Reviewerly/exHarmony huggingface dataset:
Construction of Stretching-Bending Sequential Pattern to Recognize Work Cycles for Earthmoving Excavator from Long Video Sequences

Counting the number of work cycles per unit of time of earthmoving excavators is essential in order to calculate their productivity in earthmoving projects. The existing methods based on computer vision (CV) find it difficult to recognize the work cycles of earthmoving excavators effectively in long video sequences. Even the most advanced sequential pattern-based approach finds recognition difficult because it has to discern many atomic actions with a similar visual appearance. In this paper, we combine atomic actions with a similar visual appearance to build a stretching–bending sequential pattern (SBSP) containing only “Stretching” and “Bending” atomic actions. These two atomic actions are recognized using a deep learning-based single-shot detector (SSD). The intersection over union (IOU) is used to associate atomic actions to recognize the work cycle. In addition, we consider the impact of reality factors (such as driver misoperation) on work cycle recognition, which has been neglected in existing studies. We propose to use the time required to transform “Stretching” to “Bending” in the work cycle to filter out abnormal work cycles caused by driver misoperation. A case study is used to evaluate the proposed method. The results show that SBSP can effectively recognize the work cycles of earthmoving excavators in real time in long video sequences and has the ability to calculate the productivity of earthmoving excavators accurately.
Artificial Intelligence in Education. Analysis of Implementation Goals

The article typologies the goals of using AI systems, corresponding to three key aspects of understanding education (education as a system, education as a process, education as a result) and corresponding to significant trends in the development of education (increasing flexibility and decentralization of the global education system, personalization of the education process, digital fixation of competence results education). AI technologies will be able to bring education management closer to usage of methods based on a significant amount of quality data and contribute to the formation of evidence-based educational policy, when it will be applied to the systemic aspect of education. Problems with the interpretation of the decision-making model in administration directly affects on the effectiveness of artificial intelligence support for managerial decisions in the education. The process of education and upbringing can be personalized and individualized with the support of AI through the formation of individual educational programs by format, by content, by the educational environment; methodological support of training courses; increasing the motivation and involvement of students. The transformation of interaction models between educational subjects is ambiguous in terms of the impact on the autonomy and responsibility of the subjects, on the results of socialization and upbringing, on the labor intensity and transparency of the educational process, including in the light of the prospects for the emergence of “human–AI” systems as a trained agent. In the effective aspect, education involves AI as a tool for monitoring and recording educational achievements and expended resources, capable of clarifying the links between educational activities and results. The digital educational footprint, becoming a commodity, generates a number of conflicts regarding the autonomy of subjects and the status of personal data. The key risks of using AI in education are associated in the article with the problems of human existential security and the anthropological essence of education. Their assessment is limited by the Collingridge dilemma, in the solution of which a comprehensive socio-humanitarian examination of the goals and practices of using AI in education is important, part of which is the development in the field of education philosophy.
Agent-Based Modeling of Blockchain Decentralized Financial Protocols

Decentralized financial applications running on blockchains using smart contracts have attracted a lot of attention recently. One important class of such applications is decentralized digital asset exchanges. In this paper we present an agent-based modeling approach for decentralized exchanges that allowed us to achieve realistic results both in normal and stress market conditions and also investigate the impact of front runners on the distribution of profits. We also compare the results of the two exchanges - Uniswap and Liquifi - to evaluate the effect of the proposed solution for the price slippage and front running problems.
A Sequential Sensor Selection Strategy for Hyper-Parameterized Linear Bayesian Inverse Problems

We consider optimal sensor placement for hyper-parameterized linear Bayesian inverse problems, where the hyper-parameter characterizes nonlinear flexibilities in the forward model, and is considered for a range of possible values. This model variability needs to be taken into account for the experimental design to guarantee that the Bayesian inverse solution is uniformly informative. In this work we link the numerical stability of the maximum a posterior point and A-optimal experimental design to an observability coefficient that directly describes the influence of the chosen sensors. We propose an algorithm that iteratively chooses the sensor locations to improve this coefficient and thereby decrease the eigenvalues of the posterior covariance matrix. This algorithm exploits the structure of the solution manifold in the hyper-parameter domain via a reduced basis surrogate solution for computational efficiency. We illustrate our results with a steady-state thermal conduction problem.
The status of cryptocurrency in Morocco

Cryptocurrency is a new economy of transacting value whose use case is undeniable. Its only requirement is to be human, and it is borderless. In Morocco, cryptocurrency is erasing borders and gaining popularity. However, the general public opinion on this new innovation is unclear. This study aims to elucidate the popularity of this technology in the Kingdom by means of a small survey. The present study also investigates areas related to cryptocurrency, such as people’s trust in banks. The results from the survey are contrasted with other countries to obtain a global perspective regarding Morocco’s placement in the cryptocurrency adoption spectrum. The results also suggest that Morocco has the potential to adopt this technology. Notably, 9% of the sample population claim that they have owned, or still own, some sort of cryptocurrency. However, the country faces legal obstructions and financial restrictions that impede the progress of cryptocurrency adoption.
How to prepare for the onslaught of phishing email attacks

