Reviewerly/exHarmony huggingface dataset:
Possible ore-controlling structures for the Woxi Au/Sb/W deposit in Tanghuping Mining Section, Hunan Province, South China

The Woxi Mining District in Hunan Province, South China, hosts numerous high tonnage gold, stibnite, and tungsten strata-bound deposits. Our research focuses on the geological characteristics of the Tanghuping and Xintianwan faults in the Tanghuping Mining Section, within the Woxi Mining District. The Woxi Mining District exposes mainly slate, with interbedded quartz sandstone and clastic rocks, cut by three faults: the Tanghuping, Xintianwan, and Woxi faults. We test the hypothesis that the mineralization in this district is structurally controlled, related to motion along these faults. While the presence of Tanghuping and Xintianwan faults in the Tanghuping Mining Section has not been confirmed, the proposed faults are interpreted to be intimately related to mineralization. The results of previous field works have corroborated the location of the Tanghuping and Xintianwan faults and suggest that zones of high strain coincide with these faults. However, recent core logging data do not show evidence of fracture zones at the expected depth. It is therefore still unclear if the high strain is related to the presence of the faults or if it is the result of fold-related interlayer flexural slip. Microstructural analysis is used to further characterize strain-induced recrystallization textures and sense of shear near and within the interpreted fault zones. Understanding this relationship is important to assist exploration in detecting ore systems on a regional scale and identify the most profitable areas.
Two-Stage Depth Video Recovery with Spatiotemporal Coherence

This paper proposes a practical two-stage method to enhance the depth channel of low-quality RGB-D videos. At the first stage of the proposed method, we select several key frames from an input RGB-D video and recover them with a new MRF-regularized low-rank matrix completion method. At the second stage, we introduce a novel Laplacian smoothing algorithm to smoothly expand the recovered key depth frames to other in-between frames. With associated weights extracted from color images and a depth confidence constraint, we are able to recover each in-between frame faithfully and guarantee long-range temporal consistency. Experiments show that our method can generate high-quality and spatiotemporally coherent RGB-D videos for a wide range of scene configurations and achieve the state-of-the-art performance. Several applications further validate the effectiveness of the proposed method.
Fast 3D measurement system using generalized phase-shifting method for deformation measurement of human hand

Fast fringe projection 3D measurement system using a generalized phase-shifting method with a continuous fringe scanning scheme is proposed. Fast measurement can be performed owing to the continuous fringe scanning scheme. An object phase is obtained by a generalized phase-shifting method using normalization of randomly phase-shifted deformed fringe images. We conduct experiments on measurement of small deformation in muscle tendon of human hand during mouse operation. Experimental results indicate that the proposed method can quantitatively measure the deformation of human hand.
Distant Modeling of NIZK Protocol for Public Keys Certification

We present the IT solutions for educational modeling of non-interactive zero knowledge (NIZK) cryptographic public keys certification protocols. Certified public keys can then be used in a variety of non-interactive protocols, for example, in protocols using technique designated verifier proofs. The solution is based on algebraic means provided by MPEI algebraic processor. The IT solution supports the following educational purposes: obtaining practical skills of remote implementation of multiple non-interactive oblivious transfer and of non-interactive zero-knowledge proof protocol, mastering the methods of a remote automatic modeling of cryptographic protocols and of non-interactive public keys certification, and the technique designated verifier proofs.
Corrections to “Machine Speech Chain” [Mar 20 976-989]

Presents corrections to author information for the above named paper.
Gesture Recognition with 3D Sensors using Hidden Markov Models and Clustering

We propose a method for recognizing dynamic gestures using a 3D sensor. New aspects of the developed system include problem-adapted data conversion and compression as well as automatic detection of different variants of the same gesture via clustering with a suitable metric inspired by Jaccard metric. The combination of Hidden Markov Models and clustering leads to robust detection of different executions based on a small set of training data. We achieved an increase of 5% recognition rate compared to regular Hidden Markov Models. The system has been used for human-machine interaction and might serve as an assistive system in physiotherapy and neurological or orthopedic diagnosis.
Research on the Intelligent Assessment Method of Rural Financial Risks Under Supply-Side Structural Reform

Aiming at the problem of poor reliability of traditional financial risk assessment models, a rural financial risk assessment model is constructed under the background of supply-side structural reform. The structure of the evaluation model based on neural network is determined and the improved negative selection algorithm is used to generate the rural financial risk identification detector. The detection results of the identification detector are mapped to the nodes of the risk decision tree according to the mapping relationship, and the risk assessment model of rural finance is constructed. Compared with the traditional financial risk assessment model, it is proved that the rural financial risk assessment model under the background of supply-side structural reform is more reliable and superior.
Performance Analysis of FPGA Based Modified Adaptive Median Filter (M-AMF) Design for Medical Applications

