Reviewerly/exHarmony huggingface dataset:
Assisting Ultrasound Examination Based on Transfer Learning Method

Ultrasound examination is the preferred method for related diseases. The transfer learning method is considered to be a feasible and automatic method for optimizing the ultrasound examination. This paper uses the transfer learning method the diagnosis of hepatic hemanguimas. First, establish an optimized deep learning technology model. A stacking convolutional self-coded depth model is designed to deal with the lack of label image dataset Based on CNN. Second, External transfer learning technology model is established to the automatic classification diagnosis of ultrasound images according to the transfer learning technique. Comparing with the two results and combined with the artificial examination in our experiments, the diagnosis based on the transfer learning method can achieve an acceptable result.
Research on Virtual Reality Arm Motion Capture and Recognition

In current virtual reality (VR) applications, when you wave your hand with the VR controller, you will see a controller floating in the air and shaking from side to side in a VR environment. Or in some better situation, there is also a wave of your virtual arm. But the virtual arm is moving in a different way with your real arm or a higher delay, and you will feel that the virtual arm is clumsy. This situation seriously reduces the user immersion feeling that VR industry attach great importance to. There are systems that can quickly and accurately capture and replay arm motions nowadays, but they undoubtedly cost more than a normal VR device. Therefore, we need a set of fast, accurate and cheap arm motion capture, recognition and replay solution that can be used in VR environment. Based on the HTC VIVE platform, this paper proposed a much less expensive kinematics-based VR arm motion solution. The traditional dynamic time warping (DTW) algorithm is improved by feature constraint and feature matching order strategy. Experiments show that the new algorithm can improve the response speed by 36% and reduce the response time to 216 milliseconds while maintaining the accuracy of 95.6%. The whole arm motion solution can greatly improve users’ immersion experience in a VR environment.
Virtual restoration and content analysis of ancient degraded manuscripts

In recent years, extensive campaigns of digitization of the documental heritage conserved in libraries and archives have been performed, with the primary goal to ensure the preservation and fruition of this important part of the human cultural and historical patrimony. Besides protecting conservation, the availability of high quality digital copies has increasingly stimulated the use of image processing techniques, to perform a number of operations on documents and manuscripts, without harming the often precious and fragile originals. Among those, virtual restoration tasks are crucial, as they facilitate the traditional work of philologists and paleographers, and constitute a first step towards an automatic analysis of the written contents. Here we report our experience in this field, referring, as a case study, to the problem of removing one of the most frequent and impairing degradations affecting ancient manuscripts, i.e., the bleed-through distortion. We show that techniques of blind source separation are versatile tools to either cancel these unwanted interferences or isolate specific features for content analysis goals. Specialized algorithms, based on recto-verso models and sparse image representation, are then shown to be able to perform a fine and selective removal of the degradation, while preserving the original appearance of the manuscript.
Study on Prediction of Power Allocation for the Double-Wheel Trench Cutter Control System Based on Extreme Learning Machine Method

It is uneasy to obtained the accurate mathematical model of a double-wheel trench cutter owing to the dynamics, uncertainty and complexity of the cutter and the working environment. In order to solve this problem, an approach based on neural network was applied to develop a prediction model. The torques and feeding depth of each milling wheel were set as the input variable and while the power of milling pumps was set as the output variable. The comparisons of the simulation and experimental prediction of the extreme learning machine method have been presented and the radial basis function method was utilized as a comparison group. The results obtained from the extreme learning machine method are satisfactory and superior to the radial basis function method.
Panoramic perspective of Digital Investigation

