Reviewerly/exHarmony huggingface dataset:
Challenges and solutions in the development of blockchain applications: Extraction from SLR and empirical study

Authors:
raw_author_name: Maria Nabi
raw_affiliation_string: Software Engineering Research Group (SERG-UOM), Department of Computer Science and IT University of Malakand Chakdara Pakistan
raw_author_name: Muhammad Ilyas
raw_affiliation_string: Software Engineering Research Group (SERG-UOM), Department of Computer Science and IT University of Malakand Chakdara Pakistan
raw_author_name: Jamil Ahmad
raw_affiliation_string: Software Engineering Research Group (SERG-UOM), Department of Computer Science and IT University of Malakand Chakdara Pakistan

Abstract: Abstract Data protection is the most prior and valuable concern as stealing personal/organizational data by cyber criminals may arise issues ranging from privacy disclosure to system hacking. Blockchain is considered a top leading technology with the influence to reshape future industries, but like other revolutionary technologies, blockchain has its merits and demerits as well. However, due to its nascent stage, blockchain technology poses numerous challenges that hinder the effective development of blockchain. To uncover this potential hindrance and group the relevant barriers, a total of 12 challenges that may hinder the progress of blockchain application development have been identified from a sample of 52 primary studies. Based on the results of our research, the six challenges are considered critical an account for more than 30% occurrence. The critical challenges are “Lack of Proper Development Tools and Technology,” “Security Precaution Measures,” “Lack of Governance and Standards,” “Lack of Professional Expertise with Essential Skills and Knowledge,” “Lack of Organizational Support,” and “Interoperability Integration.” Using a comprehensive systematic literature review (SLR) and a questionnaire survey, a list of 65 solutions/practices has been identified to address the challenges that were identified. These solutions/practices will help blockchain developers to address the identified challenges and develop benign blockchain application in future. The results of our questionnaire survey largely align with the findings of the SLR. However, there are variations in the ranking of the challenges between the two datasets. The finding of this paper is to provide insights that can assist in streamlining and optimizing the development process of blockchain applications with greater ease and efficiency.
Two‐Server Oblivious Transfer for Quantum Messages

Authors:
raw_author_name: Masahito Hayashi
raw_affiliation_string: Graduate School of Mathematics Nagoya University Chikusa-ku Nagoya 464-8602 Japan; International Quantum Academy (SIQA) Futian District Shenzhen 518048 China; School of Data Science The Chinese University of Hong Kong Shenzhen, Longgang District Shenzhen 518172 China
raw_author_name: Seunghoan Song
raw_affiliation_string: Graduate School of Mathematics Nagoya University Chikusa-ku Nagoya 464-8602 Japan

Abstract: Abstract Oblivious transfer is considered as a cryptographic primitive task for quantum information processing over a quantum network. It is an essential building block for secure multiparty computation. It is known that one‐server oblivious transfer is impossible. When the task is the transmission of classical messages, protocols for two‐server oblivious transfer exist, i.e., existing protocols work under the assumption that two servers do not communicate with each other. However, when the task is the transmission of a quantum state, no existing method works even under the above two‐server assumption. Two‐server oblivious transfer protocols for quantum messages are proposed for the first time.
The effectiveness of VR environment on primary and secondary school students’ learning performance in science courses

Authors:
raw_author_name: Juan Chu
raw_affiliation_string: Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, People’s Republic of China
raw_author_name: Liyan Liu
raw_affiliation_string: School of Education and Science, Ningxia Normal University, Guyuan, People’s Republic of China

