Reviewerly/exHarmony huggingface dataset:
Refined Management of Traffic Engineering Cost Based on BIM Technology

Abstract The rapid development of technology and economy has brought the construction industry into a stage of vigorous development. The traditional mode of project cost management is no longer suitable for the development of the construction industry, and more refined management of cost is imperative. At this stage, BIM technology is widely used in the management of cost. It can establish corresponding models to carry out fine management of each stage of construction. Based on the above background, the purpose of this paper is to refine the management of traffic engineering cost based on BIM technology. This article first conducts a survey of the current status of traffic engineering cost management based on BIM technology. Through statistical analysis of the survey data, it is mainly aimed at the investigation of the current BIM understanding and application of the traffic engineering cost industry. Then put forward the basic principles of BIM technology application engineering cost management, and then put forward the optimization plan of traffic engineering cost management based on BIM technology, including investment decision, design, budget, bidding, construction stage, etc. Finally, the study found that in the process of project construction, the application of BIM technology greatly facilitated the construction of various specialties, and the project quality rate reached more than 98%, and significant results were achieved.
A Distributed Ledger Management Mechanism for Storing and Selling Private Data

Storing and processing huge amount of private data is a challenging problem. The problem becomes, in particular, interesting if the user-side storage and computational power is limited. One way to solve the problem of outsourcing private data and maintaining an access control on the storage is proposed by using blockchain technology. However, blockchain technology requires heavy computational power and machinery. In this paper, we propose an approach to store and sell private data with the help of secret sharing. In comparison to blockchain, our methodology is simpler and preserves the privacy of stored data.
Impact of Node Churn in the Bitcoin Network with Compact Blocks

Compact block protocol aims to reduce bandwidth usage and, possibly, latency in the Bitcoin network. In this work, we present an analytical model to evaluate the impact of churning nodes on the Bitcoin network when the compact block protocol is used. We use a Continuous Time Markov Chain to model wake-up and sleeping behavior of each node in the network. We also calculate synchronization time, including transaction deficit recovery, when a node rejoins the network. When a node is absent from the network, it misses blocks mined during its absence but also some of the transactions which may be omitted from the subsequent compact blocks. Our results indicate that synchronization time takes more than a minute when node is away from the network for several hours. Moreover, the majority of synchronization time is spent on block verification and transaction deficit recovery.
Si Microring Resonator Switch Based on III-V/Si Hybrid MOS Optical Phase Shifter Using Ultrathin InP Membrane

We demonstrate ultralow-power Si microring resonator switch based on taperless III-V/Si hybrid MOS optical phase shifter using ultrathin InP membrane. Owing to electron accumulation at the InP MOS interface, we successfully achieved optical switching with 0.3 pW, enabling large-scale integration without thermal crosstalk.
An algorithm for the microaggregation problem using column generation

The field of statistical disclosure control aims to reduce the risk of re-identifying an individual from disseminated data, a major concern among national statistical agencies. Operations Research (OR) techniques have been widely used in the past for protecting tabular data, but not microdata (i.e., files of individuals and attributes). Few papers apply OR techniques to the microaggregation problem, which is considered one of the best methods for microdata protection and is known to be NP-hard. The new heuristic approach is based on a column generation scheme and, unlike previous (primal) heuristics for microaggregation, it also provides a lower bound on the optimal microaggregation. Using real data that is typically used in the literature, our computational results show, first, that solutions with small gaps are often achieved and, second, that dramatic improvements are obtained relative to the literature's most popular heuristics.
Toward a Context-Aware Methodology for Information Security Governance Assessment Validation

Conducting a cybersecurity assessment is a central activity in protecting a generic organization from cyber-attacks. Several methods exist in research and industry to assess the security level of an organization, from manual activities to automated attack graphs. Unfortunately, automated approaches fail in taking into account the governance aspect that still need to be evaluated manually by the assessor, introducing possible biases or problems deriving from the level of expertise. In this paper, we provide a methodology to support the assessor in the task of evaluating the coverage of cybersecurity controls coming from technical standards, regulations, internal practices. This is done by providing him/her with a multi-layer model that takes into account several organizational layers, a mapping procedure to tie the security controls to the multi-layer model, and the definition of a validation factor that can be used to possibly refine the level of coverage and to suggest possible layers where evidences should be collected to verify and assess the coverage of a security control. A usage scenario provides an initial validation of our approach based on ISO 27001. Developments of this methodology are on-going toward its application to the support of broader cyber-risk assessment activities through discounting risk factors.
Aspect-Based Sentiment Analysis of Individual Equipment Ergonomics Evaluation

