Reviewerly/exHarmony huggingface dataset:
Physical Device Compatibility Support for Implementation of IoT Services with Design Once, Provide Anywhere Concept

This paper proposes a method to ensure compatibility between physical devices for implementing a service design. The method supports the relaxation of strict implementation. It allows a set of compatible devices to be implemented instead of specifying specific devices in the service design. This paper’s main contribution is the formalization of device constraints using the device’s attributes and a method to check the compatibility between devices. The method’s characteristic is that a service designer can decide the level of strictness and abstractness of the design by adjusting the compatibility rate. We show the feasibility of the proposed method to achieve the goal of “Design Once, Provide Anywhere” with an application example. We also evaluated the quality of service of the implemented IoT service in a different environment using a platform called Elgar.
Secure Internet Voting Protocol (SIVP): A secure option for electoral processes

Colombia government wants to implement electronic voting. However, the existing electronic voting protocols only include some of the required security features and Colombia needs a protocol with all these features to ensure fraud-free elections. In this paper, we present the design of SIVP (Secure Internet Voting Protocol), a new voting protocol for electoral processes, based on blind signatures and public key cryptography. This protocol has six phases and provides: eligibility, democracy, privacy, verifiability, accuracy, fairness, robustness, receipt-freeness and coercion-resistant. Also, we compare the number of cryptographic operations per phase of SIVP with other four protocols and conclude that the computational load of our protocol is not excessively high despite including more security features.
Blockchain in Supply Chains and Logistics: Trends in Development

Supply chains and logistics play an essential role in society and must continuously adapt to market changes, customer needs and technological developments, including emerging technologies such as blockchain (BC) and smart contracts. This study reviews current literature in the supply chain and logistics field to investigate emerging trends in BC developments. The results reveal several trends, such as process and flow development, the streamlining of cross-border transactions and the use of smart sensors in logistics. This paper highlights drivers of BC developments, including costs, security, data traceability, trust and transparency. As well as revealing knowledge gaps in research and practice, this study contributes to an increased understanding of the trends in BC developments within the area of supply chains and logistics.
Saliency-Based Image Retrieval as a Refinement to Content-Based Image Retrieval

Searching for an image in a database is important in different applications; hence, many algorithms have been proposed to identify the contents of the image. In some applications, but not all, identifying the content of the image as a whole can offer good results. Searching for an object inside the image is more important in most applications than identifying the image as a whole. Therefore, studies focused on segmenting the image into small sub-images and identified their contents. In view of the concepts of human attention, various literature defined saliency as a computer representation of it, where different algorithms were developed to extract the salient regions. These salient regions, which are the regions that attract human attention, are used to identify the most important regions that contain important objects in the image. In this paper, we introduce a new algorithm that utilises the saliency principles to identify the contents of an image and search for similar objects in the images stored in a database. We also demonstrate that the use of salient objects produces better and more accurate results in the image retrieval process. A new retrieval algorithm is therefore presented here, focused on identifying the objects extracted from the salient regions. To assess the efficiency of the proposed algorithm, a new evaluation method is also proposed which considers the order of the retrieved image in assessing the efficiency of the algorithm.
A Semi-Automatic Reconstruction of Archaeological Pottery Fragments from 2D Images Using Wavelet Transformation

The problem of matching fragments of three-dimensional (3D) objects has gained increasing attention, and several approaches have been developed to solve this problem. To date, however, to the best knowledge of the authors, there is no computer-based method supporting archaeologists in this activity. For this purpose, in this paper, a semi-automatic approach is proposed for the reconstruction of archaeological pottery fragments based on two-dimensional (2D) images. Firstly, the method, considering the curves as features, involves the extraction of edge curves by applying the Canny filter algorithm to the fragments’ image. Next, the wavelet transformation method is used to fit the edge curves and obtain the approximation coefficients. Then, the correlation coefficients between fragments are computed and the matching of fragments is done by comparing their values. The proposed approach is tested on some real cases. The results of the experimentation show, if compared with the state-of-the-art, that the method seems to be efficient and accurate in the reconstruction of pottery from 2D images of their fragments.
A Frequent Pattern Conjunction Heuristic for Rule Generation in Data Streams

