Reviewerly/exHarmony huggingface dataset:
Capability Model of Judicial Document Delivery System

Evaluation of judicial document delivery capability based on a systematic model is a necessary basis for summarizing the innovation effect and further upgrading of judicial document delivery system. After investigating the progress of judicial document delivery in serveral courts, the development trends of judicial document delievery, including diversification, servitization and intensification are summarized. The relevant parties and infrastructures of judicial document delivery system around relevant information systems are analyzed, including 12 kinds of relevant parties under 4 categories. By referring to mature capability models such as CMMI and PPTR, a capability model of judicial document delievery system is developed, which covers 3 kinds of service capability for users of the system, as well as management capability in different aspects such as technology, organization, system and process. Finally, a recommended application process of the capability model is given.
Mixed Pruning Method for Vehicle Detection

Object detection is a popular direction in computer vision and digital image processing, and has important implementation significance in many fields. In unmanned driving, it is also an indispensable component, and vehicle detection as its important branch has also received much attention. With the continuous development of deep learning in recent years, convolutional neural networks have been widely used for object detection. Compared with traditional detection methods, it has better generalization ability and recognition accuracy. However, most current deep learning-based object detection methods rely on the graphics processor (GPU) to ensure the real-time detection. This is difficult for an unmanned system with limited memory and computing power resources to support its operation. In order to solve the problem of excessive resource consumption of these hardware platforms, we propose a vehicle detection algorithm based on a hybrid pruning model, which ensures that the accuracy of the model is within a controllable range while reducing the number of model parameters as much as possible. The model increases the sparsity of the network by using the L1 norm regularization method, and cyclically prunes the model through channel pruning and layer pruning. The experimental results show that the compressed network model is reduced by 83% compared to the original model, and the corresponding speed is reduced to half of the original.
Reconstruction method for optical imaging of 3D particle detection

It is a key problem to get the high resolution and large field of view (FOV) of dynamic measurement in the display technique. In this paper, Charge Coupled Device (CCD) was synthesized with seams to be a display unit. A reconstruction method is proposed which combines the principles of synthetic aperture and phase retrieval to detect the 3D particle target with synthetic CCD. In the simulation experiment, the correlation coefficient of surface l is 0.73217 between the reconstructed image and the simulated particles target image. The pictures from the experiment depicts the particle is not only clearly visible at the normal region but also at the seams. The optical information can be recovered well of the high resolution and large field of view (FOV) with this method.
Anticipating Maintenance in Telecom Installation Processes

Improving customer experience is crucial in any industry, especially in telecommunications, where competition is a constant factor. Today, all telecommunications companies rely on the massive amount of data generated daily to get to know the customer or study their behavior and thus create new effective strategies for their business. Within the most varied user experiences, the process of installing new services can be an event that raises doubts about their operation, degrade the user experience, or, in extreme cases, lead to maintenance interventions. Therefore, the use of advanced predictive models that can predict such occurrences become vital. With this, the company can anticipate the cases that will be problematic and reduce the number of negative experiences. The main objective of this work is to create a predictive model that, through all the available data history, can predict which customers will contact the customer service with problems derived from the installation process and have a following maintenance intervention. After analyzing an unbalanced dataset with approximately 560K entries from a Portuguese telecommunications company, and resorting to the CRISP-DM methodology for modeling, the best results were found with LightGBM which obtained an AUPRC of 0.11 and AUROC of 0.62. The best trade-off between precision and recall was found with a threshold model of 0.43 in order to maximize recall while still avoiding a large number of false negatives.
CORPORATE CYBERESPIONAGE: IDENTIFICATION AND PREVENTION PART 2

The purpose of this paper is to present an argument for a methodology to detect and prevent cyber-based corporate espionage. The focus of this paper will be on the human domain and how it affects control measures in the electronic and physical domain of cybersecurity. The reason for this approach is because the human domain has consistently been identified as the irrational and unpredictable component in cybersecurity. Human actions, when related to cyber-based corporate espionage, cannot be described through rudimentary factors supporting causation for “betrayal” and the motivation to compromise proprietary technology and economic information belonging to a business entrusting its secrets with an employee. The underlying issue is about how to identify a potential actor willing to compromise their “integrity” in exchange for personal gain or personal interest even when the penalty will be far greater than the reward. The next step is to propose preventative measures to either dissuade such behavior or expose a threat actor intending to compromise a corporation’s information assets.
MCGKT-Net: Multi-level Context Gating Knowledge Transfer Network for Single Image Deraining