File system forensics is an important part of Digital Forensics. Investigators of storage media have traditionally focused on the most commonly used file systems such as NTFS, FAT, ExFAT, Ext2-4, HFS+, APFS, etc. NTFS is the current file system used by Windows for the system volume, but this may change in the future. In this paper we will show the structure of the Resilient File System (ReFS), which has been available since Windows Server 2012 and Windows 8. The main purpose of ReFS is to be used on storage spaces in server systems, but it can also be used in Windows 8 or newer. Although ReFS is not the current standard file system in Windows, while users have the option to create ReFS file systems, digital forensic investigators need to investigate the file systems identified on a seized media. Further, we will focus on remnants of non-allocated metadata structures or attributes. This may allow metadata carving, which means searching for specific attributes that are not allocated. Attributes found can then be used for file recovery. ReFS uses superblocks and checkpoints in addition to a VBR, which is different from other Windows file systems. If the partition is reformatted with another file system, the backup superblocks can be used for partition recovery. Further, it is possible to search for checkpoints in order to recover both metadata and content.Another concept not seen for Windows file systems, is the sharing of blocks. When a file is copied, both the original and the new file will share the same content blocks. If the user changes the copy, new data runs will be created for the modified content, but unchanged blocks remain shared. This may impact file carving, because part of the blocks previously used by a deleted file might still be in use by another file. The large default cluster size, 64 KiB, in ReFS v1.2 is an advantage when carving for deleted files, since most deleted files are less than 64 KiB and therefore only use a single cluster. For ReFS v3.2 this advantage has decreased because the standard cluster size is 4 KiB.Preliminary support for ReFS v1.2 has been available in EnCase 7 and 8, but the implementation has not been documented or peer-reviewed. The same is true for Paragon Software, which recently added ReFS support to their forensic product. Our work documents how ReFS v1.2 and ReFS v3.2 are structured at an abstraction level that allows digital forensic investigation of this new file system. At the time of writing this paper, Paragon Software is the only digital forensic tool that supports ReFS v3.x.It is the most recent version of the ReFS file system that is most relevant for digital forensics, as Windows automatically updates the file system to the latest version on mount. This is why we have included information about ReFS v3.2. However, it is possible to change a registry value to avoid updating. The latest ReFS version observed is 3.4, but the information presented about 3.2 is still valid. In any criminal case, the investigator needs to investigate the file system version found.
Designing an Integrated Intelligence Center: New Taipei City Police Department as an Example

The rapid advancement and prevalence of Internet technology was the biggest development of the 20th century. Because criminals use the Internet to commit crimes, police work has needed to shift from traditional methods to modern technology. As investigation strategies have evolved, sharable databases and integrated intelligence have become increasingly important. By maintaining data, information professionals play a crucial role in police departments, and an integrated, sharable system is essential for supporting police work. New Taipei City is one of six main cities in Taiwan and has the largest population. Social order and traffic are the city’s greatest issues. The New Taipei City Police Department (NTPD) uses integrated resources and new technology to help first-line police and investigators focus on these issues. To solve complicated problems, an integrated intelligence center (IIC) was designed to provide needed data, help team members analyze information, and guide users in searching information systems. The IIC’s services have successfully supported social maintenance tasks such as covering celebration events and city elections. The support of IIC team members has been recognized with Outstanding Government Employee and Best Contribution awards from the Taiwanese government. This paper shows the IIC’s structure and how it is designed with innovation approach how it functions.
Evaluation of Tool-based Gesture Extractions

There are different approaches in the literature for proposals for gestures are collected from potential users and then combined into a gesture set by considering different criteria (e.g. popularity of gestures). The effort for such surveys is high and thus the samples often accordingly low. In this paper we describe the process of gesture extraction, which is supported by our online service GestureNote, as well as the results of its first evaluation with the help of inexperienced users (i.e. beginners in working on the design of gestures and the use of GestureNote). A total of 30 students took part in the study. All test persons successfully carried out the desired a guessability study, which shows that the primary concepts are appropriate. The results suggest adjustments to the classification of gestures, e.g. by adding a weighting factor to the formula for the calculation of the agreement rate.
Smart optical interaction simultaneously integrated with optical touch and fingerprint sensing

Finger touch-based interactions relying on real physical touch between the objects and the plane screens are inconvenient to be used in large scale displays, since the size of the screen is beyond the reachable range of the operators. In this work, we propose a novel optical interaction simultaneously integrated with remote optical touch and direct touched fingerprint sensing which includes three layers of transparent optical films embedded with sub-wavelength gratings. The proposed films were validated by the rigorous coupled-wave theory analysis and the coupling efficiency of each layer was optimized via the finite difference time domain (FDTD) simulation. The proposed smart transparent optical interaction system is believed to improve the performance of touching technologies which have significant applications in large screen teaching, meeting and gaming.
Tutorial: Supervised Learning for Prevalence Estimation

Quantification is the task of estimating, given a set $$\sigma $$ of unlabelled items and a set of classes $$\mathcal {C}$$ , the relative frequency (or “prevalence”) $$p(c_{i})$$ of each class $$c_{i}\in \mathcal {C}$$ . Quantification is important in many disciplines (such as e.g., market research, political science, the social sciences, and epidemiology) which usually deal with aggregate (as opposed to individual) data. In these contexts, classifying individual unlabelled instances is usually not a primary goal, while estimating the prevalence of the classes of interest in the data is. Quantification may in principle be solved via classification, i.e., by classifying each item in $$\sigma $$ and counting, for all $$c_{i}\in \mathcal {C}$$ , how many such items have been labelled with $$c_{i}$$ . However, it has been shown in a multitude of works that this “classify and count” (CC) method yields suboptimal quantification accuracy, one of the reasons being that most classifiers are optimized for classification accuracy, and not for quantification accuracy. As a result, quantification has come to be no longer considered a mere byproduct of classification, and has evolved as a task of its own, devoted to designing methods and algorithms that deliver better prevalence estimates than CC. The goal of this tutorial is to introduce the main supervised learning techniques that have been proposed for solving quantification, the metrics used to evaluate them, and the most promising directions for further research.
High-resolution short-wave infrared hyperspectral characterization of alteration at the Sadiola Hill gold deposit, Mali, Western Africa

