Reviewerly/exHarmony huggingface dataset:
Applicability of Blockchain-Based Implementation for Risk Management in Healthcare Projects

Hospitals provide diverse tasks essential for the delivery of patient care and are comprised of many functional units. This makes healthcare construction projects highly complex among other types of building projects due to the specific regulations, multiple functions it must provide, complicated mechanical and electrical systems, and so on. This complexity embodies potential risk events during its construction, which not only influences the completion of the project but can impact the patients’ safety and health conditions even after the project is finished. To prevent such outcomes, risk management is a crucial process that can identify, evaluate, and properly mitigate risks along the project lifecycle. A key aspect of risk management is that it requires the interaction and contribution from multiple stakeholders of the project. Various frameworks and tools that enable collaborative management of risks among multiple stakeholders have been developed in the past. However, the developed frameworks are not suitable in the sense that it does not protect the confidentiality of individual inputs from the stakeholders. Moreover, these frameworks are centralized systems, which can bring issues related to the security and transparency of the information that is being stored. Blockchain technology is an emergent distributed ledger technology (DLT) that can provide solutions to the listed problems found in centralized systems. It is a novel system that records information on a decentralized, distributed ledger, where transactions are constantly duplicated and updated. This study explores the applicability of blockchain technology for healthcare risk management. The key functional elements of blockchain that can resolve the challenges faced by prior risk management frameworks have been identified and discussed. Based on the discussions, a conceptual information management model for managing healthcare project risks on a blockchain has been conceived. The development of the initial prototype has been explained. The research illustrates the process, benefits, and limitations of adopting blockchain technology for collaborative risk management in healthcare projects.&#x0D;
The Performance of Thirty-Eight Evapotraspiation Methods against the Penman Montieth Method

Evapotranspiration is an important parameter to know in the management of agricultural water resources. There are several methods for estimating evapotranspiration (ETo). The FAO Penman-Monteith method is considered the universal standard for estimating ETo, however, the climate parameter data needed to calculate the ETo of the FAO Penman-Monteith method are not always available, so it is necessary to evaluate performance using several ETo equations as a comparison in estimating evapotranspiration. This study aims to find the performance of the ETo 38 empirical method against the FAO Penman Monteith method and the climatological data parameters that most influence the accuracy of the calculations. The data used is in the form of climatological data for Bora Station which represents land and settlement areas, Boladangko Station representing mountainous areas and Singkoyo Station representing coastal areas with an observation period of 32 years (1986-2017). The performance of the 38 empirical methods was evaluated using the PE, RMSE, MAE, MR and slope methods. The results of this study indicate that: the Makkink method, Abtew1, Irmak et al., Tabari &amp; Talaee1, Valiantzas1 and Abtew3 showed excellent performance, while the WMO and Mahringer methods performed poorly in the three research locations; the number of climatological parameters used had no significant effect on the ETo results obtained. The use of climatological parameters in the form of temperature and solar radiation in the ETo calculation has the greatest effect on the accuracy of the ETo value.
Visual Emotion Analysis via Affective Semantic Concept Discovery

With the development of social media, people prefer to express views and share daily life online via visual content, which has led to widespread attention in automatic emotion analysis from images. Capturing the emotions embedded in these social images has always been important yet challenging. In this paper, we propose a visual emotion prediction method that utilizes the affective semantic concepts of an image to predict its emotion. To solve the problems of narrow semantic coverage and low discriminative power of emotions in current semantic concept sets used for visual emotion analysis, we develop a concept selection model to mine emotion-related concepts from social media. Specifically, we propose several selection strategies to build an affective semantic concept set that contains various visual concepts related to emotion conveyance. And they are discovered from affective image datasets and associated tags crawled from websites. To further leverage the discovered affective semantic concepts, we train concept classifiers to predict the concept score of each concept, which are used as the intermediate features to tackle the semantic gap problem for image emotion recognition. Extensive experimental results confirm the validity of the affective semantic concepts and show the improved performance of our method.
Receding Horizon Control with Extended Solution for UAV Path Planning

