Reviewerly/exHarmony huggingface dataset:
Tingkat Pengetahuan Kesiapsiagaan Bencana Banjir Masyarakat Kelurahan Bukit Duri, Kecamatan Tebet, Jakarta Selatan

This study aims to determine the level of knowledge of flood disaster preparedness of the people of Bukit Duri Village, Tebet District, South Jakarta. The method used in this study is a quantitative descriptive method using a scoring technique. The population in this study were all residents in RW 10, 11, and 12 Bukit Duri Village, Tebet District, South Jakarta, amounting to 12,955 people. The sample in this study were residents affected by flooding with productive age, namely the age of 15-65 years in the area of RW 10, 11, and 12, Bukit Duri Village, Tebet District, South Jakarta, totaling 100 respondents. The sampling technique is proportional random sampling. Based on the results of the study that the level of knowledge of flood disaster preparedness of the people of Bukit Duri Village has a percentage of 84.7% and is included in the high category. This high level of knowledge was obtained from the experience of the respondents who lived in Bukit Duri for a long time. From the results received from respondents, as many as 91 respondents have lived in Bukit Duri for more than 10 years. This shows that the level of knowledge of the Bukit Duri community's flood preparedness is influenced by the community's experience.&#x0D; Keywords: Knowledge, Preparedness, Flood
Technology for Understanding Service Impact Using Network Resource Management Technology that Is Independent of Network Type

In large-scale and diverse networks of telecommunications carriers, it is difficult to quickly understand the effects on communication services due to damaged communications buildings and cables.NTT Access Network Service Systems Laboratories is researching and developing network resource management technology that enables unified management of various networks.In this article, we introduce technology for understanding service impact during a large-scale disaster using this network resource management technology.
Provably-Secure LED Block Cipher Diffusion and Confusion based on Chaotic Maps

Lightweight cryptography algorithms have concentrated on key generation's randomness, unpredictable nature, and complexity to improve the resistance of ciphers. Therefore, the key is an essential component of every cryptography algorithm since it affects the algorithm's level of security. Light Encryption Device (LED) is a high-performance, lightweight block encryption solution that works on resource-constrained devices and considers a lighter version of AES. It employs a 64-bit block cipher with two significant instances using 64-bit and 128-bit keys, respectively. A lack of key scheduling in LED heightens security risks, such as key-related attacks. Specifically, now that LED has been hacked and is no longer secure. Therefore, LED must achieve a high diffusion and confusion level to withstand known attacks. Chaos-based encryption provides an exceptionally high level of security because of the unique characteristics of chaotic systems, which are defined by various nonlinear deterministic dynamic equations. Merge LED algorithm and the advantages of chaotic maps randomness provide successful confusion and diffusion property to improve the LED algorithm's shortcomings by increasing its security. This paper presents a lightweight approach to construct a robust, sufficiently using 3-D Lorenz system chaotic map to generate a one-time pseudo-random bit key to avoid being predicted by adversaries, resulting in achieving sound confusion and diffusion and withstand known assaults. A NIST test suite found that the performance of the LED based on the 3-D Lorenz chaotic map approach in terms of data secrecy is nearly 0.3003 higher than that of the LED and keeps the trade-off between computation cost and security .
Abstract: the MIDOG Challenge 2021

The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor malignancy and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MItosis DOmain Generalization (MIDOG) 2021 challengewas the creation of scanner-agnosticMFdetection algorithms. It was the first challenge to be held on the topic of histology domain generalization. The challenge used a training set of 200 cases, split across four scanning systems. The test set comprised an additional 100 cases split across four scanning systems, including two previously unseen scanners. We evaluated and compared the approaches that were submitted to the challenge and identified methodological factors contributing to better performance. The winning algorithm yielded an F1 score of 0.748 (CI95: 0.704-0.781). Additionally, we compared the performance of the algorithms to six pathologists. Irrespective of whether the original challenge ground or a newly generated object-level consensus of five experts was used as the basis for evaluation, we found that the best algorithms achieved higher performance levels than all experts. Further, the best algorithm reached the highest F1 values on either definition of the ground truth, reaching values of 0.742 and 0.748 on the five-expert consensus and the original challenge ground truth, respectively [1].
Real Time Detection and Tracking in Multi Speakers Video Conferencing