Phishing email attacks are one of the biggest cyber security threats of our time, increasing exponentially year on year. Alarmingly we've seen attacks from phishing emails go up by nearly 700% compared to last year. 1 Cyber criminals are taking advantage of the huge potential for human error within any business, where one accidental click on a phishing email can allow access into entire corporate networks, a costly mistake to make. Cyber criminals are taking advantage of the potential for human error, where one click on a phishing email can allow access into corporate networks. Every business must have processes in place to cope with a potential phishing attack. Stephen Burke of Cyber Risk Aware looks at four key steps organisations can take to protect themselves. By ensuring that the correct infrastructure and technology is in place, combined with effective cyber security awareness training, businesses will be prepared to protect their networks.
Quaternion fractional-order color orthogonal moment-based image representation and recognition

Abstract Inspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generalized Laguerre polynomials. Firstly, the proposed QFr-GLMs are directly constructed in Cartesian coordinate space, avoiding the need for conversion between Cartesian and polar coordinates; therefore, they are better image descriptors than circularly orthogonal moments constructed in polar coordinates. Moreover, unlike the latest Zernike moments based on quaternion and fractional-order transformations, which extract only the global features from color images, our proposed QFr-GLMs can extract both the global and local color features. This paper also derives a new set of invariant color-image descriptors by QFr-GLMs, enabling geometric-invariant pattern recognition in color images. Finally, the performances of our proposed QFr-GLMs and moment invariants were evaluated in simulation experiments of correlated color images. Both theoretical analysis and experimental results demonstrate the value of the proposed QFr-GLMs and their geometric invariants in the representation and recognition of color images.
Provably Secure Security-Enhanced Timed-Release Encryption in the Random Oracle Model

Cryptographic primitive of timed-release encryption (TRE) enables the sender to encrypt a message which only allows the designated receiver to decrypt after a designated time. Combined with other encryption technologies, TRE technology is applied to a variety of scenarios, including regularly posting on the social network and online sealed bidding. Nowadays, in order to control the decryption time while maintaining anonymity of user identities, most TRE solutions adopt a noninteractive time server mode to periodically broadcast time trapdoors, but because these time trapdoors are generated with fixed time server’s private key, many “ciphertexts” related to the time server’s private key that can be cryptanalyzed are generated, which poses a big challenge to the confidentiality of the time server’s private key. To work this out, we propose a concrete scheme and a generic scheme of security-enhanced TRE (SETRE) in the random oracle model. In our SETRE schemes, we use fixed and variable random numbers together as the time server’s private key to generate the time trapdoors. We formalize the definition of SETRE and give a provably secure concrete construction of SETRE. According to our experiment, the concrete scheme we proposed reduces the computational cost by about 10.8% compared to the most efficient solution in the random oracle model but only increases the almost negligible storage space. Meanwhile, it realizes one-time pad for the time trapdoor. To a large extent, this increases the security of the time server’s private key. Therefore, our work enhances the security and efficiency of the TRE.
From Time–Frequency to Vertex–Frequency and Back

The paper presents an analysis and overview of vertex–frequency analysis, an emerging area in graph signal processing. A strong formal link of this area to classical time–frequency analysis is provided. Vertex–frequency localization-based approaches to analyzing signals on the graph emerged as a response to challenges of analysis of big data on irregular domains. Graph signals are either localized in the vertex domain before the spectral analysis is performed or are localized in the spectral domain prior to the inverse graph Fourier transform is applied. The latter approach is the spectral form of the vertex–frequency analysis, and it will be considered in this paper since the spectral domain for signal localization is well ordered and thus simpler for application to the graph signals. The localized graph Fourier transform is defined based on its counterpart, the short-time Fourier transform, in classical signal analysis. We consider various spectral window forms based on which these transforms can tackle the localized signal behavior. Conditions for the signal reconstruction, known as the overlap-and-add (OLA) and weighted overlap-and-add (WOLA) methods, are also considered. Since the graphs can be very large, the realizations of vertex–frequency representations using polynomial form localization have a particular significance. These forms use only very localized vertex domains, and do not require either the graph Fourier transform or the inverse graph Fourier transform, are computationally efficient. These kinds of implementations are then applied to classical time–frequency analysis since their simplicity can be very attractive for the implementation in the case of large time-domain signals. Spectral varying forms of the localization functions are presented as well. These spectral varying forms are related to the wavelet transform. For completeness, the inversion and signal reconstruction are discussed as well. The presented theory is illustrated and demonstrated on numerical examples.
AN OVERVIEW OF CHALCOPHILE ELEMENT CONTENTS OF PYRRHOTITE, PENTLANDITE, CHALCOPYRITE AND PYRITE FROM MAGMATIC NI-CU- PGE DEPOSITS

