Reviewerly/exHarmony huggingface dataset:
Application of Blockchain Hierarchical Model in the Realm of Rural Green Credit Investigation

In order to realize the application research of blockchain technology in the field of green credit investigation, the current paper adopts the method of a blockchain hierarchical model to study the rural green credit. With regard to the realm of rural green credit investigation, this paper sorts out the characteristics of credit data in China’s countryside by countryside credit investigation and determines the major problems and in rural green credit investigation of financial inclusion. Subsequently, the authors put forward a blockchain hierarchical model, which not only has reinforced the advantages in original blockchain dedicated to agriculture, rural areas and rural residents, such as traceability and immutability, but also has transformed the decentralization into disintermediation and changed the single-layered P2P network into a multilayered structure based on China’s rural financial environment. Finally, the authors collect and extract the proper credit investigation data on the rural internet to assess the application value of the model by investigating its practical applicability in reality and problems that may occur during the application of the model. Results show that private credit information has an important impact on the prediction accuracy, and the blockchain hierarchical model is helpful to ensure the reliability and security of rural green credit data.
UKF-Based Sensor Fusion Method for Position Estimation of a 2-DOF Rope Driven Robot

In this study, the unscented Kalman filter-based method was introduced as a new technique for position estimation of the two-degree-of-freedom façade cleaning robot known as the Dual Ascender Robot (DAR). While other façade cleaning robots use a winch, the DAR uses an ascender, resulting in rope slip inside the ascender. Rope slip does easily cause errors in length data, so DARs cannot be easily controlled based on length data as in the case of most façade cleaning robots. Therefore, the DARs estimate the length data and use it through position estimation to overcome the rope slip for control. DARs use a rope length-based sensor fusion method for position estimation. This method employs position data based on both length data and angle data to estimate the position; however, it is difficult to use for long periods of time owing to the increased error that accumulates with time. Therefore, the use of position data based on angle data is proposed herein via application of the unscented Kalman filter. This unscented Kalman filter-based method is tested to confirm that the positional estimation performance is improved relative to that achieved via the previously used method. The performance improvements are compared in terms of accuracy and repeatability using the double ball bar method, and the errors in accuracy and repeatability are found to be reduced by approximately 2-3 times.
SPSS Syntax for Combining Results of Principal Component Analysis of Multiply Imputed Data Sets using Generalized Procrustes Analysis

Keywords missing data, multiple imputation, principal component analysis, generalized procrustes analysis
Blockchain technology for sustainable supply chains of agri-food in Vietnam: a SWOT analysis

Such a disruptive technology as the blockchain platform has a potential to provide value-added contributions towards multiple-disciplinary applications these days. Blockchain technology, considered as the second generation of the Internet in the digital age, has been applied in practice for various industry such as finance, healthcare, tourism, retailing, manufacturing, education, public sector, supply chains management, agri-food industry and so on. In which, supply chain operations, particularly agri-food management, becomes a prosperous beneficiary from blockchain technology. In the local context, Vietnam has been achieved the competitive advantages in agricultural production among the world in some agri-food products such as black peppercorn, cashew nuts, coffee, coconut, rice, and rubber. Furthermore, Vietnam is considered as one of pioneers in implementing blockchain technology for traceability and transparency of agri-food products across Association of South East Asian Nations (ASEAN) countries. However, design and implementation of the blockchain technology application in agri-food production in the case of Vietnam agriculture industry have been confronted with opportunities and challenges, besides its strengths and weaknesses in nature. This paper aims to concentrating on the analysis of Strengths, Weakness, Opportunities and Threats (SWOT) of blockchain-applied supply chain relevant to agri-food products. In addition, the application of blockchain technology in supply chains has been expected to promote the sustainable development of Vietnam agriculture, in the accordance with the United Nation Sustainable Development Goals (SDGs) in the 21st century. We adopt the research method of interpretive approach to synthesize the literature and reasonably induce research conclusions. We finally prescribe some policy recommendations and implications for agri-food stakeholders such as regional policy makers, agri-food based blockchain platform designers, executives, and farmers as users in relevant to facilitate the blockchain technology for agri-food chains in Vietnam agriculture.
Secure quantum key distribution with a subset of malicious devices