This paper introduces a new and expressive algorithm for inducing descriptive rule-sets from streaming data in real-time in order to describe frequent patterns explicitly encoded in the stream. Data Stream Mining (DSM) is concerned with the automatic analysis of data streams in real-time. Rapid flows of data challenge the state-of-the art processing and communication infrastructure, hence the motivation for research and innovation into real-time algorithms that analyse data streams on-the-fly and can automatically adapt to concept drifts. To date, DSM techniques have largely focused on predictive data mining applications that aim to forecast the value of a particular target feature of unseen data instances, answering questions such as whether a credit card transaction is fraudulent or not. A real-time, expressive and descriptive Data Mining technique for streaming data has not been previously established as part of the DSM toolkit. This has motivated the work reported in this paper, which has resulted in developing and validating a Generalised Rule Induction (GRI) tool, thus producing expressive rules as explanations that can be easily understood by human analysts. The expressiveness of decision models in data streams serves the objectives of transparency, underpinning the vision of ‘explainable AI’ and yet is an area of research that has attracted less attention despite being of high practical importance. The algorithm introduced and described in this paper is termed Fast Generalised Rule Induction (FGRI). FGRI is able to induce descriptive rules incrementally for raw data from both categorical and numerical features. FGRI is able to adapt rule-sets to changes of the pattern encoded in the data stream (concept drift) on the fly as new data arrives and can thus be applied continuously in real-time. The paper also provides a theoretical, qualitative and empirical evaluation of FGRI.
Impact of microvibration on the optical performance of an airborne camera

Aiming to investigate the connection between camera structure and optical systems, a comprehensive analysis needs to be performed for the airborne camera. An integrated analysis method was proposed to design and analyze optical and mechanical structures. Based on the designed small airborne camera, the impact of microvibration on the optical performance of the airborne camera was studied by integrated optomechanical analysis. In addition, the change of optical surface accuracy was analyzed. First, static and dynamic analysis of the designed airborne camera was performed to verify the stability of the camera structure and obtain the data for integrated optomechanical analysis. Then, a calculation method for rigid body displacement was proposed, and the impact of rigid body displacements on the optical system was analyzed. To evaluate the change of surface accuracy, the parameters root mean square (RMS) and peak to valley (PV) were calculated by fitting the surface distortion data. Based on the Zernike polynomial coefficients, the response of the optical system was calculated and analyzed utilizing ZEMAX to analyze the impact of microvibration on the optical performance of the airborne camera. The analysis results show that microvibration has no significant impact on optical performance of the designed small airborne camera. Finally, the analysis results were verified through experiments.
Invariant Measure for Stochastic Schrödinger Equations

Quantum trajectories are Markov processes that describe the time evolution of a quantum system undergoing continuous indirect measurement. Mathematically, they are defined as solutions of the so-called Stochastic Schrödinger Equations, which are nonlinear stochastic differential equations driven by Poisson and Wiener processes. This paper is devoted to the study of the invariant measures of quantum trajectories. Particularly, we prove that the invariant measure is unique under an ergodicity condition on the mean time evolution, and a “purification” condition on the generator of the evolution. We further show that quantum trajectories converge in law exponentially fast toward this invariant measure. We illustrate our results with examples where we can derive explicit expressions for the invariant measure.
Students’ Experiences of Online English Language Learning by Using YouTube

This pandemic situation stimulates transformation of educational programs worldwide.Right now, English Language Teaching programs are transforming most of the onsite activities into remote learning which involve synchronous and asynchronous learning and communication.One of the popular technologies for teaching and learning English is YouTube.For most of the Web 2.0 generation, YouTube provides users with learning resources that offer communicative features effective for both synchronous and asynchronous learning.However, the extent to which YouTube has been used and experienced by tertiary students in specific contexts during this pandemic situation has yet to be explored to inform policy and practices of EFL education.This research draws from the results of a case study that employed questionnaire and open-ended interviews conducted among university students in an Indonesian locale.The findings indicate that YouTube was popularly used in current online EFL classrooms.The students' experience revealed positive attitude toward the use of YouTube in EFL learning.Nevertheless, it was not as frequently used for other purposes such as entertainment and learning other subjects.At the same time, potential drawbacks were identified to inform improvement of teaching and learning practices in future.
Deductive Systems and Coherence for Skew Prounital Closed Categories