The filtering mechanism in digital image processing is utilized to remove the undesirable elements or features from the image. The median filtering is a popular filter technique to suppress the undesirable impulse noises. In this paper, an efficient Modified- Adaptive Median Filter (M-AMF) is designed to filter the impulse salt-and-pepper (SP) noise at different levels 1%, 5 % and 10% for medical applications. The modified-AMF method using three-input sorter includes window 3×3 module, median filtering, and adaptive computation with error detection module. The median filter uses the sorter module to find the median value. The adaptive computation module obtains either the median filtered output or center pixel value from the window module, based on the adaptive condition. Error detection module obtains the corrupted pixels from the filtered output image. To evaluate the performance metrics of Modified-AMF on Xilinx platform, PSNR and MSE are calculated for medical Brain MRI image with the different noise level and also a comparison of similar exiting methods with an improvement of hardware constraints includes area and maximum operating frequency is tabulated. The proposed M-AMF model is synthesized on Xilinx ISE 14.7 and simulated on ModelSim and Implemented on Artix-7 FPGA.
An Analysis Of Teaching Strategies Used By English Lecturers In Increasing Students’ Motivation At Fourth Semester In STKIP Taman Siswa Bima In Academic Year 2018/2019

The purpose of research were to analyze teaching strategies used by English lecturers and to analyze students responses of teaching strategies used by English lecturers in increase students’ motivation. This research were descriptive qualitative research method. The subject of this research were the lecturers in English Education Program, Teacher Training and Education. To collect the data, the researcher used video recording, interview, questionnaire and document to added the data. Based on the result finding the researcher can be concluded that the teaching strategies used by English lecturers can increase students’ motivation at fourth semester. English lecturers at fourth semester used same strategies in teaching based on video record and different strategies of the result interview. The first lecturer by applying always used discussion strategies and the second lecturer sometimes used cooperative strategies but dependent on the lesson different lesson different strategies his used but he always used strategies in teaching. Of the 21 students 18 students gave good responses to the first English lecturer while 3 students gave bad responses. If the second English lecturer of the 24 students all of the students gave good responses.
NuMessage: Providing Scalable and Reliable Messaging Service in Distributed Systems

For e-commerce companies with complex businesses like eBay, messaging oriented middleware has become a critical component of a large-scale distributed system, to support real-time asynchronous communication. In this work, we introduce novel messaging oriented middleware named as NuMessage, which can provide universal messaging service, including supporting push and pull modes and different scenarios. We also propose a retry mechanism to guarantee each message can be delivered and processed at least once. Various interfaces are implemented in NuMessage, making it easy to deploy NuMessage in practice. Experiments are conducted in a real system, and the results show that NuMessage can achieve superior performance when there are message consuming failures happening. Moreover, we have adopted NuMessage in eBay for some time to process 12 billion of messages per day.
Evaluation Study of Kedunglarangan River To Protect Flood In Pasuruan and Sidoarjo

Construction of the Kedunglarangan river flood control system designed to prevent flooding every rainy season in the Bangil sub-district. Kedunglarangan River is a river that flows in two regencies Sidoarjo and Pasuruan which has an area of 282.67 km2 watershed with a river length of 23.7 km. Kedunglarangan river has 4 (four) watershed sub-systems. The scope of this flood prevention work-study is the normalization of the Kedunglarangan River starting from the meeting with the Wrati River downstream up to 7 km. Normalization work is carried out with excavation and river widening to meet flood discharge in accordance with the conditions of the study area. If the river excavation work is done in accordance with the design master will form a basin that causes the creation of a dike. In this condition, it will be a temporary water reservoir where the water velocity is very low. So the work carried out the impact is only temporary. From the results of analysts, it is more efficient to do river widening and embankment raising rather than increasing river depth. River excavation work like that is very risky to create very fast sedimentation. Normalization method with river widening is one way to maintain the river flow downstream and flood water levels
Case study of human pointing gestures and their data analysis

The main contribution of this work is a method to generate datasets of pointing gestures. A person points to tracked objects within a motion capture environment and a neuronal network further processes the tracking data. We found that different input data combination has no significant effect on the performance of the networks. Therefore, it is possible to train a network and obtain correct results, regardless of the complete availability of data for head, shoulder, elbow or hand. With the presented method, we could achieve an overall accuracy of 35 mm within a 2D plane over a distance of 2 m.
Detecting Sudden Drift for Single-Instance Log in Software Development Process