Abstract The malicious manipulation of quantum key distribution (QKD) hardware is a serious threat to its security, as, typically, neither end users nor QKD manufacturers can validate the integrity of every component of their QKD system in practice. One possible approach to re-establish the security of QKD is to use a redundant number of devices. Following this idea, we address various corruption models of the possibly malicious devices and show that, compared to the most conservative model of active and collaborative corrupted devices, natural assumptions allow to significantly enhance the secret key rate or considerably reduce the necessary resources. Furthermore, we show that, for most practical situations, the resulting finite-size secret key rate is similar to that of the standard scenario assuming trusted devices.
Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Era

NISQ devices have several physical limitations and unavoidable noisy quantum operations, and only small circuits can be executed on a quantum machine to get reliable results. This leads to the quantum hardware under-utilization issue. Here, we address this problem and improve the quantum hardware throughput by proposing a Quantum Multi-programming Compiler (QuMC) to execute multiple quantum circuits on quantum hardware simultaneously. This approach can also reduce the total runtime of circuits. We first introduce a parallelism manager to select an appropriate number of circuits to be executed at the same time. Second, we present two different qubit partitioning algorithms to allocate reliable partitions to multiple circuits - a greedy and a heuristic. Third, we use the Simultaneous Randomized Benchmarking protocol to characterize the crosstalk properties and consider them in the qubit partition process to avoid the crosstalk effect during simultaneous executions. Finally, we enhance the mapping transition algorithm to make circuits executable on hardware using a decreased number of inserted gates. We demonstrate the performance of our QuMC approach by executing circuits of different sizes on IBM quantum hardware simultaneously. We also investigate this method on VQE algorithm to reduce its overhead.
Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data

In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.
Daytime Cloud Detection Method Using the All-Sky Imager over PERMATApintar Observatory

In this study, we explored a new method of cloud detection called the Blue-Green (B-G) Color Difference, which is adapted from the widely used Red-Blue (R-B) Color Difference. The objective of this study was to test the effectiveness of these two methods in detecting daytime clouds. Three all-sky images were selected from a database system at PERMATApintar Observatory. Each selected all-sky image represented different sky conditions, namely clear, partially cloudy and overcast. Both methods were applied to all three images and compared in terms of cloud coverage detection. Our analysis revealed that both color difference methods were able to detect a thick cloud efficiently. However, the B-G was able to detect thin clouds better compared to the R-B method, resulting in a higher and more accurate cloud coverage detection.
A Dense Connection Based SAR Ship Detection network

Ship targets are important carriers of maritime behavior, and ship target detection has important economic and military value. Owing to its excellent characteristics, synthetic aperture radar (SAR) is widely used to detect sea targets. SAR ship detection has always been a popular field of research. The excellent performance of deep neural networks in computer vision has facilitated their application to SAR image detection. However, several problems remain in SAR ship detection at this stage. First, SAR ship targets differ significantly in size, and how to deal with multi-scale ship detection has, therefore, been a difficult research topic. Second, the size of a SAR ship target is small in low-resolution images, and thus, dealing with small size target detection is also difficult. Therefore, we propose a dense connection DetNet (DDNet), which is specifically used for SAR ship detection. The network consists of two parts: backbone sub-network and prediction sub-network. In the backbone subnetwork, we refer to DetNet, and use a stacked convolution layer instead of large downsampling to make it more suitable for small ship detection. In the prediction sub-network, we use a dense connection to fuse features of different scales, allowing it to better deal with multi-scale ship detection. The feature reuse strategy is used to improve the parameter efficiency. The experiments on SAR ship datasets show that our method performs better than other methods in small-size and complex background ship detection.
A Low-Cost and Fast Vehicle Detection Algorithm With a Monocular Camera for Adaptive Driving Beam Systems