Currently, the videoconferencing market is growing worldwide with annual growth (CAGR) up to 10%. Several companies appreciated this technique during the Coronavirus lockdown as it allowed them to maintain continuous activity and frequent remote meetings. Despite the diversity of videoconference platforms, several are still needed especially if the conference participant moves out of the video capture window or if there is more than one person in the window. In this paper, a new videoconferencing system capable of detecting, choosing and tracking one participant is proposed. The framework suggested uses deep learning algorithms and offers the detection and tracking of a chosen person in a video stream. Person detection uses the Convolutional Neural Network model trained on a selected dataset. The tracking uses the SiamFC algorithm. In the model test phase, our system achieved an accuracy of 98%.
Intra-hour photovoltaic forecasting through a time-varying Markov switching model

This work presents a Markov switching model with a time-varying transition matrix to forecast intra-hour photovoltaic (PV) power output, aiming at providing forecasting flexibility. First, the proposed methodology captures images of the sky employing a self-made, low-cost all-sky imager. Second, the goal is to limit exposure problems in those images via the exposure fusion technique. Third, the proposed algorithm identifies groups of pixels forming clouds through a super paramagnetic clustering algorithm. Finally, we model the problem with a homogeneous Poisson process and forecast the cloud location and the shadowed area on a PV plant for the coming minutes. The shadowed area together with meteorological data are the inputs to this model. In the case study, our approach shows better performance than the persistence method, in particular for changing cloud conditions.
Information encoding in the spatial correlations of entangled twin beams

The ability to use the temporal and spatial degrees of freedom of quantum states of light to encode and transmit information is crucial for a robust and efficient quantum network. In particular, the potential offered by the large dimensionality of the spatial degree of freedom remains unfulfilled, as the necessary level of control required to encode information remains elusive. We encode information in the distribution of the spatial correlations of entangled twin beams by taking advantage of their dependence on the angular spectrum of the pump needed for four-wave mixing. We show that the encoded information can only be extracted through joint spatial measurements of the twin beams and not through individual beam measurements and that the temporal quantum correlations are not modified. The ability to engineer the spatial properties of twin beams will enable high-capacity quantum networks and quantum-enhanced spatially resolved sensing and imaging.
Big data and sustainable supply chain management of hypermarkets in Jordan: An experimental study using structural equation modeling approach

The objective of the study is to identify the impact of big data on sustainable supply chain management. The current research was conducted on hypermarkets in Jordan. Many of these hypermarket brands are widely scattered in Jordan, for instance, Carrefour, Kareem, Safeway and more. Accordingly, the target population in the current research was hypermarkets managers in Jordan as they are responsible for formulating such strategies in the companies they work for. A convenience sample was selected from the target population that included 770 managers based on the sample size formula. The study hypotheses were tested by covariance based structural equation modeling (CB-SEM). The study results showed the impact of each big data dimension on sustainable supply chain management. Based on this result, the researchers recommend the hypermarkets in Jordan to use modern and diverse methods for accurate collection of reliable data and save it in organized ways, and to employ advanced programs to analyze it and extract information of high value.
Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance.
Students’ Perceived M-Learning Quality: An Evaluation and Directions to Improve the Quality for H-Learning