The receding horizon control (RHC) greatly reduces the planning time and achieves great success in UAV online path planning because of rolling window optimization. However, due to its small range of path search in the time window, UAVs cannot cope with environments with uncertain obstacles and multiple flight constraints. Therefore, the receding horizon control with extended solution (RHC-eS) method is proposed for UAV path planning based on the traditional RHC. This method first designs the path expansion mechanism, which not only expands the search range of feasible solutions but also ensures the real-time performance by the two-way search strategy. Secondly, in order to increase the richness of solutions, the crossover and directional variation strategy of Genetic Algorithm (GA) are integrated. Finally, the Sequential Quadratic Programming (SQP) method is used to optimize the objective function. The simulation results of UAV path planning in simple and complex environments certify that the proposed method can obtain shorter, safer, and smoother paths compared with the existing methods.
Status and Implementation of the AdriaArray Seismic Network

&amp;lt;p&amp;gt;With the advent of plate tectonics in the last century, our understanding of the geological evolution of the Earth system improved essentially. The internal deformation and evolution of tectonic plates remain however poorly understood. This holds in particular for the Central Mediterranean: The formerly much larger Adriatic plate is recently consumed in tectonically active belts spanning at its western margin from Sicily, over the Apennines to the Alps and at its eastern margin from the Hellenides, Dinarides towards the Alps. High seismicity along these belts indicates ongoing lithospheric deformation. It has been shown that data acquired by dense, regional networks like AlpArray provide crucial information on seismically active faults as well as on the structure and deformation of the lithosphere. The Adriatic Plate and in particular its eastern margin have however not been covered by a homogeneous seismic network yet.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here we report on the status and preparation of AdriaArray &amp;amp;#8211; a seismic experiment to cover the Adriatic Plate and its actively deforming margins by a dense broad-band seismic network. Within the AdriaArray region, currently about 950 permanent broad-band stations are operated by more than 40 institutions. Data of 90% of these stations are currently available via EIDA. In addition to the existing stations, 385 temporary stations from 18 mobile pools are to be deployed in the region to achieve a coverage with an average station distance of about 50 &amp;amp;#8211; 55&amp;amp;#160;km. The experiment will be based on intense cooperation between network operators, ORFEUS, and interested research groups. Altogether, more than 50 institutions will participate in the AdriaArray experiment. We will introduce the time schedule, participating institutions, mobile station pools, maps of suggested temporary station distribution with station coverage and main points of the agreed Memorandum of Collaboration. The AdriaArray experiment will lead to a significant improvement of our understanding of the geodynamic causes of plate deformation and associated geohazards.&amp;lt;/p&amp;gt;
A Common Theme in the Works of the Justice Squadron and the Jadid Poets

The first half of the twentieth century was a time of great trials for all Eastern countries, especially for the Korean Peninsula, which stood on the verge of a “new world”, following the news of a new era, the creation of new schools. By this time, Westernization reforms under the influence of neighboring Japan, which attracted the attention of powerful states, were gaining momentum. As a result of Japan’s efforts to westernize and reform the “Year of the Kabo”, a new culture, politics, literature, Japanese customs and traditions have entered the country. It is known from history that during the colonization of any state, first of all, a serious blow is inflicted on its culture, language, beliefs, national pride. Using such an effective method, the Japanese government pursued a colonial policy aimed at destroying the national spirit and culture of the Korean people.
C4.5 Algorithm Classification for Determining Smart Indonesia Program Recipients at MIS Al-Khoirot

The purpose of the research is to assist the school in selecting student data as recipients of the PIP (Smart Indonesia Program) to be more objective and practical and to assist in increasing the accuracy of the targeting of the recipients of the PIP funds. In this study using Data Mining techniques using the C4.5 algorithm. The source of the research data used was obtained from observations and interviews with the MIS Al-Khoirot Tambun Nabolon Pematang Siantar school. The research variables used were parents' occupations, parents' income, KKS (Prosperous Family Card) holders, SKTM holders (Poor Certificate). In this study, the alternative used as a sample is the data of MIS Al-Khoirot students. The results of this study found that the most dominant attribute was the SKTM holder with a gain of 0.833764907, besides that this study produced 8 (eight) rules with an accuracy rate of 98.00%. Based on this, it can be concluded that the C4.5 algorithm can be used for the classification of the Determination of Smart Indonesia Program Recipients at MIS Al-Khoirot
Optimal Design of Intelligent Control System in the Communication Room Based on Artificial Intelligence

