Reviewerly/exHarmony huggingface dataset:
Non-Markovian measure independent of initial states of open systems

In recent years, quantifying non-Markovian effect in open quantum system has become an important subject in the quantum decoherence control field. In this paper, a non-Markovian measure independent of the initial state of open system is proposed, thereby extending non-Markovian measure based on quantum Fisher information from the case where the initial state of the system is a pure state to the case where the initial state of the system is an arbitrary mixed state. As its application, the non-Markovian process is quantified by quantum Fisher information about a two-level system undergoing the three well-known dissipative channels, i.e. amplitude dissipative channel, phase damping channel, and random unitary channel. The results show that the conditions of non-Markovian processes in the three dissipative channels are independent of the selection of the initial state of the system by means of the quantum Fisher information of a phase parameter. Further, for amplitude dissipation channel and phase damping channel, the conditions for the non-Markovian processes to occur are equivalent to those given by trace distance, divisibility, quantum mutual information, quantum Fisher-information matrix, et al. As expected, for the case of amplitude dissipation channel, the corresponding results can reduce to the one in other paper (Lu X M, Wang X G, Sun C P &lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1103/PhysRevA.82.042103"&gt;2010 &lt;i&gt;Phys. Rev. A&lt;/i&gt;&lt;italic/&gt; &lt;b&gt;82&lt;/b&gt; 042103&lt;/ext-link&gt;) by selecting the initial state of the system as an optimal pure state. However, for random unitary channel, the conditions of non-Markovian process are not equivalent to those for other measures. In addition, we also obtain an interesting relationship between quantum Fisher information and quantum coherence of the open system in the three dissipative channels, namely the square of quantum &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$l_1$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21-20221053_M2.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21-20221053_M2.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; coherence for the evolved state of system is exactly equal to the quantum Fisher information of the phase parameter. In a word, the obtained results not only improve the application scope of using the quantum Fisher information to detect non-Markovian effects in open systems, but also further highlight its important role in quantum information processing.
Comparative study on mixed reality in the future combat visualization

Improving outdated technologies have empowered the development plan to establish judgement methods to solve real life problems. A community of end-users, such as military and civilian command and control experts, developers, engineers, and specialists, intended to help the Blended Reality (MR) environment. The Virtual Tool creates a coordination of individuals who frequently stand over a table or maybe a workbench as part of their proficient routine for the “real” work environment. The preliminary analysis indicates that the military applications called VIRTSIM system of virtual reality, which are (i) Inaccuracy and reliability of the data, (ii) Difficulties between terrain and simulator, (iii) False interpretation on Simulator and accessories, and (iv) What are the prospects of the proposed structure. Therefore, this research study will compare the future military in mixed reality design and compare the simulator model and prototype that applied for military rehearsal. The adaptation of simulator techniques such as intuitive interaction techniques (manipulation, scaling, movement, and selection), simulator details (synthesis, zooming and filtration, and particulars on demand), and simulator variables (touch sensor, replica weapon, position, and orientation) in the VIRTSIM system to help military commander make the effective practice. The research methodology of the design process is the User-Centered Design (UCD) model in which user involvement influences how a design takes shape throughout the design process. In conclusion, the evaluation of the VIRTSIM system indicate positive results to improve military rehearsal and help the commander make decision-making with appropriate planning and hone military tactical skills.
Design of IoT Based Patient Health Monitoring System

Internet of Things allows health care providers to break out of traditional clinical settings. Home monitoring systems allow to monitor the health of patients when they are not in the doctor’s office. Many years of research have shown that remote monitoring of patients is one of the most effective treatments for chronic diseases such as diabetes, heart failure and chronic obstructive pulmonary disease (COPD), but also increases patient participation and reduces emergency admissions. Assisted and individually adapted environments will be possible through the introduction of technologies that provide individual medical care to anyone living in an environment of their choice. In this article, we consider several requirements for the development of such systems, in particular sensors for automatic detection of physiological parameters of the patient’s health data which will be send to the cloud where in real time artificial intelligence will process the data and present it to the doctor in the form of infographics that can be easily analyzed, the mobile app where a patient can see own health parameters and contact with a doctor and the website where the doctor, based on real-time data, can examine the patient and, in an emergency, contact with a patient and warn against future diseases.
Fast Deep Learning Classification With Multilinear Feature Maps