Abstract: VR technology is being widely used in education due to it's a sense of immersion, real-time interaction, and ability to stimulate imagination. However, there is a lack of research comparing students at different stages. This study used a quasi-experimental design involving 73 fourth-grade and 86 eighth-grade students in two schools in northwest China minority gathering area. One class in each grade level used Virtual Reality (VR) headsets device to teach science content, while the other used interactive whiteboards. The study aimed to examine the differences in students' learning performance, problem-solving ability, self-efficacy, and technology acceptance. The findings showed that VR environment: (a) enhanced the learning performance of primary school students but did not have a significant impact on secondary students; (b) significantly improved problem-solving ability for both primary and secondary school students; (c) significantly promoted self-efficacy for both primary and secondary students; and (d) had a significant influence on technology acceptance for both primary and secondary students. Therefore, we recommended that teachers could integrate VR technology into science labs to enhance secondary students' problem-solving ability and self-efficacy, and improve primary students' learning performance, problem-solving ability, and self-efficacy.
Pelatihan MIT App Inventor sebagai Upaya Meningkatkan Kemampuan Berpikir Logis Siswa SMAN 4 Parepare

Authors:
raw_author_name: Putri Ayu Maharani
raw_affiliation_string: Institut Teknologi Bacharuddin Jusuf Habibie
raw_author_name: Indar Chaerah Gunadin
raw_affiliation_string: Universitas Hasanuddin
raw_author_name: Mardhiyyah Rafrin
raw_affiliation_string: Institut Teknologi Bacharuddin Jusuf Habibie
raw_author_name: Sitti Rahma Yunus
raw_affiliation_string: Institut Teknologi Bacharuddin Jusuf Habibie

Abstract: This community service activity aims to improve the logical thinking abilities of SMAN 4 Parepare students by teaching the application of mathematical logic in compiling computer instructions based on MIT App Inventor. The method for implementing this service was problem-based learning training by taking the example of the traditional game of rock-paper-scissors as a learning case. Next, create a rock-paper-scissors game program using MIT App Inventor as a student learning experience in applying mathematical logic concepts. The evaluation instrument employed was a questionnaire to collect pre-test and post-test results, which were then analyzed using descriptive statistics and paired sample t-tests. The results of this service showed an increase in the average participant score on the post-test results, where the average pre-test score was 7.24 out of 15, while the average post-test score was 9.17 out of 15.
Velocity-aware spatial-temporal attention LSTM model for inverse dynamic model learning of manipulators

Authors:
raw_author_name: Yunhan Lin
raw_affiliation_string: Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan, Hubei, China
raw_author_name: Mingxin Liu
raw_affiliation_string: Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan, Hubei, China
raw_author_name: Huasong Min
raw_affiliation_string: Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan, Hubei, China

Abstract: Introduction An accurate inverse dynamics model of manipulators can be effectively learned using neural networks. However, further research is required to investigate the impact of spatiotemporal variations in manipulator motion sequences on network learning. In this work, the Velocity Aware Spatial-Temporal Attention Residual LSTM neural network (VA-STA-ResLSTM) is proposed to learn a more accurate inverse dynamics model, which uses a velocity-aware spatial-temporal attention mechanism to extract dynamic spatiotemporal features selectively from the motion sequence of the serial manipulator. Methods The multi-layer perception (MLP) attention mechanism is adopted to capture the correlation between joint position and velocity in the motion sequence, and the state correlation between hidden units in the LSTM network to reduce the weight of invalid features. A velocity-aware state fusion approach of LSTM network hidden units' states is proposed, which utilizes variation in joint velocity to adapt to the temporal characteristics of the manipulator dynamic motion, improving the generalization and accuracy of the neural network. Results Comparative experiments have been conducted on two open datasets and a self-built dataset. Specifically, the proposed method achieved an average accuracy improvement of 61.88% and 43.93% on the two different open datasets and 71.13% on the self-built dataset compared to the LSTM network. These results demonstrate a significant advancement in accuracy for the proposed method. Discussion Compared with the state-of-the-art inverse dynamics model learning methods of manipulators, the modeling accuracy of the proposed method in this paper is higher by an average of 10%. Finally, by visualizing attention weights to explain the training procedure, it was found that dynamic modeling only relies on partial features, which is meaningful for future optimization of inverse dynamic model learning methods.
Enhancing teachers’ instruction on how to study: an exploration of the effectiveness of learning strategies for particular secondary school subjects

