Reviewerly/exHarmony huggingface dataset:
Demonstration of 75 km-fiber quantum clock synchronization in quantum entanglement distribution network

Abstract The quantum entanglement distribution network, serviced as the communication infrastructure which distributes quantum information among remote users, enables many applications beyond the reach of classical networks. Recently, the applications such as quantum key distribution and quantum secure direct communication, have been successfully demonstrated in the quantum entanglement distribution network. In this article, we propose a multi-user round-trip quantum clock synchronization (QCS) scheme in the quantum network, which can be implemented with one single entangled photon source located at the server. The server distributes the entangled photons to remote multiple users with the wavelength division multiplexing strategy, and each user feeds partial received photons back to the server. The clock difference between the server and each user is calculated from the one-way and round-trip propagation times, which are determined according to the time correlation of entangled photons. Afterwards, the demonstration has been conducted between the server and a user over a 75-km-long fiber link, where the measured clock difference uncertainty is 4.45 ps, and the time deviation is 426 fs with an average time of 4000 s. Furthermore, the proposed QCS scheme is linearly scalable to many users, with respect to user hardware and number of deployed fibers.
Robust retrieval of material chemical states in X-ray microspectroscopy

X-ray microspectroscopic techniques are essential for studying morphological and chemical changes in materials, providing high-resolution structural and spectroscopic information. However, its practical data analysis for reliably retrieving the chemical states remains a major obstacle to accelerating the fundamental understanding of materials in many research fields. In this work, we propose a novel data formulation model for X-ray microspectroscopy and develop a dedicated unmixing framework to solve this problem, which is robust to noise and spectral variability. Moreover, this framework is not limited to analyzing two-state material chemistry, making it an effective alternative to conventional and widely used methods. In addition, an alternative directional multiplier method with explicit or implicit regularization is applied to obtain the solution efficiently. Our framework can accurately identify and characterize chemical states in complex and heterogeneous samples, even under challenging conditions such as low signal-to-noise ratios and overlapping spectral features. By testing six simulated datasets, our method improves the existing methods by up to 151.84% and 136.33% in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) for the chemical phase map. Extensive experimental results on simulated and real datasets demonstrate its effectiveness and reliability.
Research Status and Thinking of Service Failure and Service Recovery of Service Robot

With the development of intelligent technology, service robots have appeared in various service first-line environments, and consumers will inevitably encounter service failure of service robots. This paper analyzes the types of service failure by combing the relevant literature on service failure and service recovery, and based on the social cognitive theory, clarifies the remedial measures that should be taken after service robot fails. It is found that after process failure, the service robot makes emotional response such as apology or empathy, the service recovery effect is better; When the result fails, the service robot makes rational response such as explanation, the consumer's attitude is more positive. The research results provide practical enlightenment for enterprises to carry out service recovery.
The Effect Of Using Think Pair Share Stragey On Students’ Writing Ability In Descriptive Text At Seventh Grade Students Of Junior High School

This study examined the effect of the think-pair-share strategy on seventh grade students at the junior high school in MtsS Ashhabul Yamin, Lasi Tuo's ability to write descriptive text. The background for this study was the students' difficulties with writing when learning English. First, most of the students did not understand well how to make a short paragraph. Second, many of the students had limited of vocabulary. Third, some of the students were difficult to writing the sentences that has the correct grammar. In this study, the researcher compared two classes using a quantitative method and a quasi-experimental research design. The sample of this study were junior high school students in the seventh grade at MTsS Ashhabul Yamin, Lasi Tuo, specifically 7.6 as the experimental class with 28 students and 7.9 as the control class with 27 students. The instrument of this research was writing test. The data was analyzed by using independent sample t-test and paired sample t-test in SPSS 22. The result of the research indicated that there was significant effect of students writing ability between the students who were taught by using think pair share strategy and without using think-pair-share strategy. It can be seen from the value of sig. (2-tailed) which was smaller than the alpha value (0,000&lt;0,05). From the result of the data, it can be concluded that, using think pair share strategy gave significant effect of writing ability in descriptive text.
A Simple Nonlinear Classifier Using a Multimode Optical Chip