The current algorithm for detecting drift in the business process can only detect the drift of the log of multi-instance. However, the log generated in the software development process is mainly single-instance. Hence, this paper proposes an algorithm for detecting sudden drift of the log of single-instance. First, we apply the LDA topic model for the topic extraction of log in the software development process to obtain the log of single-instance. Then the location of the drift point of the single-instance log can be detected according to the change of the lowest frequency of direct successor relation. Finally, the location numbers of the actual drift points were obtained by corresponding to original data. Multiple sets of simulated data evaluate this algorithm. The results show that this algorithm can effectively detect the sudden drift location of the single-instance log in the software development process.
Categorical Classification Approach for Identifying Multi-SIM Users from Call Detail Records

In this paper, we present a categorical classification approach for identifying multi-SIM users from Call Detail Records. Multi-SIM user classification is an unexplored domain in research literature and remains a challenging problem due to the diversity in telecom user population. This paper presents a subpopulation-based classification approach which incorporates this variety into the model, which is able to identify multi-SIM usage with higher precision and recall. A comparison of our approach to other baseline approaches (Gaussian Naive Bayes, Bernoulli Naive Bayes & Linear SVC) shows the effectiveness of subsample modelling for detecting multi-SIM usage. Additionally, we present an empirical study with which we quantify the contribution of oversampling and feature selection for multi-SIM detection. Further, using feature importance, we are able to identify possible rationales behind multi-SIM usage.
Productivity of the NN pattern and compound noun formation in French

This study aims to analyze the productivity of the NN pattern and reveal the formation mechanism of French compound nouns. The NN pattern could embed complex structure on N1 or N2 and the constitutive elements are linked in a hierarchical relation. The analysis is conducted from four levels: structure hierarchy, compounding method combination, semantic relation and semantic class combination. A pattern should be defined by four parameters and their productivities for forming compound nouns vary with vocabulary. The formation process is analyzed based on the corpora, which are elaborated by annotating both the formal and semantic information. This study presents in detail the formation mechanism of NN compound nouns and it allows well predicting the production of compound nouns. The analysis method could be applied to other patterns and vocabularies. The annotated resource and analysis results contribute to the research in language acquisition, lexicography and natural language processing.
Image Retrieval Based On Multi-Semantic Region Weighting And Multi-Scale Flatness Weighting

The feature representation of images is the key to bridge the semantic gap and make computer understand images in the tasks of image retrieval. Semantic regions and salient targets are irreplaceable parts of cognitive images in image recognition. This paper proposes an unsupervised image retrieval method based on multi-semantic region weighting and multi-scale flatness weighting. Firstly, we divide the semantic regions by using the Fully Convolutional Network (FCN) and calculate the multi-Semantic weight map (S-mask) to obtain the global features. Secondly, we introduce a flatness-weighted strategy to weight feature maps and aggregate the multi-scale features to obtain the local features. Finally, we cascade the global features and the local features to construct the final image representation. Experimental results on two widely-used databases demonstrate that the proposed method is effective and significantly outperforms the state-of-the-art retrieval methods.
Adaptive Depth Network for Crowd Counting And Beyond

Crowd counting is a challenging computer vision task which aims to estimate people count in crowded scenes. Although CNN-based methods have designed multi-scale or multicolumn structures to cope with scale variation within one image, the variety of distribution features among different images has not been taken into consideration, which is difficult to handle in a fixed scheme. In this paper, we propose a multi-output structure network named Adaptive Depth Network (ADNet) that can adaptively adjust the network’s depth according to the inputs’ features. This flexible model introduces extra output blocks into internal layers to exploit their representation abilities and selects the output from the output block that produces the best confidence value as the final result. In our experiments on three crowd counting datasets, ADNet shows a consistent improvement. Moreover, ablation study also proves the effectiveness of the multi-output structure on both crowd counting datasets and CIFAR-100.
MeXT-SE: A System-Level Design Tool to Transparently Generate Secure MPSoC

This paper presents the MeXT-SE (Multiprocessor Exploration Tool with Security Extension), an FPGAbased MPSoC development tool capable of generating platformindependent MPSoCs with enforced hardware access control mechanism from a high-level abstraction.
Image Dynamic Range Enhancement Based On Fusion Pyramid

Learning techniques have been widely applied in image processing, including dynamic range enhancement. But they require a lot of computational resources to train and run. As an alternative, in this paper we propose an algorithm utilizing only traditional image processing techniques to achieve better results on test datasets. We adopt a contrast-based fusion approach. First, the original image is segmented into multiple regions. Then, several enhanced images are generated and evaluated for their quality. Finally, the best enhanced images for each region is selected and then fused together using an image pyramid. We achieve a 3.9% performance improvement over the state of the art, based on experimental results on datasets.
Libraries’ accomplishing military and defense tasks during the Great Patriotic War