Authors:
raw_author_name: Gino Camp
raw_affiliation_string: Department of Educational Sciences, Open University, Heerlen, Netherlands

Abstract: Teachers’ instructions on learning strategies play a key role on how students study. However, how the effectiveness of learning strategies can be matched to different types of knowledge in a diversity of subject content has remained unexplored. In the present study, we related the effectiveness of learning strategies to different types of knowledge through an umbrella review. Furthermore, using focus-groups interviews, we explored the relation between school subjects and these knowledge types. We concluded that the effects of particular learning strategies vary across different learning objectives of the subjects and we offered suggestions for the instruction of effective learning strategies.
Collective Displacement of Regional Cryptomarket Vendors—A Study on the Aftermath of the Flugsvamp 3.0 Closure

Authors:
raw_author_name: Kim Moeller
raw_affiliation_string: Department of Criminology, Malmö University, Malmö, Sweden

Abstract: The cryptomarket ecosystem has become increasingly volatile and fragmented with sites shutting down on short notice. Displacement to new marketplaces is tricky when the original location was domestically oriented. We examine the spatial and temporal displacement of 83 Swedish vendors in the aftermath of the Flugsvamp 3.0 shutdown. Vendors rejected the successor Flugsvamp 4.0 and moved to German-run Archetyp Market. Using quantitative cross-sectional data from Archetyp Market we measure bivariate correlations between the temporal displacement and status-related variables. We found moderately strong correlation between vendors’ number of sales per day and the order of their relocation to Archetyp. We also examined cryptomarket discussion forums and blogs during the time of the Flugsvamp 3.0 shutdown. This qualitative data supported the finding that migration choices of high-status vendors inspired others to follow.
Affine combination‐based over‐sampling for imbalanced regression

Authors:
raw_author_name: Zhen‐Zhen Li
raw_affiliation_string: School of Science Kunming University of Science and Technology Kunming Yunnan China
raw_author_name: Niu Huang
raw_affiliation_string: School of Science Kunming University of Science and Technology Kunming Yunnan China
raw_author_name: Lun‐Zhao Yi
raw_affiliation_string: Faculty of Food Science and Engineering Kunming University of Science and Technology Kunming Yunnan China
raw_author_name: Guang‐Hui Fu
raw_affiliation_string: School of Science Kunming University of Science and Technology Kunming Yunnan China

Abstract: Abstract Imbalanced domain prediction analysis is currently one of the hot research topics. Many real‐world data mining analyses involve using imbalanced data to obtain predictive models. In the context of imbalance, research on classification problems has been extensive, but research on regression problems is negligible. Rare values rarely occur in imbalanced regression problems, but the focus is on accurately predicting the continuous target variables of rare instances. One of the challenges in imbalanced regression is finding a suitable strategy to rebalance the original dataset in order to improve the predictive performance of the model in rare instances. In this study, two algorithms are proposed: sigma nearest over‐sampling based on convex combination for regression (SNOCCR) and affine combination‐based over‐sampling (ACOS). ACOS rebalances the original dataset by generating new instances through the affine combinations of the original examples. The region where the new instances are generated can be adjusted based on the distribution of the data, ensuring that the generated cases better mimic the distribution of the original examples. The comparison among ACOS, SNOCCR, and other preprocessing methods was conducted on 15 datasets to validate the predictive performance of models trained on rebalanced datasets for rare instances. The experimental results indicate that ACOS outperforms other existing methods.
Continuous Variable Quantum Teleportation in a Dissipative Environment: Comparison of Non‐Gaussian Operations Before and After Noisy Channel

Authors:
raw_author_name: Chandan Kumar
raw_affiliation_string: Department of Physical Sciences Indian Institute of Science Education and Research Mohali Sector 81 SAS Nagar Punjab 140306 India
raw_author_name: Mohak Sharma
raw_affiliation_string: Department of Physical Sciences Indian Institute of Science Education and Research Mohali Sector 81 SAS Nagar Punjab 140306 India
raw_author_name: Shikhar Arora
raw_affiliation_string: Department of Physical Sciences Indian Institute of Science Education and Research Mohali Sector 81 SAS Nagar Punjab 140306 India; Department of Physics University of Illinois at Chicago Chicago IL 60607 USA