Neural network accelerator based on photonic‐integrated circuits is emerging as a promising technology for fast, power‐efficient, and parallel computing. Under such technology, optics is mainly used for linear transformations, e.g., through an array of cascaded switching networks. Nonlinear activation is implemented either electronically requiring extra optical–electrical conversion or via nonlinear optical materials that often suffer from high loss, large power consumption, and difficulty in integration. Herein, an optical neural chip with only one multimode waveguide, fabricated using low‐cost linear optical materials, plus seven heater electrodes to control the multimode interference, is proposed. The nonlinear networks are intrinsically integrated in the electrical‐to‐optical signal conversion through the waveguide. The linear computation, in the electronic domain, is included in the mandatory step to convert the input matrix to the intermediate current values on the seven electrodes. Though extremely simple, the proposed system can classify nonlinear datasets and images by optical readout with high accuracy and without calibration. Prospects for future development are given at the end. In this work, an alternative route is offered to exploiting the classic multimode interference for advanced optical computing applications.
Financial Literacy Plus New Purchasing Power, How Ledger Economics can End Poverty and Promote Continuous Economic Improvement

Financial Literacy plus new purchasing power can drive rapid and environmentally sustainable, local-to-global, economic development. Historically, new technologies promote new forms of money and commerce that usher in new economic eras. This chapter is for leaders and innovators in financial services and sustainable economic development. It reveals an emerging era of sustainable prosperity for all. The world can now eradicate centuries-old poverty and inequality at the pace of mobile apps and social media. The funding for this paradigm shift is a next-generation financial instrument and not higher taxes, deeper debt, or redistribution of wealth schemes. The chapter introduces the first token-less ledger currency that is distributed through a public Business-Community Wealth Ledger (BCWL). Dual Currency transactions integrate fiat currencies with wealth-backed ledger currencies, monetizing and mobilizing currently underutilized business resources and increasing profits for participating businesses.
CDMnet: Cloud Detection in Remote Sensing Images Based on CNN

Abstract The main objective of cloud detection is to accurately distinguish between clouds and areas that are not clouds to extract accurate surface information. To achieve an accurate distinction between cloud and non-cloud regions, we propose a novel method called Cloud Detection Method Net (CDMnet). The method employs a lightweight network backbone, compared to traditional classical algorithms like SVM, to effectively reduce computational costs while maintaining accuracy. Additionally, we utilize hole convolutions with varying dilation rates to extract cloud features in parallel. We also employ SAM spatial attention multiple times to fine-tune cloud position positioning and obtain more accurate cloud masks. From the experimental results, it can be concluded that our method is more efficient than other traditional methods.
Active Learning—Methodology

Since the beginning of the computer era, researchers have been curious whether they can be made to learn. This led to the development of various algorithms and programs, which eventually got better with time, inevitably advancing to human-level performances.
MOTIVATION FOR LEARNING ACTIVITIES: AN ANALYSIS OF MODERN THEORIES

The article presents and analyzes modern theories of motivation for learning activities of higher education students. The classification of the main factors of motivation for the educational and professional activity of higher education students, which include personal, pedagogical, professional and socio-psychological factors, is described. The division of motivation for professional activity according to the structural-level principle is determined, and the motivational influences that form the readiness of future specialists for practical activity are established. The main modern view of motivation is based on the theory of self-efficacy, which focuses on convincing a person that he or she is able to complete a task or cope with it. It has been established that the main task in the educational process is not only high professional training of students, but also the holistic development of the individual - a person with an active life position, capable of self-determination, self-development, conscious professional activity and self-regulation. The nature of the motives for students' learning activities is significantly influenced by motivation (both internal and external), focus, meaningfulness, satisfaction with the process, and clear goals. Student success is determined by many factors, including natural abilities and the development of motivation to learn. It is known that there is a rather complex system of interrelationships that, under certain conditions, affects the entire compensatory mechanism of the individual. Thanks to such activities, the lack of abilities is compensated by the development of motivation, which can lead to quite high results in student learning. Correct identification of professional motives, interests, and aptitudes is an important predictor of future career satisfaction. Attitude to the chosen professional activity, motives for its choice are extremely important factors that determine the success of professional training and mastering professional skills in general. The problem of motivation and motives of behavior and activity is one of the main ones in psychology.
Implementation of iPad-based digital classroom services at al Azhar Islamic school 21 Solo Baru, Central Java, Indonesia