In this paper, we develop the proof theory of skew prounital closed categories. These are variants of the skew closed categories of Street where the unit is not represented. Skew closed categories in turn are a weakening of the closed categories of Eilenberg and Kelly where no structural law is required to be invertible. The presence of a monoidal structure in these categories is not required. We construct several equivalent presentations of the free skew prounital closed category on a given set of generating objects: a categorical calculus (Hilbert-style system), a cut-free sequent calculus and a natural deduction system corresponding to a variant of planar (= non-commutative linear) typed lambda-calculus. We solve the coherence problem for skew prounital closed categories by showing that the sequent calculus admits focusing and presenting two reduction-free normalization procedures for the natural deduction calculus: normalization by evaluation and hereditary substitutions. Normal natural deduction derivations (beta-eta-long forms) are in one-to-one correspondence with derivations in the focused sequent calculus. Unexpectedly, the free skew prounital closed category on a set satisfies a left-normality condition which makes it lose its skew aspect. This pitfall can be avoided by considering the free skew prounital closed category on a skew multicategory instead. The latter has a presentation as a cut-free sequent calculus for which it is easy to see that the left-normality condition generally fails. The whole development has been fully formalized in the dependently typed programming language Agda.
Fine-tuning neural machine translation on gender-balanced datasets

This work is supported in part by the Catalan Agency for Management of University and Research Grants (AGAUR) through an Industrial Ph.D. Grant, the Spanish Ministerio de Ciencia e Innovacion and by the Agencia Estatal de Investigacion, through the postdoctoral senior grant Ramon y Cajal and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 947657).
Applications of Data Mining in Intelligent Computer-aided Athletic Training

The mining and application of data in sports training through intelligent computer-aided research, this paper proposes a system development and design method to improve the development and design efficiency of the data visualization system and reduce the development cost by using the Python language to handle the high efficiency of system programming and the convenience of charts.Thus, it further improves the efficiency and interactivity of the information management of training data and the convenience and stability of managing training data.This system is a preliminary exploration of Python language and Echarte technology in the management of training data of sports training programs.Because of the integration of training programs with computer application technology and data visualization technology, the system still needs to be strengthened in terms of data processing methods and the diversity of chart styles generated.In the future, the use of artificial intelligence, big data analysis, and other technologies in sports training data management and analysis has more room for development.The model only needs to measure 14 indicators of bipedal closed-eye standing and single-leg closed-eye standing movements to achieve the ability to predict the balance of motor training, and its accuracy meets the clinical requirements.This method has a shorter measurement time and fewer metrics than traditional methods.
Deep Learning Based Automatic Modulation Classification With Long-Short Term Memory Networks

The automatic modulation classification (AMC) process is used to determine the modulation format of the transmitted signal at the receiver side without any prior knowledge. Deep learning is a type of machine learning that consists of multiple layers in which raw data is taken as input. This study analyzes the AMC process with a deep learning approach. In this context, performances of LSTM (Long-Short Term Memory) and Bi-LSTM (Bidirectional LSTM) methods on the modulation classification problem are compared. Simulation results show that Bi-LSTM method has a higher performance than does the LSTM method.
Amplified spontaneous emission based quantum random number generator

Amplified spontaneous emission based quantum random number generator
Sentiment Analysis User Comments On E-commerce Online Sale Websites

E-commerce websites, based on their structural ontology, provides access to a wide range of options and the ability to deal directly with manufacturers to receive cheaper products and services as well as receiving comments and ideas of the users on the provided products and services. This is a valuable source of information, which includes a large number of user reviews. It is difficult to check the bulk of the comments published manually and non-automatically. Hence, sentiment analysis is an automated and relatively new field of study, which extracts and analyzes people's attitudes and emotions from the context of the comments. The primary objective of this research is to analyze the content of users' comments on online sale e-commerce websites of handcraft products. Sentiment analysis techniques were used at sentence level and machine learning approach.  First, the pre-processing steps and TF-IDF method were implemented on the comments text. Next, the comments text were classified into two groups of products and services comments using Support Vector Machine (SVM) algorithm with 99.2% accuracy. Finally, the sentiment of comments was classified into three groups of positive, negative and neutral using XGBoost algorithm. The results showed, 95.23% and 95.12% accuracies for classification of sentiments in comments about products and services, respectively.
Machine Reading Comprehension Framework Based on Self-Training for Domain Adaptation

Machine reading comprehension (MRC) is a type of question answering mechanism in which a computer reads documents and answers related questions. The accuracies of recent MRC systems surpass those of humans. However, most MRC systems exhibit significant performance deteriorations when domains are changed. Hence, we propose a self-training framework for MRC. The proposed framework is composed of a pseudo-answer extractor, a pseudo-question generator, and an MRC system. In the source domain, components are pretrained using an MRC training dataset. In the target domain, the performances of the pseudo-question generator and MRC system is improved through a mutual self-training scheme. During the mutual self-training, the pseudo-question generator provides new training data to the MRC system and obtains rewards from the MRC system for reinforcement learning. In experiments with a Wikipedia domain (source domain) and civil affair domain (target domain), an MRC system based on the proposed self-training scheme demonstrates better performances than that based on automatic data augmentation.
Perspectives on “Good” in Blockchain for Good