The man-machine adaptability between soldiers and equipment affect the operational effectiveness of the whole individual soldier integrated system. In view of the complex semantic information and various equipment tendencies of individual soldier equipment evaluation, in order to extract key insights from soldiers' comments on equipment, this paper constructs a Chinese multi-faceted and multi emotional (MME) dataset, and proposes an aspect-based sentiment analysis model. This model combines attention mechanism and context feature dynamic weighting method with global perceptual aspect embedding to enhance the BERT representation of aspect-based sentiment analysis. Then this study adds the offset correction to the ADAM algorithm, and finally maps the target to the correct context representation. The experimental results show that BERT's dynamic weighting model has higher accuracy and score than MGAN, ATAE-LSTM, BERT-AEN, and BERT-Base models respectively in the classification of soldier equipment evaluation. It proves the effectiveness and feasibility of the data processing of soldier equipment evaluation.
Second-Order Convolutional Neural Network Based on Cholesky Compression Strategy

In the past few years, Convolution Neural Network (CNN) has been successfully applied to many computer vision tasks. Most of these networks can only extract first-order information from input images. The second-order statistical information refers to the second-order correlation obtained by calculating the covariance matrix, the fisher information matrix, or the vector outer product operation on the local feature group according to the channels. It has been shown that using second-order information on facial expression datasets can better capture the distortion of facial area features, while at the same time generate more parameters which may cause much more computational cost. In this article we propose a new CNN structure including layers which can (i) incorporate first-order information into the covariance matrix; (ii) use eigenvalue vectors to measure the importance of feature channels; (iii) reduce the bilinear dimensionality of the parameter matrix; and (iv) perform Cholesky decomposition on the positive definite matrix to complete the compression of the second-order information matrix. Due to the incorporation of both first-order and second-order information and the Cholesky compression strategy, our proposed method reduces the number of parameters by half of the SPDNet model, and simultaneously achieves better results in facial expression classification tasks than the corresponding first-order model and the reference second-order model.
Visible Reversible Watermarking for 3D Models Based on Mesh Subdivision

Visible reversible watermark can clearly identify the copyright of digital works, and can completely remove the watermark when it is needed. It can be utilized for visibly labeling the copyright of digital host. To protect the copyright of 3D mesh model, a visible reversible watermarking based on mesh subdivision is proposed in this paper. First, the smooth area of the 3D mesh model is projected onto the 2D plane for cropping and subdivision to achieve the embedding of the visible watermark. Then, by comparing the vertex information of the original model and the model with visible watermark, the index of the visible watermark vertex is obtained, and it is embedded as a reversible watermark to the rest of the model. The visible watermark can be removed by deleting the vertices corresponding to the reversible watermark. Experimental results and analysis show that the visible watermark and the host are tightly integrated, and it is robust to translation, rotation, scaling, mesh subdivision, and mesh smoothing. When unauthorized users try to delete the watermark or steal the 3D mesh model, the host will be destroyed and the model will be unusable. It has potential application in the copyright protection of the 3D mesh model.
Numerical Prediction Of Foils Configuration In A Design of Buoy Glider System For Supporting Tsunami Early Warning

Abstract Geographically, Indonesia is a disaster-prone country, with the potential to cause damage to public infrastructure not only cause enormous loss of life. Based on the tsunami risk map released by BNPB in 2018, it needs more attention so that mitigation can be implemented properly. It necessary to revitalize nationally developed by BPPT the buoy-based early warning system for tsunami disasters. Early generation tsunami buoy designs were successfully developed and operated in several waters in Indonesia. In order to improve the performance of the tsunami buoy, a new vehicle system known as the Wave Buoy Glider (WBG) is proposed. It is hoped that this WBG design can cover a wider monitoring area as well as more wave elevation measurement data related to tsunami events. In this research some of the buoy glider-design parameters such as sizes and configurations of foil are studied in order to obtain the optimal pulling force of the wave glider to the surface floater. A numerical calculation using the CFD technique is used to determine the hydrodynamic force. So that from this study, it can be seen that an increasing number used foils, the drag and lift forces also tend to increase. The larger the span foil, the greater the value of the drag and lift forces, especially at the larger AoA angle. The positive values of drag forces at any lift forces confirm that wave glider always move forward with every vertical movement of the floater.
Design Technology of Geographical Information System of Rice Pandanwangi Cianjur Prediction Factors Products