Sadiola Hill is an ~8 Moz gold deposit located in western Mali within a ca. 2200-2050 Ma tectonic window known as Kedougou-Kenieba inlier (KKI), exposing the Western African craton. The deposit is hosted in a metasedimentary package made up of impure carbonate rocks, wackes, and arenites intruded by three distinct igneous phases. A N-S-trending Sadiola shear zone, related to the regional Senegal-Mali shear zone, and NNE-trending third order fault splays acted as conduits for auriferous hydrothermal fluid flow. The deposit has undergone a complex poly-phase alteration history. Alteration assemblages related to gold mineralization consist of biotite-carbonate-quartz-sulphide. Other assemblages include calc-silicates, chlorite, white mica, scapolite, and tourmaline (Hein and Tshibubudze 2007; Cameron 2010; Masurel et al. 2017). Current research is aimed at characterising alteration at the mineral-scale, as well as assessment of cooling trend(s) and alteration footprint(s) with high-resolution short-wave infrared (SWIR) hyperspectral scanning. In addition to detailed mineralogical classification, changes in fluid chemical parameters are determined with variations in white mica, namely, the position of Al-OH bond in the SWIR range and white mica crystallinity. Furthermore, hydrothermal fluid chemistry is assessed with Fe2+ content in carbonate group minerals. Protolith control on alteration expression is also investigated.
A New F-PCASVD based Multivariate Denoising Algorithm for FID Transversal Data

The free induction decay (FID) transversal data determines the measurement accuracy of time-dependent geomagnetic fields, whereas the conservation of clean components and removal of noise cannot be easily achieved for this kind of data. Even though numerous techniques have been proven to be effective in improving the signal-to-noise ratio (SNR) by filtering out frequency bands, how to efficiently reduce noise is still a crucial issue. To end this, a new multivariate algorithm based on the fusion of principal component analysis (PCA) and singular value decomposition (SVD), namely F-PCASVD, was presented. This novel algorithm aims to reduce noise as well as cancel the interference of FID transversal data. Specifically, the F-PCASVD algorithm is able to obtain the dominant principal components of the FID and that of the noise floor by PCA, in which an optimal number of subspaces could be retained via a cumulative percent of variance criterion. Furthermore, the PCA was combined with an SVD filter whose singular values corresponding to the interferences were identified, and then the noise was suppressed by nulling the corresponding singular values to zero, which was able to achieve an optimum trade-off between the preservation of pure FID data and the denoising efficiency. Our proposed F- PCASVD algorithm was compared with the widely used filter methods via extensive experiments on synthetic and real FID transversal data. The results demonstrated that this method shows a significant improvement in noise suppression.
Scene Graph Generation via Convolutional Message Passing and Class-Aware Memory Embeddings

Detecting visual relationships between objects in an image still remains challenging, because the relationships are difficult to be modeled and the class imbalance problem tends to jeopardize the predictions. To alleviate these problems, we propose an end-to-end approach for scene graph generations. The proposed method employs the ResNet as the backbone network to extract the appearance features of the objects and relationships. An attention based graph convolutional network is exploited and modified to extract the contextual information. Language and geometric priors are also utilized and fused with the visual features to better describe the relationships. At last, a novel memory module is designed to alleviate the class imbalance problem. Experimental results demonstrate the validity of our model and our superiority compared to our baseline technique.
Path Planning of Escort Robot Based on Improved Quantum Particle Swarm Optimization

The path planning working space of resident community for escort robot is a complicated NP-Hard problem in the optimization research. The novel escort robot path planning model in resident community is presented, path describing as a grid graph of mathematical model. The Improved Quantum Particle Swarm Optimization algorithm is proposed for solving path planning model. The escort robot prototype is developed for the elderly assist in the community, which is also designed to walk with global path indoor and outdoor. The experiment platform is built on the developed escort robot and test environment. And then the experiment results are implemented for verifying effective and robust Improved Quantum Particle Swarm Optimization algorithm, as well as the algorithm can solve the real optimal escort robot path.
Improved Artificial Moment Method for Path Planning of Swarm Robots