Abstract: Abstract This article explores the relative advantages in continuous‐variable quantum teleportation when non‐Gaussian operations, namely, photon subtraction, addition, and catalysis, are performed before and after interaction with a noisy channel. The resource state for teleporting unknown coherent and squeezed vacuum states is generated using two distinct strategies: i) Implementation of non‐Gaussian operations on two mode squeezed vacuum (TMSV) state before interaction with a noisy channel, ii) Implementation of non‐Gaussian operations after interaction of TMSV state with a noisy channel. The results show that non‐Gaussian operations can enhance teleportation fidelity in a dissipative environment. However, the most advantageous non‐Gaussian operation and appropriate strategy depend on the state and channel parameters. The results suggest that employing such strategies can be advantageous in other non‐Gaussian continuous variable quantum information (QIP) processing tasks in a dissipative environment.
A speed‐area‐efficient hardware ECPM‐engine in GF(<i>p</i>) over generic Weierstrass curves

Authors:
raw_author_name: Yujun Xie
raw_affiliation_string: School of Integrated Circuits Guangdong University of Technology Guangzhou 510006 China
raw_author_name: Yuan Liu
raw_affiliation_string: School of Integrated Circuits Guangdong University of Technology Guangzhou 510006 China
raw_author_name: Xin Zheng
raw_affiliation_string: School of Integrated Circuits Guangdong University of Technology Guangzhou 510006 China
raw_author_name: Shuting Cai
raw_affiliation_string: School of Integrated Circuits Guangdong University of Technology Guangzhou 510006 China
raw_author_name: Xiaoming Xiong
raw_affiliation_string: School of Integrated Circuits Guangdong University of Technology Guangzhou 510006 China

Abstract: Abstract This paper proposes a 256‐bit speed‐area‐efficient hardware elliptic curve point‐multiplication engine (ECPM‐engine) in GF( p ) over generic Weierstrass curves, which is optimized by a new speed‐area‐efficient radix‐64 Montgomery modular multiplication (R64MMM) and a novel Montgomery ladder scheduling. The R64MMM calls one 129‐bit adder and one (64x64+129)‐bit multiply‐accumulator (64‐129‐MAC) in parallel to make a trade‐off between speed and area. The novel Montgomery ladder scheduling is used to improve the utilization of MAC in ECPM operation. In this ECPM‐engine, both MAC utilization in R64MMM operations and R64MMM utilization in ECPM operations are close to 100%. The result shows that the proposed ECPM‐engine consumes 72k gates when the clock frequency is 714 MHz with a 90 nm standard cell library, and it computes one 256‐bit ECPM in 0.14 ms.
Exploring substitution random functions composed of stationary multi-Gaussian processes

Authors:
raw_author_name: Julien Straubhaar
raw_affiliation_string: The Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Emile-Argand 11, 2000, Neuchâtel, Switzerland
raw_author_name: Philippe Renard
raw_affiliation_string: The Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Emile-Argand 11, 2000, Neuchâtel, Switzerland