During the pre-war and war [WWII] years, the libraries took part in accomplishing military and defense tasks: promoted knowledge on air and chemical defense, sanitation, etc. The libraries participated in universal military training of population. They cooperated closely with Osoaviakhim (Society for the Promotion of Aviation and Chemical Defense) that was publishing a number of specialized publications intensively used by the libraries in their popularization work. The libraries contributed to the country’s defensive power both theoretically and practically. They conformed to the decrees of the Council of People’s Commissars and The State Defense Committee (in the first war days). The author specifies the main vectors of the libraries’ interaction with the Red Army divisions, training of new recruitees, front support. The promotion of military defense knowledge by the libraries all over the country including the capital libraries and the libraries in Soviet republics is also discussed. The article is based on archival materials listed in the bibliography attached.
An Improved Image Inpainting Technique Using Fuzzy Hard C-Means Algorithm

A novel algorithm which can repair video sequence deprived of any artefacts that are present in numerous such prevailing technique is proposed in this paper. Specifically, a video inpainting is proposed that detects moving object in a background where water is falling from a fountain. The input video is converted to frames. Using the Canny edge detection, the edges of the objects in the frames are found out. These edges are classified into different clusters using fuzzy hard C-means algorithm so that inpainting can be done effectively. The classified edges are patch-matched with the most similar pixels. The resultant inpainted video when viewed gives a very good result with minimum time frame.
A survey on technique for solving web page classification problem

Abstract Nowadays, the number of web pages on the World Wide Web has been increasing due to the popularity of the Internet usage. The web page classification is needed in order to organize the increasing number of web pages. There are many web page classification techniques that have been proposed by the other researchers. However, there is no comprehensive survey on the performance of the techniques for the web page classification. In this paper, surveys of the different web page classification techniques with the result of the techniques achieved are presented. The existing works of web page classification are reviewed. Based on the survey, we found that the neural network technique namely Convolutional Neural Network (CNN) produce high F-measure value and meet the real-time requirement for classification compared to the other machine learning technique.
Towards Neural Ar: Unsupervised Object Segmentation with 3d Scanned Model Through Relative

Neural AR augments the reality with a neural model to enable direct manipulation of the reality. This demands object tracking for augmentation, but there is not much work as in typical AR setting. Since most computer vision models manipulate on an image level, effectively tracking a target object for manipulation manifests as a segmentation problem. To the best of our knowledge, there is a gap for a model to perform unsupervised object segmentation with a 3D scanned model useful for Neural AR in reducing the labor-intensive data annotation. We propose in this paper a CycleGAN-inspired model, Realistic Layered Training Image from Virtual Environment (ReLaTIVE), which only requires a user to 3D scan a target object as with typical AR. The ReLaTIVE generator then outputs the object’s mask for Neural AR. Without any annotated segmentation mask, it enables the generation of training samples with the 3D scanned model for learning to separate the foreground and background.
Study on Stocks Based on Big Data Methods and Computer Programs

Recently big data is a hot topic in many fields. Here we use distributed algorithms to treat big data in Chinese stock market. First the two stock indexes (Shanghai Composite and Shenzhen Composite indexes) from 1990-2019 are collected. Then the data are divided to several subsets and are analyzed separately via computer programs. Finally the results for every subset are combined due to distributed algorithms theory. In addition to big data scheme, we adopt stochastic processes theory and moment equations methods.
Cognitive Machinery and Behaviours

In this paper we propose to merge theories and principles explored in artificial intelligence and cognitive sciences into a reference architecture for human-level cognition or AGI. We describe a functional model of information processing systems inspired by several established theories: deep reinforcement learning mechanisms and grounded cognition theories from artificial intelligence research; dual-process theory from psychology; global-workspace theory, somatic markers hypothesis, and Hebbian theory from neurobiology; mind-body problem from philosophy. We use a formalism inspired by flow-graph and cybernetics representations. We called our proposed model IPSEL for Information Processing System with Emerging Logic. Its main assumption is on the emergence of a symbolic form of process from a connectionist activity guided by a self-generated evaluation signal. We also discuss artificial equivalents of concepts elaboration, common-sense and social interactions. This transdisciplinary work can be considered as a proposition for an artificial general intelligence design. It contains elements that will be implemented on further experiments. Its current aim is to be an analyzing tool for Human interactions with present and future artificial intelligence systems and a formal base for discussion of AGI features.