Blockchain projects have been developed to extend the reach of distributed ledger technology (DLT) beyond cryptocurrency to achieve “good” in the world. Such projects may make a claim for moral, ethical, and responsible intent, but many researchers have not critically examined what good means in context. The concept of good has been debated for centuries and whilst we will not conclude the argument, we should engage in the discourse. We propose the idea that exploration across micro, meso, and macro levels of value creating ecosystems is needed. The implications, both practical and theoretical, of the use of blockchain for good require analysis. As the ambition for blockchain innovations to transform society for the better becomes practical reality, understanding of such change will come from transdisciplinary researchers able to bridge knowledge of social and technical systems.
Performance evaluation of the SM4 cipher based on field‐programmable gate array implementation

Information security is essential to ensure security of exchanged sensitive data in resource-constrained devices (RCDs) because they are used widely in the Internet of things (IoT). The implementation of special ciphers is required in these RCDs, as they have many limitations and constraints, such as low power/energy dissipation, and require low hardware resources. The SM4 cipher is one of the common block ciphers, which can be easily implemented and offers a high level of security. The objective of this study is to determine the optimum field-programmable gate array (FPGA) design for SM4 to facilitate reconfiguring the FPGA with an optimum design during operation. Various FPGA design options for SM4 ciphers are examined, and the performance metrics are modeled: power, energy, area, and speed. Scalar and pipelined designs with one or multiple hardware rounds are considered without altering the cipher algorithm. The results show that the best scalar implementation utilises less resources than the pipelined implementations by 7%. Alternatively, pipelined implementations perform better regarding speed and energy dissipation by 10 times and 40% of the scalar implementation, respectively. The pipeline implementations with eight or 16 rounds are optimum for continuous streams of data, and the two-round design is the optimum design across ciphers.
SSD7-FFAM: A Real-Time Object Detection Network Friendly to Embedded Devices from Scratch

The high requirements for computing and memory are the biggest challenges in deploying existing object detection networks to embedded devices. Living lightweight object detectors directly use lightweight neural network architectures such as MobileNet or ShuffleNet pre-trained on large-scale classification datasets, which results in poor network structure flexibility and is not suitable for some specific scenarios. In this paper, we propose a lightweight object detection network Single-Shot MultiBox Detector (SSD)7-Feature Fusion and Attention Mechanism (FFAM), which saves storage space and reduces the amount of calculation by reducing the number of convolutional layers. We offer a novel Feature Fusion and Attention Mechanism (FFAM) method to improve detection accuracy. Firstly, the FFAM method fuses high-level semantic information-rich feature maps with low-level feature maps to improve small objects’ detection accuracy. The lightweight attention mechanism cascaded by channels and spatial attention modules is employed to enhance the target’s contextual information and guide the network to focus on its easy-to-recognize features. The SSD7-FFAM achieves 83.7% mean Average Precision (mAP), 1.66 MB parameters, and 0.033 s average running time on the NWPU VHR-10 dataset. The results indicate that the proposed SSD7-FFAM is more suitable for deployment to embedded devices for real-time object detection.
RDFRules: Making RDF rule mining easier and even more efficient

AMIE+ is a state-of-the-art algorithm for learning rules from RDF knowledge graphs (KGs). Based on association rule learning, AMIE+ constituted a breakthrough in terms of speed on large data compared to the previous generation of ILP-based systems. In this paper we present several algorithmic extensions to AMIE+, which make it faster, and the support for data pre-processing and model post-processing, which provides a more comprehensive coverage of the linked data mining process than does the original AMIE+ implementation. The main contributions are related to performance improvement: (1) the top-k approach, which addresses the problem of combinatorial explosion often resulting from a hand-set minimum support threshold, (2) a grammar that allows to define fine-grained patterns reducing the size of the search space, and (3) a faster projection binding reducing the number of repetitive calculations. Other enhancements include the possibility to mine across multiple graphs, the support for discretization of continuous values, and the selection of the most representative rules using proven rule pruning and clustering algorithms. Benchmarks show reductions in mining time of up to several orders of magnitude compared to AMIE+. An open-source implementation is available under the name RDFRules at https://github.com/propi/rdfrules.