An Artificial Moment Method with Decreasing Negative Effects and Keeping Sensing Obstacles (AMM-DNE-KSO) is proposed for the path planning of swarm robots in complex situations. First, a new algorithm is presented for the key companion of a robot close to its target. The algorithm will not make the robot change its key companion frequently and select the same robot as its key companion for a long duration. Second, two new algorithms are presented respectively for attractive points and attractive angles of robots. As such, a robot can decrease its negative effects on others and is easier to keep important obstacles within sensing range. Finally, the existing artificial moment motion controller is improved. Simulation results indicate that the method is helpful for robots to keep sensing important obstacles, and can decrease the negative effects in the system and yield better solutions in complex situations.
Scheduling Method Based on Backfill Strategy for Multiple DAGs in Cloud Computing

Multiple DAGs scheduling strategy is a critical factor affecting resource utilization and operating cost in the cloud computing. To solve the problem that multiple DAG scheduling cannot meet the resource utilization and reliability when multiple DAGs arrive at different time, the multiple DAGs scheduling problem can be transformed into a single DAG scheduling problem with limited resource available time period through multiple DAGs scheduling model based on backfill. On the basis of discussing the available time period description of resources and the sorting of task scheduling when the available time period is limited, the multiple DAGs scheduling strategy is proposed based on backfill. The experimental analysis shows that this strategy can effectively shorten the makespan and improve the resources utilization when multiple DAGs arrive at different time.
Classification of Human Actions in Videos with a Large-Scale Photonic Reservoir Computer

The identification of different types of human actions in videos is a major computer vision task, with capital applications in e.g. surveillance, control, and analysis. Deep learning achieved remarkable results, but remains hard to train in practice. Here, we propose a photonic reservoir computer for recognition of video-based human actions. Our experiment comprises off-the-shelf components and implements an easy-to-train neural network, scalable up to 16,384 nodes, and performing with a near state-of-the-art accuracy. Our findings pave the way towards photonic information processing systems for real-time video processing.
The structure of spaces of neural network functions

We analyse spaces of deep neural networks with a fixed architecture. We demonstrate that, when interpreted as a set of functions, spaces of neural networks exhibit many unfavourable properties: They are highly non-convex and not closed with respect to L<sup>p</sup>-norms, for 0 &lt; p &lt; &infin; and all commonly-used activation functions. They are not closed with respect to the L<sup>&infin;</sup>-norm for almost all practically-used activation functions; here, the (parametric) ReLU is the only exception. Finally, we show that the function that maps a family of neural network weights to the associated functional representation of a network is not inverse stable for every practically-used activation function.
Uncertain Confidence Network-Based Collaborative Information Retrieval Relevance Feedback Algorithm

Collaborative retrieval allows increasing the amount of relevant information found and sharing history with others. The collaborative retrieval can reduce the retrieval time performed by the users of the same profile. This chapter proposes a new relevance feedback algorithm to collaborative information retrieval based on a confidence network, which performs propagation relevance between annotations terms. The main contribution in this work is the extraction of relevant terms to reformulate the initial user query considering the annotations as an information source. The proposed model introduces the concept of necessity that allows determining the terms that have strong association relationships estimated to the measure of a confidence. Since the user is overwhelmed by a variety of contradictory annotations, another contribution consists of determining the relevant annotations for a given evidence source. The experimental study gives very encouraging results.
Expected Quantization Error Stability-Based Self-Organizing Growth Neural Network With Adaptive Output Network Scale

Self-organizing growth neural network (SOGNN) is an unsupervised clustering algorithm based on competitive learning, which can extract the distribution information and topology information of input data. However, the current SOGNN lacks effective and stable judgment indicators for the scale of network node growth, thereby failing to effectively evaluate and control the reasonable size of the output space. To this end, this paper proposes a judgment index, i.e., expected quantization error stability (EQES), to objectively judge the approximation degree of the output space to the input space. Based on the Growing Neural Gas (GNG) algorithm, an improved GNG algorithm, called GNG-EQES, is proposed, which introduces the EQES criterion to enable the GNG algorithm to generate an appropriate number of output network nodes autonomously without pre-determining the size of the output network. This not only improves the accuracy of feature extraction of the SOGNN, but also improves its adaptive ability and expands its application scope. The experiments in continuous input space and discrete input space have verified the validity and feasibility of the method proposed, and the method is applied to the construction of mobile robot environment topology map.
An Interval Discrete Wavelet Neural Network Model and Its Numerical Analysis

This paper proposes an interval discrete wavelet neural network (IDWNN) model with learning algorithm, which can be used for the system modeling with uncertainty. Compared with the conventional interval BP neural network (IBPNN), IDWNN has both the time-frequency localization characteristics of wavelet analysis and the powerful nonlinear approximation ability, which can provide some guidelines for the network structure construction. The algorithms of the forward calculation and the gradient descent-based back propagation training of IDWNN are derived. The numerical simulation results show the advantages of IDWNN in network structure selection and convergence speed.