In the late 1990s, [1] and [2] the development and application of Convolutional Neural Networks (CNN). CNN is considered a Deep Learning algorithm and achieved the best performance in image recognition, localization, and segmentation tasks, compared with traditional image processing techniques [3] and [4], mainly due to the CNN ability to extract a large number of features from input images. When [5] won the Imagenet-2012 challenge, a breakthrough occurred, achieving a significant performance improvement over previous architecture. Another successful architecture is the deep neural network proposed in [6], called VGG-16 (Visual Geometry Group), which showed the importance of depth architecture to achieve high-performance classification tasks.
Study of Question Answering on Legal Software Document using BERT based models

The transformer-based architectures have achieved remarkable success in several Natural Language Processing tasks, such as the Question Answering domain. Our research focuses on different transformer-based language models’ performance in software development legal domain specialized datasets for the Question Answering task. It compares the performance with the general-purpose Question Answering task. We have experimented with the PolicyQA dataset and conformed to documents regarding users’ data handling policies, which fall into the software legal domain. We used as base encoders BERT, ALBERT, RoBERTa, DistilBERT and LEGAL-BERT and compare their performance on the Question answering benchmark dataset SQuAD V2.0 and PolicyQA. Our results indicate that the performance of these models as contextual embeddings encoders in the PolicyQA dataset is significantly lower than in the SQuAD V2.0. Furthermore, we showed that surprisingly general domain BERT-based models like ALBERT and BERT obtain better performance than a more domain-specific trained model like LEGAL-BERT.
Using Translation Memory to Improve Neural Machine Translations

In this paper, we describe a way of using translation memory (TM) to improve the translation quality and stability of neural machine translation (NMT) systems, especially when the sentences to be translated have high similarity with sentences stored in the TM. The difference between the sentences to be translated and the sentences stored in the TM may only be in a few phrases. Our TM comprises not only paired sentences (i.e., a sentence in the source language paired with its translation in the target language) but also paired phrases. Translation quality is improved using good phrase translations for the differing phrases. The NMT system is used to assist phrase translation. We tested our TM on 3,000 English-Chinese paired sentences which were randomly picked from recent annual reports published and submitted to the Hong Kong Stock Exchange. Our TM translations achieved a significant BLEU improvement for high similar sentences compared with our NMT translations.
Improved Inverse-Based Solution Model of Zhang Neuronet for Time-Dependent Cholesky Factorization

Time-dependent Cholesky factorization is investigated and solved further in this paper. An improved solution model via Zhang neuronet (ZN) is proposed with constraint conditions considered. At first, we combine the objective equations and constraint conditions into new equations. The final coefficient matrix of the new equations (as an equation group) is invertible. Then, the new equations are solved by using inverse. The improved ZN solution model accurately solves the problem, and it exponentially converges to the theoretical solution. Numerical experiments and results substantiate the efficacy and the superiority of the improved ZN solution model.
Denominal Verbs in Algonquian: Verbs of Acquiring

This article proposes a lexical analysis of the derivation of denominal verbs of a type found widely in Algonquian languages: verbs of acquiring, which express the process by which the referent of the subject acquires tokens of items of the type named by the nominal on which the verb stem is based. Focusing on data from Ojibwe and Maliseet-Passamaquoddy, I argue against a proposed syntactic analysis of these verbs via noun incorporation, instead developing a lexical alternative; employing the mechanisms of Bochner's (1993) Lexical Relatedness Morphology this allows us to track connections among lexical formations that follow related but distinct derivational patterns.
Implications of Jerk’s On the Measure of Game’s Entertainment: Discovering Potentially Addictive Games