Abstract Cianjur is one of the renowned rice producers, particularly with the presence of Pandanwangi rice that can be grown in the Cianjur area. Pandanwangi has become one of Cianjur City’s icons, the rice supremacy created by Pandanwangi Cianjur’s tenacity and distinctive scent (pandanus fragrance). The Interest of pandanwangi was very high, both within the town of Cianjur and outside the town of Cianjur, but this was inversely proportional to the desires of the groups of farmers who appeared to prefer to plant rice other than Pandanwangi, on the grounds that the planting of Pandanwangi had a long harvest and that the likelihood of crop failure was also very high. Problems with information flows that are not well integrated between farmers’ groups, farmers’ group organizations, and the Agriculture Department in adjusting land details, planting time, planting location, estimating total harvest and harvesting time. To solve this problem, a framework must be built that can integrate the information needed for Pandanwangi Cianjur Harvest Prediction with Pandanwangi farmers’ regional analysis based on geographic information system technology, using Google Maps Service.
A novel method for contrast enhancement of gray scale image based on shadowed sets

Image processing is considered to be one of the basic components in intelligent and decision support systems. On the other hand, improving image contrast is one of the most challenging issues in the field of image processing, pattern recognition and machine vision. This process improves understanding the information in the image for the human viewer. This is especially of great importance for medical imaging because low light intensity and high noise levels make it difficult to accurately diagnose specific diseases as well as to document and analyze them. The main motivation in image enhancement is to modify the image properties so that it is suitable for viewing and application, which can be done by removing noise, sharpening the edge and improving contrast enhancement. Due to the importance of the subject, various methods have been proposed by researchers in this field. In this paper, a new method based on shadowed sets is presented to improve image contrast. The results show that this method has acceptable accuracy and efficiency compared to other image enhancement techniques.
Designing Trojan Detectors in Neural Networks Using Interactive Simulations

This paper addresses the problem of designing trojan detectors in neural networks (NNs) using interactive simulations. Trojans in NNs are defined as triggers in inputs that cause misclassification of such inputs into a class (or classes) unintended by the design of a NN-based model. The goal of our work is to understand encodings of a variety of trojan types in fully connected layers of neural networks. Our approach is: (1) to simulate nine types of trojan embeddings into dot patterns; (2) to devise measurements of NN states; and (3) to design trojan detectors in NN-based classification models. The interactive simulations are built on top of TensorFlow Playground with in-memory storage of data and NN coefficients. The simulations provide analytical, visualization, and output operations performed on training datasets and NN architectures. The measurements of a NN include: (a) model inefficiency using modified Kullback–Liebler (KL) divergence from uniformly distributed states; and (b) model sensitivity to variables related to data and NNs. Using the KL divergence measurements at each NN layer and per each predicted class label, a trojan detector is devised to discriminate NN models with or without trojans. To document robustness of such a trojan detector with respect to NN architectures, dataset perturbations, and trojan types, several properties of the KL divergence measurement are presented.
Deep Learning versus High-order Recurrent Neural Network based Decoding for Convolutional Codes

In the last decade, deep neural networks (DNNs) have shown impressive results in various fields such as image classification, speech recognition, or playing the abstract strategy board game Go. Recently, also an increased interest in the application of DNNs to physical layer problems in digital communications can be observed. We use a DNN for one-shot decoding of convolutional (self-orthogonal) codes. An advantage of this use case is the unlimited amount of labeled data for training. A disadvantage is, that the number of code words to be learned increases exponentially with the dimension of the code. We compare the performance of the DNN-based decoding with iterative threshold decoding (ITD). Here, a discrete-time high-order recurrent neural network (HORNN) is used as a computational model for ITD. Unfolding the HORNN in time, we arrive at a DNN with a special structure as defined by the HORNN. With a training procedure we can optimize the performance of this unfolded HORNN (uHORNN). The advantage of this approach is that the structure of the uHORNN is determined by the structure of iterative threshold decoding. Only a few weights, which are shared within and between the layers, must be adapted to optimize the network. In this way we combine the advantages of both approaches, the structured approach of the HORNN as a computational model, on the one hand, and the training based optimization as given by a DNN on the other hand.
Bayesian Sampler: Fairness in Sampling

We reinterpret the concept of Bayesian balance from Yang Liu [26] to find connection between fairness of algorithms and class imbalance in latent clusters of training data. We argue that the degree of class imbalance in the latent clusters of some training data is manifested in the lack of fairness of an algorithm trained on that data. A novel algorithm is proposed in this paper which decides an optimal policy to draw a balanced data sample from clustered raw data with class imbalance. The proposed Bayesian network model trades off accuracy with redefined Bayesian balance to find the optimal policy. We claim that a novel application of this sampling technique would be sampling a fair training set for recommender systems. To illustrate, we present experimental results on two real world recommender systems raw data sets and one synthetic data-set.
The 3D-DTW Custom IP based FPGA Hardware Acceleration for Action Recognition