We have compiled the trace element concentrations in pyrrhotite, pentlandite, chalcopyrite, and pyrite from magmatic Ni-Cu-PGE ore deposits with the aim of understanding their petrogenesis and whether these minerals can be used as indicator minerals. Among the samples, there are some of the most studied world-class Ni-Cu- (Aguablanca, Duluth, Jinchuan, Noril’sk-Talnakh-Kharaelakh, Sudbury, Voisey’s Bay, and others) and PGE-dominated (Bushveld, Lac des Iles, Stillwater, Great Dyke, and Penikat) deposits. Crustal assimilation may be constrained using As/Se and Sb/Se ratios in pentlandite. The degree of interaction between the silicate and sulfide liquids (R-factor) can be estimated by the content of highly chalcophile elements (Dsulf liq/sil liq above 1000) in sulfide minerals. The fractional crystallization of the sulfide liquid can be traced using Se/Te ratios of pentlandite. Pyrite formed by exsolution from MSS has higher Rh, Ru, Ir, and Os than co-existing pyrrhotite, whereas pyrite formed by hydrothermal alteration of pyrrhotite inherits the Rh, Ru, Ir, and Os contents of the pyrrhotite it replaced. Sulfide minerals are preserved in transported glacial cover and their trace element chemistry can be used to discriminate their source. Pentlandite from Ni-Cu deposits has much lower Rh and Pd concentrations than those from PGE-dominated deposits, pyrite from magmatic deposits has higher Co/Sb and Se/As ratios relative to pyrite from hydrothermal deposits, and chalcopyrite from magmatic deposits has much higher Ni and lower Cd concentrations than those from hydrothermal deposits.
Towards a Privacy Conserved and Linked Open Data Based Device Recommendation in IoT

Interconnecting Internet of Things (IoT) devices creates a network of services capable of working together to accomplish certain goals in different domains. The heterogeneous nature of IoT environments makes it critical to find devices that extend existing architectures and helps in reaching the desired goal; especially if we have to take into consideration data privacy. In this paper, we present a Linked Open Data (LOD) based approach to semantically annotate and recommend IoT devices while adding a layer of data security and privacy through implementing the SOLID (SOcial LInked Data) framework.
Developing an AR Pop-up Picture Book and its Effect Editor Based on Teaching Motions

We propose an augmented reality (AR) pop-up picture book (AR book), which is a digital picture book using the AR technology. Digital picture books increase the interest of children in reading and are often used to encourage children to read. Conventional picture book writers have few technical skills and expertise in developing AR contents. We aim to develop an effect editor for AR books to aid the writers create expressive AR books. We propose a new intuitive method for obtaining editing effects in AR books using teaching motions based on demonstrations of writers using real-world objects. The results of our experimental evaluation demonstrate that the system can be effectively used to edit 3D motion effects in AR books.
The Hidden Authenticators: Nanometer-Scale Electromechanical Tags Could Thwart Counterfeiters

What's the largest criminal enterprise in the world? Narcotics? Gambling? Human trafficking? Nope. The biggest racket is the production and trade of counterfeit goods, which is expected to exceed US $1 trillion next year. You've probably suffered from it more than once yourself, purchasing on Amazon or eBay what you thought was a brand-name item only to discover that it was an inferior-quality counterfeit.
Wearable energy efficient fitness tracker for sports person health monitoring application

Continuous evaluation of biological and physiological metrics of sports personalities, evaluating general health status, and alerting for life-saving treatments, is supposed to enhance efficiency and healthy performance. Wearable devices with acceptable form factors compact, flexibility, minimal power consumption, etc., are needed for continuous monitoring to avoid affecting everyday operations, thereby retaining functional effectiveness and consumer satisfaction. This research focuses on the acceleration tracker for particularizing the work. Acceleration data is typically collected on battery-powered sensors for activity detection, referring to an exchange between high-precision detection and energy-efficient processing. From a feature selection perspective, the paper explores this trade-off. It suggests an Energy-Efficient Behavior Recognition System with a comprehensive energy utilization model and the Multi-objective Algorithm of Particle Swarm Optimization (EEBRS-MPSO). Therefore, using Random Forest (RF) classifiers, the model and algorithm are tested to measure the precision of identification and obtain the task’s best performance with the lowest energy consumption, among other biologically-inspired algorithms. The findings indicate that energy consumption for data storage and data processing is minimized with magnitude relative to the raw data method by choosing suitable groups of attributes. Thus, the platform allows a scalable range of feature clusters that require the authors to provide an adequate power adjustment for given target use.
Bran: Reduce Vulnerability Search Space in Large Open Source Repositories by Learning Bug Symptoms