A physical phenomenon represented through a sudden change of acceleration, termed ‘jerk’ (or jolt), is a compelling phenomenon that humans feel and has been seen in practical application in comfort and wear-and-tear effects in mechanical and engineering domains (for instance, in a daily situation, jerk can be experienced when we take an elevator). The logistical model of the game progress model and development of game refinement had led to the same measures of ‘jerk’ from the game-playing field. However, the influence of ‘jerk’ in such a domain has been limited. Mirroring real-world problems, incomplete information games, such as card games, were utilized to investigate the influence of ‘jerk,’ denoted as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AD$ </tex-math></inline-formula> values ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AD$ </tex-math></inline-formula> is the abbreviation of addictive, which means players are getting addicted to the game, and players have the tendency to repeat to play the game, implying that it is difficult to stop when the action associated with it have started), from the game-playing domain. Based on different rules designs and game complexities of several popular card games (Wakeng, Doudizhu, Winner, Big Two, Tien Len), the implications of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AD$ </tex-math></inline-formula> value relative to its predecessors, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GR$ </tex-math></inline-formula> measure, and its extension (called motion in mind model) were investigated. It was discussed from the perspective of game design and game-playing experiences, where artificial intelligence (AI) agents conducted self-playing simulations and verified the findings with empirical data. The condition of play that affects players’ behavior was determined, outlined as the principle of play, and relationships between attractive and addictive games were established.
TraConcept: Constructing a Concept Framework from Chinese Traffic Legal Texts

To construct a legal knowledge graph to support the intelligent computing in the domain of law, a conceptual framework of this domain needs to be set up first. This framework usually comprises domain-specific inter-related concepts, their relations, and their attributes, to achieve a comprehensive understanding of the subject domain. While some of the topics in the task of concept framework construction had been studied, such as the identification of hypernymy/hyponymy relations, other key parts in this task have not been fully investigated. In this work, we propose a system TraConcept, in which we use PLM to encode traffic concept pairs with context to alleviate the “lexical memorization” problem. In addition, we model this task as a multi-relation identification problem and use a Siamese Network with a tensor layer to solve this problem. Compared with state-of-the-art methods, our method is more effective in detecting multi-relations between Chinese traffic concepts and finding attributes of these concepts in a large corpus of traffic legal texts.
Random Grid-Based DIC Analysis of Plastic Flow Near Interfaces in Deformation Processing

Abstract Digital Image Correlation (DIC), an in situ analysis technique, has gained widespread popularity within the mechanics community over the past two decades. Despite this, accurate computation of strain and displacement fields, especially at interfaces and free surfaces, remains a central challenge. This problem is particularly acute since material flow near free surfaces and interfaces is paramount for understanding the mechanics of several deformation processing configurations, such as machining and forming. Two common DIC implementations exist, and they exploit either local or global information about the deformation. Local techniques suffer from a lack of continuity across subsets, while global methods, despite ensuring continuity, fail to estimate fields at interfaces accurately. Furthermore, global DIC necessitates grid refinement to capture heterogeneous deformation and can often be computationally expensive. Both local and global methods finally use interpolation schemes to obtain continuous displacement fields, along with a finite difference scheme to compute strains. However, these present additional limitations, such as spurious strains at interfaces and loss of experimental data. In this work, we present a random grid-based scheme that uses local correlation search, while simultaneously exploiting global information. Our algorithm is based on a forward 6-parameter (displacement and its first order derivatives) Newton-Raphson (N-R) search. An underlying random grid is first generated and serves to locate subset centers for the correlation scheme. Second derivatives are then computed using a triangulation method. Multiple random grid realizations enable averaging with minimal data loss, thereby eliminating the need for post-processing. The use of second-order derivatives ensures continuous strain fields, which will otherwise need a twelve-parameter (displacement, its first and second derivatives) based correlation search. We establish the validity of our scheme using standard test cases derived from synthetic non-homogeneous displacement fields and demonstrate its utility in practical machining and deformation processing applications.
Design and Implementation of Location Algorithm for Rubber tapping robot

In recent years, new agricultural production methods and rapidly developing new technologies have promoted the development of agricultural equipment, giving birth to intelligent equipment and agricultural robots. Tapping robots have great advantages in improving rubber productivity. Aiming at the problem of robot-based navigation and positioning and rubber tree incision recognition, a set of SLAM system based on lidar is designed, which is mounted on the robot to realize navigation and positioning, and uses the combination of monocular and binocular cameras to identify the incision and realize the position of the cutter. In the design of system algorithms such as sensor data reading and motion control, the robot navigation adopts the optimized SLAM framework. The robot's position and direction are determined through the combination of the GPS positioning module and the IMU inertial navigation module, and the rubber tree positioning is realized through the A* algorithm. The incision recognition adopts the canny edge detection algorithm, color detection, artificial marking and other methods to realize the local positioning of the rubber tree rubber tapping cutter. Several comparative experiments were carried out in this paper. According to the analysis of the experimental results, it is verified that the system has achieved the expected goals, and has the characteristics of good accuracy and high real-time performance, which provides a reference for the research and application of robots in the field of rubber tapping.
Protecting the Most Significant Bits in Scalar Multiplication Algorithms