The adaptive driving beam headlamp system is helpful to solve the traffic safety problems caused by the abuse of high beams at night. The fundamental problem for the adaptive driving beam system is to detect and track the vehicles at specific night environment for light shape control. This paper proposes a new and efficient algorithm for detecting and tracking based on video collected by a low-cost camera. The related automobile regulations are analyzed for classifying the application scenarios of the adaptive driving beam system and summarizing the performance requirements of the algorithm. A standard CMOS camera mounted behind windshield captures the test video from different scenarios. Some preprocessing approaches are designed to optimize the captured video so that the algorithm can work independently on specific camera. The color and morphological characteristics of the rear lights are utilized to extract the rear lamps of the vehicle. The symmetry of rear lights is checked by a correlation coefficient method to pair the rear lamps and determine the vehicle ahead preliminary. Then, the Hungarian algorithm and Kalman filter are performed to track the multiple occurrences in two consecutive frames and correct the detection results. Finally, an estimation method is given for calculating the vehicle position in real world. The experiments are designed according to referred regulations and the test video is obtained from a low-cost camera mounted on test vehicles driving in specific scenarios. The experimental results demonstrate that the algorithm can get high detection rates and adaptability to the working condition of adaptive driving beam system. Moreover, the proposed algorithm has low time cost and can be applied in embedded devices of the vehicle.
Interleaving Graph Search and Trajectory Optimization for Aggressive Quadrotor Flight

Quadrotors can achieve aggressive flight by tracking complex maneuvers and rapidly changing directions. Planning for aggressive flight with trajectory optimization could be incredibly fast, even in higher dimensions, and can account for dynamics of the quadrotor, however, only provides a locally optimal solution. On the other hand, planning with discrete graph search can handle non-convex spaces to guarantee optimality but suffers from exponential complexity with the dimension of search. We introduce a framework for aggressive quadrotor trajectory generation with global reasoning capabilities that combines the best of trajectory optimization and discrete graph search. Specifically, we develop a novel algorithmic framework that interleaves these two methods to complement each other and generate trajectories with provable guarantees on completeness up to discretization. We demonstrate and quantitatively analyze the performance of our algorithm in challenging simulation environments with narrow gaps that create severe attitude constraints and push the dynamic capabilities of the quadrotor. Experiments show the benefits of the proposed algorithmic framework over standalone trajectory optimization and graph search-based planning techniques for aggressive quadrotor flight.
The calibration method of the circle-structured light measurement system for inner surfaces considering systematic errors

Abstract Light plane deviation and motion direction deviation of stage systems are the two main sources of systematic errors in circle-structured light inner surface measurement systems. In this paper, we propose a new two-step calibration method to compensate these systematic errors. In the first step, a more accurate initial light plane calibration result is obtained by multi-position binocular-structured light calibration. Then, in the second step, the initial result is optimized by measuring the inner radius of a standard ring gauge to obtain the optimal light plane calibration result. Based on this result, the real motion direction is also calibrated by optimization using the inner surface scanning measurement data of the standard ring gauge. The proposed method improves the system calibration accuracy through the second calibration step, making it convenient to operate without using complex calibration targets or expensive equipment. Therefore, this calibration method has the potential to be used in actual measurements. The experimental results show that the mean relative measurement error can reach 0.015% in our homemade measurement system and validate that the measurement system calibrated by our proposed method realizes high-precision and high-repeatability measurements for inner surfaces.
Virtual place during quarantine – a curious case of VRChat