With the current data-driven era, there is the potential to employ controllers that can store a large amount of data, which is not achievable with traditional controllers. Our goal is to propose an intelligent controller system for computer room management based on artificial intelligence that maintains data integrity, saves memory, minimizes computation, and simplifies program design. To upgrade the computer room management system’s intelligence that is not high, the management mode that is not flexible, and the distributed large-scale management of the whole school that is difficult to realize, the original system is improved to the distributed computer room management system based on artificial intelligence. By starting from the actual situation of higher vocational college computer room, combined with the characteristics of the school computer room, we designed framework model based on distributed artificial intelligence machine room management system, the system by means of network communication technology and database access technology, put forward the B/S combined with C/S structure to realize the computer room management system model, and used radio frequency identification technology to develop radio frequency card. The results show that the optimization results of the traditional computer automatic control system in the computer room vary greatly and fluctuate between 0.6 and 0.8, while the control results of the automatic control system in this paper keep stable at 0.8, which can reach the ideal state in a short time. Through the outcome, it can be said that the proposed control method can be used of higher level of automation, flexibility, and robustness which will work effectively. Therefore, the improved system integrates software, hardware, communication, and distributed system technology into one, which greatly improves the control effect of computer automatic control process, and control result of computer automatic control process is more stable and has a certain practical application value.
SFO for enhancing steganogrphy by exploiting exact resemblance between cover and secure text

Steganography has become an important science in security in recent years, it hides the existence of the secret information in such a way that no one suspects the information exists. In this paper a new steganography method for concealing message in message by using sailfish optimizer (SFO) and transposition method is proposed. In this work, the (SFO) is utilized to enhance the security and reduce the disfigurement to obtain best message quality, where the SFO has advantages in the term of security and text quality. The fitness values of the cover and secret message are calculated by using a new way. The SFO works to determining the matching fitness value between the cover and secret message and generates a key to recapture secret message. Lastly, relying on the matching fitness value, the steganography process is done by replacing (transposition) the secret's letters with cover's letters. The results proved that proposed method is effective, efficient and provided high security, high capacity, not malformed text and resistance against several steganalytic attacks
Fine-Grained Feature Classification of Objects by Learning-Based Feature Selection Using Region-Proposal Convolutional Neural Network (RCNN)

Fine-grained classification represents localizing and aggregating distinct points between similar regions based on correlation among these known regions. Hence, finding accuracy in this localization approach of such discriminative regions in multi-domain scenario remains a major challenge. Here an improved framework through patch triplet has been proposed to designate this problem of localization in an image. A triplet of similar feature points with logical constraints has been applied to improve the accuracy of region localization, and automatically extract distinct features as geometrically-constrained triplets for classification. The resulting approach only presents object bounding and detecting boxes as in Region-Proposal Convolutional Neural Network (RCNN) approach. Its efficiency is proved using publicly available fine-grained datasets, where it outperforms or obtains comparable results. Several simple real-world objects in a home have been taken as input images. In our trial, the Scale Invariant Feature Transform (SIFT)-based key points have been taken and prepared utilizing channel and convolution operation. The model is retrained to recognize four different types of metal objects, with the entire process requiring four hours to explain and prepare each strong piece. A major benefit of using fine-grained based Faster-RCNN approach is better performance for object identification with SIFT key points.
KASLR in the age of MicroVMs

Address space layout randomization (ASLR) is a widely used component of computer security aimed at preventing code reuse and/or data-only attacks. Modern kernels utilize kernel ASLR (KASLR) and finer-grained forms, such as functional granular KASLR (FGKASLR), but do so as part of an inefficient bootstrapping process we call bootstrap self-randomization. Meanwhile, under increasing pressure to optimize their boot times, microVM architectures such as AWS Firecracker have resorted to eliminating bootstrapping steps, particularly decompression and relocation from the guest kernel boot process, leaving them without KASLR. In this paper, we present in-monitor KASLR, in which the virtual machine monitor efficiently implements KASLR for the guest kernel by skipping the expensive kernel self-relocation steps. We prototype in-monitor KASLR and FGKASLR in the open-source Firecracker virtual machine monitor demonstrating, on a microVM configured kernel, boot times 22% and 16% faster than bootstrapped KASLR and FGKASLR methods, respectively. We also show the low overhead of in-monitor KASLR, with only 4% (2 ms) increase in boot times on average compared to a kernel without KASLR. We also discuss the implications and future opportunities for in-monitor approaches.
Contextualized Design of IoT (Internet of Things) Finance for Edge Artificial Intelligence Computing