The need for digital innovation in the educational paradigm is increasingly clear after the Covid-19 phenomenon. This has also been strengthened by government policies in responding to this need, by launching “kurikulum merdeka” that demands the use of “merdeka mengajar” platform with all its characteristics. The concept of digital learning with a variety of devices is offered. SMP Islam Al Azhar 21 Solo Baru has implemented a program that is quite progressive in an effort to find an education digitalization system. It is in the form of digital class services, which can be used as an option for students and parents to carry out all digital-based and e-learning activities. Furthermore, the digital system used is relatively new and not many are used in Indonesia. The use of iPad 1:1 (every 1 student uses 1 iPad) as the main device with various Apple for education applications integrated in it. Regarding to this implementation, there is an interest from the researcher for further research related to its implementation. This type of research is descriptive qualitative which reports on how the school’s role in implementing iPad and its functions and contributions to classroom learning. The description regarding this matter was developed based on the results of semi-structured interviews and non-participant observations to see how this implementation could be carried out. Student group interviews were also used to support the findings. The result is an overview of the iPad-based digital classroom implementation starting from the initial ideas, the stages and steps of implementation, the perceptions of students and teachers, and the evaluation of those implementation. On the other hand, the main challenge lies in the relatively high budget requirements, and parents' doubts about the bad consequences of excessive iPad use, both in terms of health and social behavior
Adversarial Detection from Derived Models

Deep Neural Networks (DNNs) can be easily fooled by inputs that are crafted by adversaries. For example, an adversarial image can be forged by adding to an image a tiny perturbation which is often unnoticeable by human eyes, though the semantic interpretations of the original image and the adversarial image, which are represented as outputs of a DNN, may be drastically different. This weakness can potentially lead to serious consequences in security-critical applications such as medical diagnostic tests and self-driving vehicles. Most existing approaches for adversarial detection only have satisfactory performance for specific types of attacks. These methods do not generalize their performances when applied to a broad range of attacks, models or datasets. In this work, we propose a new adversarial detection method called Adversarial Detection from Derived Models (ADDM), which applies derived models to “simulate” the functionality of a DNN, and analyzes the distribution for the neuron activation values in the derived models as indicators for adversarial inputs. In order to further enhance performance, we propose a heuristic that selects neurons from the derived models that are sensitive to perturbations. We compare our approach with six existing adversarial detection approaches of different methodologies, and the experimental result confirms that the proposed approach has generally better performance regarding stability over different types of adversarial attacks on a variety of tested DNN models and datasets.
Effectiveness of the Online Service Management Information System (SIMPONIE) Application in Online Business Licensing Services

This research discusses the effectiveness of the SIMPONIE application in improving public services in South Tangerang City according to the information system success measurement developed by William H. Delone and Ephraim R. Mclean (2016). This application is the result of a joint initiative between the Department of Investment and One Stop Integrated Services (DPMPTSP) and the Department of Communication and Information (Diskominfo) of South Tangerang City. SIMPONIE is designed with a number of features that aim to support the licensing process digitally, with the main aim of providing easy access for the public in obtaining business permits, increasing efficiency in the licensing process, and increasing the capability of government departments in serving the public. The features offered by SIMPONIE include the ability to register and apply for business permits online, tracking application status, payment of licensing fees online, notifications and communication between applicants and authorities, as well as electronic data archiving for documentation and reporting. According to research results, the SIMPONIE application has received a positive response from the people of South Tangerang City for many years. This positive reception reflects success in overcoming the challenges faced in business licensing services at the regional level. This success provides a concrete example of how information technology innovation can be used to improve efficiency, transparency and service to the community.
Cyber Insurance in Malaysian Organisations: An Introductory Journey

Over the past century, many profound technological, economic, and social transformations have occurred. 21st century emerged as of technology and the borderless world; businesses have embraced more strategic ways to handle risk in computing; Cyber Risk Management (CRM) has become one of the essential components in risk management initiatives that seek to mitigate and analyse the multitude of new risks. One risk mitigation process is investing in cyber insurance to safeguard IT assets from cyber threats by transferring such risks to another party known as the insurer. However, implementing cyber insurance in Malaysia is still a considerable organisationsal gap. Unlike any other insurance purchased by default due to financial obligations, many organisations in Malaysia are still lackadaisical towards cyber insurance. The research looks into the factor that contributes to the gap. The study starts with identifying the research background and problem statement and then collecting the data. 30 semi-structured interviews were conducted. The data was collected from 30 experienced Malaysian organizations in the public and private sectors. Finally, the outcome is analysed and concluded.
Complex Numbers in Modern C Programming

In this chapter, we explain complex number data type used in C programming. Complex numbers are frequently used in electrical and computer engineering applications. Complex number header file is available in C language since the release of C99 standard, and it is included in C11, C17, and C23 standards. The C language provides powerful tools for the calculation of operations involving complex numbers. For instance, trigonometric operations involving complex numbers, such as sine of a complex number, can be easily done using built-in functions in C libraries.
Directives and Macros in C