Software is continually increasing in size and complexity, and therefore, vulnerability discovery would benefit from techniques that identify potentially vulnerable regions within large code bases, as this allows for easing vulnerability detection by reducing the search space. Previous work has explored the use of conventional code-quality and complexity metrics in highlighting suspicious sections of (source) code. Recently, researchers also proposed to reduce the vulnerability search space by studying code properties with neural networks. However, previous work generally failed in leveraging the rich metadata that is available for long-running, large code repositories.
Fast and More Powerful Selective Inference for Sparse High-Order Interaction Model

Automated high-stake decision-making, such as medical diagnosis, requires models with high interpretability and reliability. We consider the sparse high-order interaction model as an interpretable and reliable model with a good prediction ability. However, finding statistically significant high-order interactions is challenging because of the intrinsically high dimensionality of the combinatorial effects. Another problem in data-driven modeling is the effect of ``cherry-picking" (i.e., selection bias). Our main contribution is extending the recently developed parametric programming approach for selective inference to high-order interaction models. An exhaustive search over the cherry tree (all possible interactions) can be daunting and impractical, even for small-sized problems. We introduced an efficient pruning strategy and demonstrated the computational efficiency and statistical power of the proposed method using both synthetic and real data.
An Implicit Representation and Iterative Solution of Randomly Sketched Linear Systems

Randomized linear system solvers have become popular as they have the potential to reduce floating point complexity while still achieving desirable convergence rates. One particularly promising class of methods, random sketching solvers, has achieved the best known computational complexity bounds in theory, but is blunted by two practical considerations: there is no clear way of choosing the size of the sketching matrix a priori; and there is a nontrivial storage cost of the sketched system. In this work, we make progress towards addressing these issues by implicitly generating the sketched system and solving it simultaneously through an iterative procedure. As a result, we replace the question of the size of the sketching matrix with determining appropriate stopping criteria; we also avoid the costs of explicitly representing the sketched linear system; and our implicit representation also solves the system at the same time, which controls the per-iteration computational costs. Additionally, our approach allows us to generate a connection between random sketching methods and randomized iterative solvers (e.g., the randomized Kaczmarz method and randomized Gauss--Seidel). As a consequence, we exploit this connection to (1) produce a stronger, more precise convergence theory for such randomized iterative solvers under arbitrary sampling schemes (i.i.d., adaptive, permutation, dependent, etc.), and (2) improve the rates of convergence of randomized iterative solvers at the expense of a user-determined increase in per-iteration computational and storage costs. We demonstrate these concepts on numerical examples on 49 distinct linear systems.
Top Threats to Cloud: A Three-Dimensional Model of Cloud Security Assurance

The incredible growth in the cloud applications and services reflects a positive swing in the thought processes of the business decision makers for cloud adoption. However, ever-evolving security and privacy issues continue to influence the decision makers to delay the cloud adoption. In this integrationist exposition, the previous publications are enriched and enhanced to holistically analyze different threats to cloud computing to conceptualize a three-dimensional model of cloud security assurance. These three dimensions, namely Security Solution, Security Operation, and Security Compliance, are interwoven to address the top threats to cloud computing, which are identified and reported by the cloud security alliance (CSA) research group in their latest and previous reports. The model will help practitioners to design and implement a security assurance system for a cloud ecosystem to strengthen trust in the cloud and accelerate its adoption to bring agility and velocity in cloud applications and services delivery in a cost-effective way.
Transformer Fault Diagnosis Based on Optimized CPSO-BP Neural Network

Abstract Oil chromatographic analysis (DGA) is an important way to transformer fault diagnosis, combining research topics based on I-K-means clustering, t SNE visual clustering in data mining, fault classification number. In order to improve the convergence speed of neural network, an improved back-propagation BP neural network using ADAM gradient optimization algorithm instead of traditional stochastic gradient descent optimization to update the weight of neural network is proposed. Fuzzy C-means clustering and particle swarm optimization are proposed to optimize the initial parameters of neural network. By using 3500 data samples of transformers from a power plant in a city of Liaoning Province to carry out simulation experiments, and comparing the traditional BP network algorithm, CPSO-BP network algorithm and the CPSO-BP network algorithm optimized by Adam, it is proved that the CPSO-BP network optimized by Adam has fast training convergence, strong generalization ability and high accuracy. At the same time, the accuracy, precision, recall and F-1 values were used to evaluate the CPSO-BP network algorithm optimized by ADAM to verify the effectiveness and stability of the algorithm in transformer fault diagnosis.