Rain streak removal in a single image is a very challenging task due to its ill-posed nature in essence. Recently, the end-to-end learning techniques with deep convolutional neural networks (DCNN) have made great progress in this task. However, the conventional DCNN-based deraining methods have struggled to exploit deeper and more complex network architectures for pursuing better performance. This study proposes a novel MCGKT-Net for boosting deraining performance, which is a naturally multi-scale learning framework being capable of exploring multi-scale attributes of rain streaks and different semantic structures of the clear images. In order to obtain high representative features inside MCGKT-Net, we explore internal knowledge transfer module using ConvLSTM unit for conducting interaction learning between different layers and investigate external knowledge transfer module for leveraging the knowledge already learned in other task domains. Furthermore, to dynamically select useful features in learning procedure, we propose a multi-scale context gating module in the MCGKT-Net using squeeze-and-excitation block. Experiments on three benchmark datasets: Rain100H, Rain100L, and Rain800, manifest impressive performance compared with state-of-the-art methods.
Stochastic objects velocity estimation using Modified Time Mutability Method, based on two-dimensional images

The time mutability method allows estimation of drift velocity of stochastic objects basing on sequences of their images. A procedure for constructing and selecting rotational radial-sections from two-dimensional images through which a velocity field can be constructed is proposed. The constructed sections are applied to determine the velocity field of a cloud field based on a series of its two-dimensional images. Numerical examples, illustrated by graphic material, have been shown.
Zero Trust in the Context of the Utility Industry

Zero Trust is an evolving paradigm in modern information security. A number of drivers have led to the need for operation of systems in an environment where networks and endpoints cannot necessarily be fully trusted. Existing standards in different industries are not in synch with this evolving landscape and there is gap between where these standards need to be and where they are. The purpose of this paper is to provide a background on existing models and current research and propose a novel model to address Zero Trust for utility organizations.
Expressing Marginal Identity through Living House

Abstract As landed-sea gipsy, Akit people recognized as an ethnic group who are trying to adopt some different cultures and traditions in their daily life. In the adopting process, the people are settled down on a coastal area of an island, or at river’s estuary compound. This process has seen as a way to ensure preserving their sea life tradition and also to adopt land traditions at the same time. Through field survey and in-depth interviews method, this paper shows how most Akit people at the Rupat Island foster Chinese culture in their daily life, including on their house. The house is not only to show their marginal cultural identity but also to express the influence of the Chinese-side that can be seen both on their traditional and contemporary home. This paper confirms that the living house of Akit people has found as a form of compromising cultural life in expressing their today’s identity.
Commodity Trading in the Blockchain Technology Era

The emergence of Blockchain technology is gradually disrupting the traditional way of trading commodities. To go beyond the theory to the practical cases, this chapter provides an overview of the Blockchain-based digital transformation process behind and the pre-requisites for its inclusion in the commodity industry. A reality check through vivid examples of global companies highlights the increasing attention drawn to the Blockchain. Furthermore, this chapter discusses the impact of Blockchain technology's use in improving commodity finance trades at different levels and optimizing the transactions' effectiveness. Finally, future challenges and useful perspectives for managers and commodity firms are spotlighted.
Study on quantum adiabatic approximate solution of max-cut with different vertices

The max-cut is used to solve maximum partition for given undirected weighted graph, the sum of the weights of all sides between the subset of vertex and complementary subset obtains a maximum value. Quantum adiabatic approximation is used to design system Hamiltonian H<sub>sys</sub>, and the ground state of system Hamiltonian corresponds to the solution of max-cut problem. System Hamiltonian H<sub>sys</sub> changes slowly along with initial Hamiltonian Hini follow the evolution path of Hamiltonian H<sub>max</sub>, and then the ground state of Hamiltonian can be calculated. By analyzing the change of expected value with evolution time, one can judge whether the approximate solution is the optimal solution or not. In this paper, we test the adiabatic evolution of complete undirected graph with vertices from 6 to 14. Based on Python and Project Q package, we write a solver program that sets parameters according to the number of vertices and edges of undirected graph, and then obtain experimental results by measuring the state of qubits in quantum registers. It can be inferred from experimental results that for a complete undirected graph with less vertices, expected value can converge well, and then the optimal solution of max-cut problem can be obtained. When the number of vertices increase, the energy variation of Hamiltonian become more complex and the expected value is hard to converge.
Comparison of water availability in 2015 and 2022 based on land cover in the Maros River Basin