The Montgomery Ladder is widely used for implementing the scalar multiplication in elliptic curve cryptographic designs. This algorithm is efficient and provides a natural robustness against (simple) side-channel attacks. Previous works however showed that implementations of the Montgomery Ladder using Lopez-Dahab projective coordinates easily leak the value of the most significant bits of the secret scalar, which led to a full key recovery in an attack known as LadderLeak [3]. In light of such leakage, we analyse further popular methods for implementing the Montgomery Ladder. We first consider open source software implementations of the X25519 protocol which implement the Montgomery Ladder based on the ladderstep algorithm from Düll et al. [15]. We confirm via power measurements that these implementations also easily leak the most significant scalar bits, even when implementing Z-coordinate randomisations. We thus propose simple modifications of the algorithm and its handling of the most significant bits and show the effectiveness of our modifications via experimental results. Particularly, our re-designs of the algorithm do not incurring significant efficiency penalties. As a second case study, we consider open source hardware implementations of the Montgomery Ladder based on the complete addition formulas for prime order elliptic curves, where we observe the exact same leakage. As we explain, the most significant bits in implementations of the complete addition formulas can be protected in an analogous way as we do for Curve25519 in our first case study.
Interpreting Deep Neural Networks through Model Transformation: Literature Review

Machine learning especially deep learning models have achieved state-of-the-art performances in many fields such as automatic driving, speech recognition, facial expression recognition and so on. However, these models are usually less interpretable which means that it is hard for people to understand and trust the decisions they made. This paper focuses on improving interpretability of deep neural network (DNN) through model transformation, in which the behaviors of DNNs are approximated by transparent models, such as decision tree or rules. We provide a comprehensive literature review for model transformation methods from different aspects, including type of interpretable models, structure of model transformation, and type of model transformation. The characteristics and perspectives of the model transformation approach are also explored.
SISTEM INFORMASI MANAJEMAN UJIAN SEKOLAH BERBASIS WEB PADA SMK MA’ARIF 01 PADANGRATU

School is one of the basic needs of society in order to improve human resources through academic education and other supporting education. Through schools, the community can learn about the development of education science by utilizing the development of technology and information that is increasingly easy to obtain. With the advancement of internet technology, it is no stranger to hear among the public. And the rapid development of technology and information science, researchers aim to create a mobile web-based school exam information system at SMK Ma'arif 1 Padangratu to provide services in carrying out school exams online. Online exams were made to avoid cheating between students and to save time for teachers when correcting exam results. Researchers designed and realized a mobile web-based school exam information system using the PHP programming language and MySQL database. The use of the mobile web for competency development and school quality is the right thing because the mobile web is a very informative and dynamic medium.
Classification of Hate Tweets Using Hybrid Deep Belief Network Algorithm

Social media platforms have presented a way to express the users’ opinions on various topics and connect to friends and share messages, photos, and videos. But there has been an increase in abusive, racial, and hateful messages. As a result, hate tweets have become a significant issue in social media. Detecting hate tweets from Twitter posts with little contextual detail poses several practical problems. Furthermore, the variety of user-generated information and the existence of different hate speech make determining the degree and purpose of the post extremely difficult. A deep belief network with softmax regression is implemented in this work utilizing various embedding techniques for detecting hate speeches in social media. A deep belief network is chosen for resolving the sparse high-dimensional matrix estimation hitch of the text data. Softmax regression is executed to classify the text data in the provided learned feature space, succeeding the feature extraction procedure using hybrid DBN. Experiments are performed on the publicly accessible dataset and evaluate the effectiveness of the deep learning model by considering various metrics.
EFL Learners’ Perceptions Regarding Facebook Use in English Language Learning Proficiency Development at University Level

The purpose of this study was to highlight views of university level learners regarding the use of Facebook for English language learning proficiency development. That is why a descriptive study was conducted among the university level mature learners. Self-developed questionnaire was used for data collection and then the collected data was set, coded and then used SPSS version 24 for its analysis. Different statistics like percentage, mean, standard Deviation, t-test, and one way ANOVA was used for data analysis. Results of the study clearly indicated the positive role of Facebook use in English language learning proficiency development at university level.