In this chapter, we explain the use of directives and macros in C programming. Directives and macros are preprocessed units of C programs. They are expanded in C programs to form the final version of the C programs before compilation operation. Both built-in macros served from compiler’s library, and the ones written by code developer can be used inside the C program.
Arrays

In this chapter, we explain arrays in C programming. Arrays can be considered a group of variables belonging to the same data type, and these variables are placed in a sequential manner in memory. Arrays can be used to keep some records. For instance, midterm and final marks of students for a course kept in an array.
Type Qualifiers, Enumerations, and Storage Classes in C

In this chapter, we explain type qualifiers, enumerations, and storage classes in C programming. The primary type qualifiers are const, restrict, and volatile. Storage classes are auto, extern, static, and register. The keywords restrict, volatile, and register are especially important for hardware programmers, whereas the keywords const, auto, and extern are of equal importance for every C programmer.
Dynamic Virtual Machine Consolidation for Energy Efficiency in OpenStack-based Cloud

The rapid growth of cloud computing demands efficient resource allocation to maximize performance while minimizing energy consumption. Scalability improves, and up-front expenditures for IT infrastructure are decreased due to cloud computing. As the number of virtual machines (VMs) in a cloud environment grows, guaranteeing appropriate resource allocation and utilization becomes a difficult issue. Inefficient resource allocation can result in underutilized resources, energy waste, and increased operational expenses. Our research aimed to contribute to this objective by proposing a dynamic consolidation approach. We evaluated existing consolidation methods and identified their shortcomings. Many earlier methods lacked the adaptability and flexibility necessary to optimize energy use. We presented a consolidation technique that takes the hosts’ and VMs’ computing factors into account to address these flaws. The modules in this technique generate diverse and realistic environments for hosts and VMs, allowing for comprehensive evaluation. The Energy-Efficient Consolidation module serves as the core component, utilizing algorithms to optimize migration decisions. The technique effectively migrated virtual machines (VMs) from underutilized hosts to more effective ones, resulting in energy savings of 27% and 35% in the two scenarios, respectively. These results show how our method may help create cloud computing environments that are more environmentally friendly and energy-efficient. The implementation of our strategy in a real-time Multi-Node OpenStack testbed adds practicality and relevance to our findings.
Cyber Security Implementation Issues in Small to Medium-sized Enterprises (SMEs) and their Potential Solutions: A Comprehensive Analysis

As the tech industry is advancing day by day, the digital data they store, their dependency on technology and other resources is also enlarging. Although large businesses account for returning high returns and turnover, Small and Medium-sized Enterprises (SMEs) also have a large market share in this evolving era of technology. As these SMEs are becoming dependent on technology and moving towards the cyber space, the cyber security risks for these companies are also increasing. In current scenario, providing cyber security is more inclined towards large businesses, but what about the SMEs? Today, SMEs are not well prepared to defend themselves from cyber threats and attacks, and some of them can't even detect data breaches and manage cyber security risks. In addition, the mindset of the people within these SMEs is like "We SMEs are too small for any cyber-attack or threat". But this is just a myth and a mistake. Since they are small, lack resources and capital, SMEs become one of the prime targets of hackers. Therefore, this review paper gives a literature review and insights about the research conducted so far around the world regarding the cyber security problems faced by SMEs and how they can cope with cyber security threats and defend themselves against malicious cyber-attacks.
Comparative Analysis of Multi-Model and Uni-Model Approaches using Time Distributed Bidirectional LSTM for Multidata Sentiment Analysis

The sentiment of the data contained in a text can be determined by analysis. This sentiment can be good, negative, or neutral. When carrying out this research, one may choose to employ either a uni-model or a multi-model technique. In the uni-model method, both the training and the analysis are performed using just a single model. The application of this strategy is uncomplicated and uncomplicated, but it might not be able to make sense of complicated or different facts, which would lead to wrong findings. Instead, the multi-model technique utilizes a number of different models, each of which is designed to examine a particular aspect of the data or a particular kind of attitude. This allows the multi-model strategy to produce more accurate results. The results of several different models might be combined in order to obtain a more in-depth understanding of feelings. This method is more accurate overall and captures more complex expressions of emotion, but it requires more time and money to train personnel and put into practice. The evaluation of metrics was carried out using Multimodal and mono Model with Time Distributed Bidirectional LSTM in this research. Both the loss and the accuracy. The Multi-Model achieves the highest accuracy of 99.71 percent for the train, while the validation accuracy achieves 99.32 percent, and the Multi-Model achieves the least loss of 0.010 percent.