Abstract Forest as one of permanent vegetation can have an impact either to increase or decrease the quality of a watershed due to the changes in land cover. Land use activities that are changing landscapes within a watershed can often affect water yield. This study aims to determine the availability of water in Maros watershed in 2015 and 2022 and to identify the sub-watershed experiencing changes in water availability. Data analysis in this study uses the SWAT (Soil and Water Assessment Tool) model. Input data used in the analysis are the 2015 land use map, 2022 land cover projected map (Markov model analysis), soil type map, and Digital Elevation Model (DEM) data. The results showed that land cover in 2015 and 2022 was projected to change. The number of settlement and rice fields area was increased, while the forest vegetation was decreased. The change in land cover will affect water availability. The result of the SWAT modeling analysis showed that the availability of water in Maros Watershed in 2015 was 1,303,169,370 m 3 , and in 2022 was 1,322,495,380 m 3 . The volume of water availability from 2015 to 2022 has increased by 19,326,010 m 3 . The increase that occurred was influenced by the surface flow, which increased from 2015 to 2022. There are 38 sub-watershed, which have decreased water availability, 26 sub-watersheds in which the water availability was increased while one sub-watershed did not change at all.
Mental Imagery-Driven Neural Network to Enhance Representation for Implicit Discourse Relation Recognition

Implicit discourse relation recognition is an important sub-task in discourse parsing, which needs to infer the relation based on proper discourse comprehension. Recent studies on cognitive learning strategies have suggested that using mental imagery strategy will foster text comprehension, which could effectively improve the capability of learners’ reading. Therefore, we propose a novel Mental Imagery-driven Neural Networks (MINN) to enhance representation for implicit discourse relation recognition. It employs the multi-granularity imagery vectors generated by the arguments to capture the deeper semantic information of discourse at different scales. Specifically, we 1) encode the different granularities of arguments (i.e., phrases, sentences.) and generate the corresponding imagery vectors as mentally imagining images of text content; 2) fuse the argument representations and imagery vectors as sequence representations; 3) further adopt self-attention to mine the important interactions between the sequence representations to infer the discourse relations. Extensive experimental results on the Penn Discourse TreeBank (PDTB) show that our model achieves competitive results against several state-of-the-art systems.
Moment Factory, <i>Terra Lumina</i>

What will the world be like in the year 2099? Will humanity unite to overcome the devastating effects of climate change? Will current ecological disasters be a catalyst to promote earthly security ...
Known-Key Distinguishing and Partial-Collision Attacks on GFN-2 with SP F-Function

We study known-key distinguishing and partial-collision attacks on GFN-2 structures with various block lengths in this paper. For 4-branch GFN-2, we present 15-round known-key distinguishing attack and 11-round partial-collision attack which improve previous results. We also present 17-round known-key distinguishing attack on 6-branch GFN-2 and 27-round known-key distinguishing attack on 8-branch GFN-2 and show that several partial-collision attacks are derived from them. Additionally, some attacks are valid under special conditions for the F-function.
The Devil is in the Detail: Deep Feature Based Disguised Face Recognition Method

Face recognition have been developed rapidly, launched by the breakthrough of Deep learning based face representation method. However, disguised face verification in the wild is still a challenge problem. To address this issue, we propose a novel deep feature based disguised face recognition scheme (DDFR). DDFR introduces the multi-scale residual network with AM-softmax loss for learning face representation. In training stage, we put the different occlusions (mask, sunglasses and scarf et al.) on clean face images to enhance the diversity of training set. Meanwhile, both aligned face image and un-aligned face image are combined to improve the discriminative power of feature representation for disguised face verification. Experimental results demonstrate that the proposed method achieves the better results than state-of-the-art methods on the DFW (disguised face in the wild) set.
The ECF-WS estimator for univariate symmetric stable distributions with application in seismic trace signals

The parameter that is of most interest in applications of stable distributions is the stability index α, which determines how “heavy” the tail is. For applications when the main interest is on the tail, we develop an estimator of α for univariate symmetric stable distributions, based on the characteristic function and on the principle of wavelet shrinkage. We analyze the proposed estimator by the Monte Carlo simulations and prove that it is a consistent estimator. The presence of human-made explosions in a seismic catalog leads to errors in the statistical analysis of seismicity. Here, we also proposed the CWS coefficient, based on the proposed estimator, like an additional classification tool to discriminate between natural tectonic earthquakes and human-made explosions.
A Method for Fast Capturing Human Face in Video Image Based on Color Features