During the first months of the COVID-19 pandemic many governments imposed forced lockdowns and implemented social distancing measures. At the same time there was also a large increase in gaming sales, which was particularly pronounced in the Virtual Reality (VR) sector of the market. We hypothesize that this is no coincidence since VR immersion and the capability of inducing embodiment and a feeling of presence can mitigate the loss of contact with outside world. VR has social and spatial potential to provide space and place for human interactions in time when physical contracts are restricted. To investigate this, we analyse reviews of VRChat (a social VR game) posted on the Steam platform, both before and during the pandemic. Among several themes that were identified, we found indications that spatiality plays an important role in the players’ experience. Users describe virtual worlds of the game using emotional language that suggest bonding and presence of place attachment. In the reviews made during the pandemic there is a strong theme of safety associated with virtual places of VRChat – a replacement of physical space that is no longer accessible or is perceived as unsafe. At least for some users, VRChat has provided a sympathetic and comfortable environment during the pandemic to act as a surrogate for social interaction during social distancing and isolation. Future interviews with users are needed to extend and validate this preliminary research.
THE INFLUENCE OF PERSONALITY AND LANGUAGE PROFICIENCY ON ESL LEARNERS' WORD CONTRIBUTIONS IN FACE-TO-FACE AND SYNCHRONOUS ONLINE FORUMS

Background and Purpose: Recent technological advancements and Covid-19 pandemic have prompted more opportunities for online learning. However, there is a dearth of empirical research that is focused on high school students. Therefore, this study aims to obtain a more comprehensive view of ESL learners’ involvement or word contributions in a ‘normal’ face-to-face situation and in a synchronous online text-based environment by relating it to their personality and language proficiency.&#x0D; &#x0D; Methodology: In this study, a quasi-experiment was carried out over four weeks. It involved eight sessions of face-to-face and online discussions with 48 Malaysian high school students who were divided into two matched-sample groups. The participants' feedback was used to support and provide insights on the findings. The quantitative data were analysed using using IBM SPSS statistics 26 software while the participants’ responses to the online feedback session were analysed using open coding and axial coding strategies.&#x0D; &#x0D; Findings: Statistically significant difference was found in learners’ word contributions during face-to-face and online discussions. Apart from the extroverts with high-intermediate language proficiency, the other groups of learners who are either introverts or having lower level language proficiency were found to produce significantly more words in synchronous online text-based discussions. The non-face-to-face context of online forums might have lowered the learners’ level of anxiety and increased their confidence to interact with their peers.&#x0D; Contributions: Learners’ personalities and language proficiency levels should be considered when choosing a discussion setting and when facilitating discussion activities. More support should be provided for the less proficient or introverted learners to express themselves in a face-to-face discussion. &#x0D; &#x0D; Keywords: Personality, Language proficiency, Computer-mediated communication, Synchronous text-based online discussions, Word contributions&#x0D; &#x0D; Cite as: Chew, S. Y., &amp; Ng, L. L. (2021). The influence of personality and language proficiency on ESL learners' word contributions in face-to-face and synchronous online forums. Journal of Nusantara Studies, 6(1), 199-221. http://dx.doi.org/10.24200/jonus.vol6iss1pp199-221
ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on Display

We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility.
EXPLICIT ESTIMATION OF DERIVATIVES FROM DATA AND DIFFERENTIAL EQUATIONS BY GAUSSIAN PROCESS REGRESSION

In this work, we employ the Bayesian inference framework to robustly estimate the derivatives of a function from noisy observations of only the function values at given location points, under the assumption of a physical model in the form of a differential equation governing the function and its derivatives. To overcome the instability of numerical differentiation of the fitted function solely from the data or the prohibitive costs of solving the differential equation on the whole domain, we use the Gaussian processes to jointly model the solution, the derivatives, and the differential equation, by utilizing the fact that differentiation is a linear operator. By regarding the linear differential equation as a linear constraint, we develop the Gaussian process regression with the constraint method (GPRC) at the Bayesian perspective to improve the prediction accuracy of derivatives. For nonlinear equations, we propose a Picard-iteration approximation of linearization around the Gaussian process obtained only from data to iteratively apply our GPRC. Besides, a product of experts method is applied if the initial or boundary condition is also available. We present several numerical results to illustrate the advantages of our new method and show the new estimation of the derivatives from GPRC improves the parameter identification with fewer data samples.