruppert averaging forsolving a linear system $\bar{A} \theta = \bar{b}$. When the matrix $\bar{A}$is Hurwitz, we prove a central limit theorem (CLT) for the averaged iterateswith fixed step size and number of iterations going to infinity. The CLTcharacterizes the exact asymptotic covariance matrix, which is the sum of theclassical Polyak-Ruppert covariance and a correction term that scales with thestep size. Under assumptions on the tail of the noise distribution, we prove anon-asymptotic concentration inequality whose main term matches the covariancein CLT in any direction, up to universal constants. When the matrix $\bar{A}$is not Hurwitz but only has non-negative real parts in its eigenvalues, weprove that the averaged LSA procedure actually achieves an $O(1/T)$ rate inmean-squared error. Our results provide a more refined understanding of linearstochastic approximation in both the asymptotic and non-asymptotic settings. Wealso show various applications of the main results, including the study ofmomentum-based stochastic gradient methods as well as temporal differencealgorithms in reinforcement learning.
A scikit-learn compatible learning classifier system

Parallel to genetic algorithms and genetic programming, the evolutionary computation (EC) sub-field of learning classifier systems (LCS) has been an active area of research, development, and application. LCSs are best known for their ability to flexibly adapt to perform well on a wide range of problems, and to outperform other machine learning (ML) algorithms on problems characterized as being complex and heterogeneous. However, to date LCSs remain poorly recognized and thus rarely considered for comparison to other ML approaches in or outside of the EC community. One likely reason for this is the general lack of easy-to-use LCS implementations to facilitate comparisons to other well-known ML approaches. The Python-based scikit-learn library has become a very popular way to implement, utilize, and compare ML algorithms in a simple, uniform manner. In this work, we develop and evaluate the first LCS scikit-learn package, called scikit-eLCS. The scikit-eLCS algorithm is a modern Michigan-style supervised-learning LCS descended from eLCS, UCS, and XCS. We demonstrate the efficacy and capabilities of this package over benchmark n-bit multiplexer problems. We expect scikit-eLCS to serve as an algorithmic benchmark to facilitate future ML comparisons, as well as a blueprint for the implementation of other scikit-learn compatible LCS algorithms.
The impact of institutions on financial development: Evidence from East Asian countries

Abstract This paper examines the impact of institutions on regional financial development using a panel data model of 11 East Asian countries during 1996–2017. It divides the institutional factors into six economic factors and six legal‐political factors. The analysis demonstrates that the legal‐political institutional factors have a stronger impact on financial development than the economic institutional factors in East Asia. Improvement in institutional quality such as fiscal freedom, business freedom, control of corruption, government effectiveness, regulatory quality and rule of law can promote financial development. Improvements of these institutional factors facilitate the ability of enterprises to allocate resources and improve the strength of business operations, thereby reducing transaction costs and making the financial operating environment fairer and more efficient. With the improvement of institutional factors, financial development will have more opportunities to develop better. Institutional impacts are more pronounced in the financial development in Malaysia, Indonesia, the Philippines and Myanmar than in the other countries such as China, Japan, Korea and Singapore.
Improving the affordability of robustness training for DNNs

Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational complexity associated with this approach, due to the maximization of the loss function when finding adversaries, is a longstanding problem and may be prohibitive when using larger and more complex models. In this paper we show that the initial phase of adversarial training is redundant and can be replaced with natural training which significantly improves the computational efficiency. We demonstrate that this efficiency gain can be achieved without any loss in accuracy on natural and adversarial test samples. We support our argument with insights on the nature of the adversaries and their relative strength during the training process. We show that our proposed method can reduce the training time by a factor of up to 2.5 with comparable or better model test accuracy and generalization on various strengths of adversarial attacks.