The COVID-19 pandemic has transformed the paradigm of the higher education sector and has instigated a speedy consumption of a diverse range of mobile learning software systems. Many universities were adhering to online modes of education during the pandemic; however, some of the universities are now following hybrid modes of learning, termed h-learning. Higher education students spent two years of taking their classes online during the COVID-19 pandemic and have experienced various challenges. Simultaneously, the main challenge for higher education institutions remains how to consistently offer the best quality of students’ perceived m-learning and maintain continuance for the new shift towards hybrid learning. Hence, it becomes essential to determine the m-learning quality factors that would contribute to maintaining superior m-learning quality in higher education during the COVID-19 pandemic and afterwards via a hybrid mode of learning. Thus, the m-learning quality (MLQual) framework was conceptualized through an extensive review of the literature, and by employing survey-based quantitative research methods, MLQual was validated via structural equation modeling (SEM) techniques. The outcome of this research yielded the MLQual framework used to evaluate the students’ perceived m-learning quality and will offer higher education practitioners the chance to upgrade their higher education policies for h-learning accordingly. With the preceding discussion, it is evident that evaluation of the students’ perceived m-learning quality factors in higher education is always a question that should be researched adequately. Determination of such m-learning quality factors is essential in order to offer significant directions to the higher education practitioners for improving both the quality and delivery of m-learning and h-learning. Consequently, the present study embraces two key objectives: First, to identify and evaluate the m-learning quality factors which could be employed to improve the quality of m-learning. Second, to propose the MLQual framework for the evaluation of students’ perceived m-learning quality.
13 Integration of virtual reality in the e-learning environment

In our ever-changing world, learning is an essential part of everyone’s daily routine. The traditional approach to education relies on students applying what they have learned from textbooks and professors in the classroom to real-world circumstances. The use of cutting-edge technology in teaching and learning techniques is critical in today’s digital world. Information and communication technology (ICT) is a major focus for universities. These are important scientific instruments with the potential to have an impact on how people learn and teach science. Three-dimensional (3D) virtual reality (VR) interfaces give an experience via e-learning activities, software games, and simulated labs. Using VR, users may see, modify, and interact with computer systems and massive amounts of data. The term “visualization” refers to a computer’s ability to provide the user with sensory inputs such as visual, aural, or any other combination of these. E-learning may benefit from the realistic virtual environments that VR and web technologies can create. 3D graphics have been made possible by the development of web technologies such as HTML, JAVA, and the Virtual Reality Modeling Language. Issues linked to employing a VR application as a medium for students to study and gain practical knowledge about a place shown in a virtual environment are discussed in this chapter.
Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture

Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.
Automated Detection of Under-Constrained Circuits in Zero-Knowledge Proofs

As zero-knowledge proofs gain increasing adoption, the cryptography community has designed domain-specific languages (DSLs) that facilitate the construction of zero-knowledge proofs (ZKPs). Many of these DSLs, such as Circom, facilitate the construction of arithmetic circuits, which are essentially polynomial equations over a finite field. In particular, given a program in a zero-knowledge proof DSL, the compiler automatically produces the corresponding arithmetic circuit. However, a common and serious problem is that the generated circuit may be underconstrained, either due to a bug in the program or a bug in the compiler itself. Underconstrained circuits admit multiple witnesses for a given input, so a malicious party can generate bogus witnesses, thereby causing the verifier to accept a proof that it should not. Because of the increasing prevalence of such arithmetic circuits in blockchain applications, several million dollars worth of cryptocurrency have been stolen due to underconstrained arithmetic circuits. Motivated by this problem, we propose a new technique for finding ZKP bugs caused by underconstrained polynomial equations over finite fields. Our method performs semantic reasoning over the finite field equations generated by the compiler to prove whether or not each signal is uniquely determined by the input. Our proposed approach combines SMT solving with lightweight uniqueness inference to effectively reason about underconstrained circuits. We have implemented our proposed approach in a tool called QED 2 and evaluate it on 163 Circom circuits. Our evaluation shows that QED 2 can successfully solve 70% of these benchmarks, meaning that it either verifies the uniqueness of the output signals or finds a pair of witnesses that demonstrate non-uniqueness of the circuit. Furthermore, QED 2 has found 8 previously unknown vulnerabilities in widely-used circuits.
A Cross-Domain Aspect-Based Sentiment Classification by Masking the Domain-Specific Words