This article proposes an implementation of an action recognition system, which allows the user to perform operations in real time. The Microsoft Kinect (RGB-D) sensor plays a central role in this system, which provides the skeletal joint information of humans directly. Computationally efficient skeletal joint position features are considered for describing each action. The dynamic time warping algorithm (DTW) is a widely used algorithm in many applications such as similarity sequence search, classification, and speech recognition. It provides the highest accuracy compared to all other algorithms. However, the computational time of the DTW algorithm is a major drawback in real world applications. To speed up the basic DTW algorithm, a novel three-dimensional dynamic time warping (3D-DTW) classification algorithm is proposed in this work. The proposed 3D-DTW algorithm is implemented in both software and field programmable gate array (FPGA) hardware modeling techniques. The performance of the 3D-DTW algorithm is evaluated for 12 actions in which each action is described with the feature vector size of 576 over 32 frames. From our software modeling results, it has been shown that the proposed algorithm performs the action classification accurately. However, the computation time of the 3D-DTW algorithm increases linearly when we increase either the number of actions or the feature vector size of each action. For further speedup, an efficient custom 3D-DTW intellectual property (IP) core is developed using the Xilinx Vivado high-level synthesis (HLS) tool to accelerate the 3D-DTW algorithm in FPGA hardware. The CPU centric software modeling of the 3D-DTW algorithm is compared with its hardware accelerated custom IP core. It has been shown that the developed 3D-DTW Custom IP core computation time is 40 times faster than its software counterpart. As the hardware results are promising, a parallel hardware software co-design architecture is proposed for the Xilinx Zynq-7020 System on Chip (SoC) FPGA for action recognition. The HLS simulation and synthesis results are provided to support the practical implementation of the proposed architecture. Our proposed approach outperforms many of the existing state-of-the-art DTW based action recognition techniques by providing the highest accuracy of 97.77%.
Complementary measurement-induced quantum uncertainty based on metric adjusted skew information

We investigate complementary measurement-induced quantum uncertainty based on metric adjusted skew information, and propose two new measures of quantum uncertainty associated with mutually unbiased measurements and general symmetric informationally complete measurements, respectively. Based on these measures of quantum uncertainty, we present two entanglement criteria and show that they detect better entanglement than the existing corresponding criteria by detailed examples.
Empirical assessment of the impact of blockchain technologies upon effective development of the banking system

This article is dedicated to studying the problems and prospects in development of financial market of the national economy of the Russian Federation in the conditions of globalization and integration of FinTech technologies. Consideration of potential risks and consequences of their usage in economic activity would contribute to substantiation of the key strategic vectors in development of the financial market and national economy overall. Methodological framework contains positions of the theory of strategic management, financial management, and procedural approach. Peculiarities of determination of the parameters of financial sector development within the framework of scenario forecasting of integration of blockchain technologies into the banking system are revealed using the set of methods of systemic, complex, logical, comparative and economic-mathematical modeling. Based on assessment of the emerging effects caused by integration of distributed data storage technologies into the system operational processes of credit institutions, the author carries out a formalized assessment of development prospects of the banking sector in new institutional conditions of the economy. The theoretical and practical significance of the research consists in substantiation of the need for activation of the processes of integration of blockchain technologies into the financial system of national economy, which allows increasing sustainability and effective development of its participants, as well as ensuring the growth of global economic competitiveness for decades to come. The scientific novelty consists in proposal of an algorithm for assessing macroeconomic effects as a result of integration of blockchain technologies into the banking sector. The author builds the models that assess the impact of operational and credit risks upon the key parameters of financial performance of the banking sector of the Russian Federation.
Multi-Agent Path Finding with Destination Choice

Multi-agent path finding problem (MAPF) is a problem to find collision-free paths on a graph for multiple agents from their initial locations to their destinations. MAPF has mainly two types of variants regarding the usage of agent destination; each agent has a unique destination or all agents share common destinations. We propose the MAPF with destination choice problem (MAPF-DC) as a new variant of MAPF. Agents in MAPF-DC could implicitly select the best destinations out of assigned destination candidates partially shared with other agents. Experimental results indicate that the total travel time declines with an increase in the number of destination candidates assigned to each agent.