Abstract: Abstract Simulation of random fields is widely used in Earth sciences for modeling and uncertainty quantification. The spatial features of these fields may have a strong impact on the forecasts made using these fields. For instance, in flow and transport problems the connectivity of the permeability fields is a crucial aspect. Multi-Gaussian random fields are the most common tools to analyze and model continuous fields. Their spatial correlation structure is described by a covariance or variogram model. However, these types of spatial models are unable to represent highly or poorly connected structures even if a broad range of covariance models can be employed. With this type of model, the regions with values close to the mean are always well connected whereas the regions of low or high values are isolated. Substitution random functions (SRFs) belong to another broad class of random functions that are more flexible. SRFs are constructed by composing ( $$Z=Y\circ T$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Z</mml:mi> <mml:mo>=</mml:mo> <mml:mi>Y</mml:mi> <mml:mo>∘</mml:mo> <mml:mi>T</mml:mi> </mml:mrow> </mml:math> ) two stochastic processes: the directing function T (latent field) and the coding process Y (modifying the latent field in a stochastic manner). In this paper, we study the properties of SRFs obtained by combining stationary multi-Gaussian random fields for both T and Y with bounded variograms. The resulting SRFs Z are stationary, but as T has a finite variance Z is not ergodic for the mean and the covariance. This means that single realizations behave differently from each other. We propose a simple technique to control which values (low, intermediate, or high) are connected. It consists of adding a control point on the process Y to guide every single realization. The conditioning to local values is obtained using a Gibbs sampler.
Anomaly sound detection of industrial devices by using teacher-student incremental continual learning

Authors:
raw_author_name: Hao Zhou
raw_author_name: Hu Li
raw_author_name: Wenhao Yang
raw_author_name: Juan Yao
raw_author_name: Yi Chai

Abstract: Abstract Normal production processes will be substantially impacted by industrial devices in abnormal working conditions. Anomaly sound detection (ASD) model can monitor the working condition of devices by the non-contact and non-invasive way. When new device data is introduced, traditional ASD models are trained using data from all devices, to accommodate every device. However, in real-world settings, the kinds and amounts of devices are constantly changing, which raises difficulties for the current ASD models. This paper proposes a teacher-student incremental learning method for ASD models, aiming to solve ASD model scalability problem. In this paradigm, teacher model has knowledge of all the old devices. The objective of student model is to learn new device knowledge, while avoiding the forgetting of old device knowledge. When student model learns new device data, teacher model transfers the acoustic feature knowledge of old devices to student model via knowledge distillation. Furthermore, the imbalance between old and new knowledge causes challenges, such as knowledge forgetting or lower learning efficiency for student model. This paper presents a dual-teacher-student (DTS) model to solve the problem of knowledge imbalance. Different teacher models for new and old devices in DTS, directing student model to accomplish continuous and deep integration of knowledge. Evaluation for proposed method on the DCASE 2020 Task2 dataset. The results show, the proposed method outperforms other methods in terms of learning capability and robustness during the incremental learning process. Analysis of significance test on the experimental results demonstrates that the method outperforms other methods statistically.
Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator

Authors:
raw_author_name: Grace Rhodes
raw_affiliation_string: Eli Lilly and Company , Indianapolis, IN 46204, USA
raw_author_name: Wenbin Lu
raw_affiliation_string: Department of Statistics, North Carolina State University , SAS Hall, 2311 Stinson Dr , Raleigh, NC 27607, USA

Abstract: Summary Clinicians and patients must make treatment decisions at a series of key decision points throughout disease progression. A dynamic treatment regime is a set of sequential decision rules that return treatment decisions based on accumulating patient information, like that commonly found in electronic medical record (EMR) data. When applied to a patient population, an optimal treatment regime leads to the most favorable outcome on average. Identifying optimal treatment regimes that maximize residual life is especially desirable for patients with life-threatening diseases such as sepsis, a complex medical condition that involves severe infections with organ dysfunction. We introduce the residual life value estimator (ReLiVE), an estimator for the expected value of cumulative restricted residual life under a fixed treatment regime. Building on ReLiVE, we present a method for estimating an optimal treatment regime that maximizes expected cumulative restricted residual life. Our proposed method, ReLiVE-Q, conducts estimation via the backward induction algorithm Q-learning. We illustrate the utility of ReLiVE-Q in simulation studies, and we apply ReLiVE-Q to estimate an optimal treatment regime for septic patients in the intensive care unit using EMR data from the Multiparameter Intelligent Monitoring Intensive Care database. Ultimately, we demonstrate that ReLiVE-Q leverages accumulating patient information to estimate personalized treatment regimes that optimize a clinically meaningful function of residual life.