The Aspect-Based Sentiment Classification (ABSC) models often suffer from a lack of training data in some domains. To exploit the abundant data from another domain, this work extends the original state-of-the-art LCR-Rot-hop++ model that uses a neural network with a rotatory attention mechanism for a cross-domain setting. More specifically, we propose a Domain-Independent Word Selector (DIWS) model that is used in combination with the LCR-Rot-hop++ model (DIWS-LCR-Rot-hop++). It uses attention weights from the domain classification task to determine whether a word is domain-specific or domain-independent, and discards domain-specific words when training and testing the LCR-Rot-hop++ model for cross-domain ABSC. Overall, our results confirm that DIWS-LCR-Rot-hop++ outperforms the original LCR-Rot-hop++ model under a cross-domain setting in case we impose a domain-dependent threshold value for deciding whether a word is domain-specific or not. For a target domain that is highly similar to the source domain, we find that a moderate attention threshold yields the best performance, while a target domain that is dissimilar requires a high attention threshold. Also, we observe information loss when we impose a too strict restriction and classify a small proportion of words as domain-independent.
Which Challenges Do Exist With Agile Culture in Practice?

Context: In agile transformations there are many challenges. One very important but less considered and treated in research are cultural challenges associated with an agile mindset. Although research shows that cultural clashes and general organizational resistance to change are part of the most significant agile adoption barriers. Objective: We identify challenges that arise from the interplay between agile culture and organizational culture. In doing so, we tackle this field and come up with important contributions for further research regarding a problem that practitioners face today. Method: This is done with a mixed-method research approach. First, we gathered qualitative data among our network of agile practitioners and derived in sum 15 challenges with agile culture. Then, we conducted quantitative data by means of a questionnaire study with 92 participants. Results: We identified 7 key challenges out of the 15 challenges with agile culture. These key challenges refer to the technical agility (doing agile) and the cultural agility (being agile). The results are presented in a conceptual model. Conclusion: Based on our results, we started deriving future work aspects to do more detailed research on the topic of cultural challenges while transitioning or using agile methods in software development and beyond.
A Blockchain-Based Method for Optimizing the Routing of High-Frequency Carbon-Trading Payment Channels

Carbon trading is an effective way to achieve carbon neutrality. It is a market mechanism aimed at reducing global greenhouse gas emissions and carbon dioxide emissions. Blockchain technology can be applied to the carbon-trading scenario using characteristics that guarantee the security, decentralization, data immutability, and data traceability of the carbon-trading process. It would be difficult to implement carbon trading on blockchains for all enterprises and individuals, as the current performance of blockchains does not meet the requirements. There has been extensive research conducted on blockchain performance optimization, and the off-chain payment channel is one of the more mature solutions. This approach involves the transfer of transactions to off-chain transactions, thus avoiding high transaction fees. Existing research has addressed the problem of routing security and efficiency, with less emphasis on factors such as routing transaction costs, node reputation, and routing path considerations. This paper researches the optimization of payment routing in Payment Channel Networks (PCNs) and proposes the Multi-Factor Routing Payment Scheme (MFPS), which integrates factors such as the node reputation, transaction fee cost, and distance to select the route for payment transactions. In order to improve the success ratio of routing transactions, the transaction-splitting algorithm is proposed. To ensure the security and privacy of the transaction process, the Asymmetric Time-Lock Contract (ATLC) protocol is proposed. The results of extensive experimental simulations show that the MFPS proposed in this paper outperforms the ShortestPath, Cheapest, and SplitDistance algorithms. It achieves an approximately 13.8%∼49% improvement in the transaction success ratio and reduces the average transaction processing cost. The security and privacy measures can defend against wormhole and double-flower attacks and exhibit better performance in terms of computational verification and message overhead.