We propose a method to fast capture faces in video images, in order to make full use of the screening effect of color features, in order to find the face frame in the video image more quickly, in order to cut out the face in the picture containing the face information frame, filter out the background part, and in order to expand the facial recognition algorithm application method. The method is to use the color features of each frame of the video in the video image to screen the skin color. After screening, the image is sampled and counted. If the threshold is exceeded, it is judged whether it is a human face. When determining, the face is captured and output and the threshold is corrected according to the recognized face information; if the threshold is not exceeded, skip some frames and then prejudge the frames. Experiments on videos containing face information with more complex backgrounds prove that this method can quickly find frames containing face information, and can intercept faces from frames containing face information, saving some time, and it basically does not affect the accuracy of face capture.
Chinese Language Standards of Primary and Secondary Texts

Chinese is taught as a second language in Singapore, taking up 15–20% of curriculum time in primary and secondary schools and without the support of the other subjects in the curriculum. The notion of a two-year difference with Chinese as a first language is common but with no official documentation. This study aims to verify the standard gap between Chinese as a first language in China and as a second language in Singapore, using samples of texts of the two countries. It was found that Primary 1 texts of China are equivalent to Primary 3 in Singapore in terms of readability, based on a newly developed readability formula. The gap gets gradually wider at the primary level but fast widening and erratic at the secondary level. Probably causes and implications are discussed.
Mineral Content Conversion by Piecewise Polynomial Truncated Singular Value Decomposition Algorithm in the Context of Formation Element Logging

Abstract In spite of diverse types of elements and minerals in the stratum, there are only a few kinds they are mainly concentrated, thus the key to element-mineral conversion lies in the quantitative relationship between main elements and main minerals, which task, however, is challenged in practical logging. Firstly, the element types detected by logging tools are fixed, while the mineral types vary in different regions, which makes it difficult to determine the mineral type merely from logging data accurately. Secondly, the mineral types are generally more than the element types detected in the stratum, leading to a multiplicity of the conversion results. Therefore aiming to solve such challenges, this paper establishes a reasonable conversion relationship between the element and mineral, and ultimately attains relatively precise results through certain mathematical method, i.e. piecewise polynomial truncated singular value decomposition (PP-TSVD) algorithm that eliminates “small” singular values of the ill-conditioned conversion coefficients matrix, and meanwhile makes a superimposition of the effective components in the “small” singular value spectrum in the solution of the TSVD method, which greatly reduces the mean square error of the solution, so as to improve the accuracy and reliability of the solution. In addition, PP-TSVD method can determine either the mineral content of single mineral or diversified types, on condition that the known element types are not less than the mineral types to seek. When mineral types are numerous, the integration of some types into one mineral is imperative in order to establish suitable conversion coefficients. In this paper, based on previous studies and the #JD1 well data measured by ECS logging tool, the PP-TSVD method is employed to study the calculation and conversion between the element content and mineral content.
Research on the Technology of Dynamic Information Fusion Processing of 3D Animation Images

In order to improve the recognition and detection ability of movie and TV 3D animation images, a method of movie and TV 3D animation image information fusion and feature extraction based on dynamic information fusion and edge contour feature detection is proposed. Establishing a visual information acquisition model of a movie and television three-dimensional animation image, performing corner detection and feature point extraction of the movie and television three-dimensional animation image by adopting a key feature point matching method, extracting dynamic information feature quantity of the edge of the movie and television three-dimensional animation image, combining saliency detection, feature fusion and optimal segmentation of the movie and television three-dimensional animation image, establishing a super-resolution reconstruction feature distribution set of the movie and television three-dimensional animation image. The edge information space region fusion method is used to fuse pixel information and optimize feature reorganization of movie and television three-dimensional animation images. The fuzzy feature distribution set of movie and television three-dimensional animation images is extracted. The information fusion and feature extraction of movie and television three-dimensional animation images are realized by combining wavelet multi-layer decomposition algorithm. The simulation results show that the feature resolution capability of using this method for information fusion and feature extraction of movie and TV 3D animation images is higher, and the output peak signal-to-noise ratio of movie and TV 3D animation image reconstruction is improved.
Programming for Moving Bodies

Movement plays an increasingly important role in interactive systems design, from users’ physical engagement, to how designed artefacts can move or be moved, and to the concert between users and artefacts. Designers, as well as programmers, have to engage more and more in physical activities when they want to create appealing experiences involving movement. There is a need for articulating emerging dialogues between designers, developers, and their materials. We will explore such dialogues in a 2-half-day workshop, focusing on data and its challenges, on tools and methods, on sensing and actuation when designing or detecting subtle body movements, and on catering for bodily changes over time.