With the widespread application of IoT technology in the world, the new industry of IoT finance has emerged. Under this new business model, commercial banks and other financial institutions can realize safer and more convenient financial services such as payment, financing and asset management through the application of IoT technology and communication network technology. In the cloud computing model, the local terminal device of IOT will transmit the collected data to the cloud server through the network, and the cloud server will complete the data operation. Cloud computing model can well solve the problem of poor performance of IoT devices, but with the increasing number of IoT terminal devices and huge number of devices accessing the network, cloud computing model is constrained by network bandwidth and performance bottleneck, which brings a series of problems such as high latency, poor real-time and low security. In this paper, based on the new industry of IoT finance which is developing rapidly, we construct a POT (Peaks Over Threshold) over threshold model to empirically analyze the operational risk of commercial banks by using the risk loss data of commercial banks, and estimate the corresponding ES values by using the control variables method to measure the operational risk of traditional commercial banks and IoT finance respectively, and compare the total ES values of the two. This paper adopts the control variable method to reduce the frequency of each type of loss events of operational risk of commercial banks in China respectively.
Object Categorization Capability of Psychological Potential Field in Perceptual Assessment Using Line-Drawing Images

Affective/cognitive engineering investigations typically require the quantitative assessment of object perception. Recent research has suggested that certain perceptions of object categorization can be derived from human eye fixation and that color images and line drawings induce similar neural activities. Line drawings contain less information than color images; therefore, line drawings are expected to simplify the investigations of object perception. The psychological potential field (PPF), which is a psychological feature, is an image feature of line drawings. On the basis of the PPF, the possibility that the general human perception of object categorization can be assessed from the similarity to fixation maps (FMs) generated from human eye fixations has been reported. However, this may be due to chance because image features other than the PPF have not been compared with FMs. This study examines the potential and effectiveness of the PPF by comparing its performance with that of other image features in terms of the similarity to FMs. The results show that the PPF shows the ideal performance for assessing the perception of object categorization. In particular, the PPF effectively distinguishes between animal and nonanimal targets; however, real-time assessment is difficult.
Fed_ADBN: An efficient intrusion detection framework based on client selection in <scp>AMI</scp> network

Abstract Data transmission between smart meters and data center is facing network security threats in advanced metering infrastructure of smart grid. The traditional solution is to move the data to the data center to build a centralized attack detection model, or divide the collected data into several independent and identically distributed datasets to build a distributed attack detection model. However, the long‐distance transmission and the centralized storage of data not only increase the communication overhead and time overhead, but also increase the risk of being attacked, causing privacy disclosure during the process of building the model. In this paper, we propose an efficient intrusion detection framework Fed_ADBN based on federated attention deep belief network and client selection. Clients cooperate with the data center to jointly build a horizontal federated learning framework. Under the premise of protecting data security by keeping data on the clients, we design a client selection algorithm based on client computing power, communication quality and security risks, which can improve the operating efficiency of federated learning. We also deploy a deep belief neural network with attention mechanism in each client to accurately detect possible network attacks in AMI network in real time. Experimental results show that compared with state‐of‐the‐art methods, the proposed framework can not only maintain good detection accuracy but also protect privacy.
Optimized Quantum Circuits in Quantum Image Processing Using Qiskit

Quantum image representation is an essential component of quantum image processing and plays a critical role in quantum information processing. Flexible Representation of Quantum Images (FRQI) presents pixel colors and associated locations as a state to represent images on quantum computers. A fundamental part of the quantum image processing system is quantum image compression (QIC), which is utilized to maintain and retrieve binary images. This compression allows us to minimize the number of controlled rotation gates in the quantum circuits. This paper designed optimized quantum circuits and simulated them using Qiskit on a real quantum computer based on minimum boolean expressions to retrieve the 8×4 binary single-digit images. To demonstrate the feasibility and efficacy of quantum image representation, quantum circuits for images were developed using FRQI, and quantum image representation experiments were done on IBM Quantum Experience (IBMQ). We were able to visualize quantum information by doing the quantum measurement on the image information that we had prepared. Without utilizing this method, the number of controlled rotation gates is equal to the number of pixels in the image; however, we showed that by using the QIC algorithm, we could decrease the number of gates significantly. On these images, the maximum and minimum compression ratios of QIC are 90.63% and 68.75%, respectively.
A Quantitative Study on Dream of the Red Chamber: Word-Length Distribution and Authorship Attribution

This paper investigates the distribution characteristics of word lengths in the Dream of the Red Chamber (DRC), measured in terms of the number of syllables or characters. The results show that the frequency distribution of words of different lengths in the DRC abides by the extended logarithmic distribution model. A comparison between the first forty, the middle forty, and the last forty chapters shows that the distribution of word lengths in these three parts does not differ significantly, which sheds light on the authorship of the novel.