Reviewerly/exHarmony huggingface dataset:
Blockchain Electronic Voting System for Preventing One Vote and Multiple Investment

An anonymous online voting system based on blockchain is proposed for the problem that the voting process details are not public, citizens do not trust, and the results cannot be verified. Using the characteristics of blockchain cryptography and distributed networks, it is possible to perfectly solve the problem of voting data security and server single point of failure. Recording by the id of the account for data verification in the future. The pricing mechanism of the Ethereum system can effectively prevent the situation of one vote and more investment, and truly realizes the openness and transparency of the voting process technically, and realizes the anonymity of the voter identity to a certain extent. In the long term, it is planned to study IPFS distributed storage in depth and distribute the voting data of the uplink to different nodes to achieve higher security and transparency.
A novel 4 camera multi-stereo tracking system for application in surgical navigation systems

Abstract In this paper, we present a novel 4 camera stereo system for application as optical tracking component in navigation systems in computer-assisted surgery. This shall replace a common stereo camera system in several applications. The objective is to provide a tracking component consisting of four single industrial cameras. The system can be built up flexibly in the operating room e. g. at the operating room lamp. The concept is characterized by independent, arbitrary camera mounting poses and demands easy on-site calibration procedures of the camera setup. Following a short introduction describing the environment, motivation and advantages of the new camera system, a simulation of the camera setup and arrangement is depicted in Section 2. From this, we gather important information and parameters for the hardware setup, which is described in Section 3. Section 4 includes the calibration of the cameras. Here, we illustrate the background of camera model and applied calibration procedures, a comparison of calibration results obtained with different calibration programs and a new concept for fast and easy extrinsic calibration.
Multi Keyword Search on Encrypted Text Without Decryption

Various organizations use plain text to store data related to day-to-day computations. Data is stored in the form of plain text documents without any structure and specifications. Retrieval and searching from structured data will be easier with various existing database systems. Querying and searching on unstructured content is difficult. In general, searching on unstructured content can be implemented using a similarity between input keywords and documents. Organizations are moving towards cloud to store the data because of high availability, lower maintenance cost, reliability, and portability. In a cloud system, sensitive data like personal records are to be protected to avoid malicious access from intruders. But searching data from encrypted content is difficult. In this paper, we are proposing a scheme called Multi Keyword Search on Encrypted text (MKSE) which enables searching on encrypted unstructured text without decryption in the cloud using Cosine Similarity. To store the documents, we are using CryptDB database where documents are stored in encrypted form. Thus the multi-keyword search is done on encrypted data in the cloud using cryptDB for providing data security.
Block-Wise Authentication and Recovery Scheme for Medical Images Focusing on Content Complexity

Digital images are used to transfer most critical data in areas like medical, research, business, military, etc. The images transfer takes place over an unsecured Internet network. Therefore, there is a need for reliable security and protection for these sensitive images. Medical images play an important role in the field of Telemedicine and Tele surgery. Thus, before making any diagnostic decisions and treatments, the authenticity and the integrity of the received medical images need to be verified to avoid misdiagnosis. This paper proposes a block-wise and blind fragile watermarking mechanism for medical image authentication and recovery. By eliminating embedded insignificant data and considering different content complexity for each block during feature extraction and recovery, the capacity of data embedding without loss of quality is increased. This new embedding watermark method can embed a copy of the compressed image inside itself as a watermark to increase the recovered image quality. In our proposed hybrid scheme, the block features are utilized to improve the efficiency of data concealing for authentication and reduce tampering. Therefore, the scheme can achieve better results in terms of the recovered image quality and greater tampering protection, compared with the current schemes.
Trabecular Texture Analysis using Morpho-Clinical Features and Bayes Classifiers

The objective of this paper is to analyze radiographic images of patients and to discriminate between them using nine morphological and clinical parameters. Four models were constructed and trained using three Bayes classifiers: Bayesian logistic regression, Byes Net, and Naive Bayes. The purpose was to find the best configuration combining selected features and the best classifier providing the highest rate of classification. The validation was done using the '10-fold cross-validation' technique. A total of 100 images were collected from patients, divided into two groups, 50 healthy subjects, and 50 osteoporotic patients. The results obtained reveal that the selected features model combined with the Bayesian logistic regression classifier provided accurate discrimination between the two populations, with ACC = 87% demonstrating the performance of this configuration.
RLWE Commitment-Based Linkable Ring Signature Scheme and Its Application in Blockchain

Aiming at the problems of large key size and low computation efficiency of linkable ring signature (LRS) schemes from lattice, we construct a LRS scheme based on the RLWE (learning with errors from ring) commitment scheme and further apply the proposed LRS scheme to blockchain to construct an anonymous post-quantum cryptocurrency model. Concretely, we first prove through setting parameters reasonably, we can make a RLWE-based commitment scheme to have homomorphism; Then use the RLWE-based homomorphic commitment scheme, combined with the Σ-protocol and Fiat-Shamir heuristic to construct a LRS scheme; Finally, by combining the proposed LRS scheme with blockchain we present an anonymous post-quantum cryptocurrency model. Analysis shows that compared with the previous LRS schemes, since the proposed LRS scheme is constructed based on the intractability of RLWE problem which can be reduced to SVP (shortest vector problem) on lattice, it can both resist the quantum computer attacks and have smaller key size, signature size and higher computational efficiency. The proposed cryptocurrency model uses the proposed LRS scheme to ensure the sender’s anonymity and the one-time stealth address to guarantee the recipient’s anonymity, which can both protect users’ identities and resist quantum attacks.
StarFont

Font design has become an essential part of multimedia. It has the ability to convey the mood and intention of the designer. However, creating a new font for Chinese characters takes a lot of effort because the language contains over 20 thousand character with complex morphological structures. Current font completion methods have many disadvantages. To address this problem, we propose StarFont, a font completion system that can automatically complete a whole font using a few-shot learning method. Our model takes several examples of a new font, learns the design style and applies it to the remaining characters to complete the font. Unlike existing models proposed for font generation, we treat each character not the font as a class and abandon reconstruction loss because the font's ground truth is easier to obtain. Moreover, we combine multiple input images to generate new images, while existing methods use a one-to-one approach. Compared to other deep learning-based font completion methods, our model requires fewer examples of the new font and generates better results. Both qualitative and quantitative methods show that our method is more advanced.
A Blockchain-Based Data-Sharing Architecture

In the new global economy, data has become an important resource. Sharing data can bring their value into full play. Before the process of data sharing, due to the limitations of personal storage resources, data owners usually upload data to a public cloud server for storage. However, the method of storing data to the cloud cannot guarantee that stored data has not be accessed when its owner is unknown, and such unauthorized access behavior often results in a loss of interest to the data owner. To solve this problem, in this paper, a blockchain-based data sharing architecture is proposed. The architecture uses the way to store meta-data in isolation from raw data to avoid the possibility of user data being stolen and to ensure information security in the process of data sharing. At the same time, it describes a blockchain network model for data sharing to ensure that there is no association between unrelated entities. On the other hand, it also provides a way for data owners to distribute fine-grained access control permissions based on specific scenarios.
A Route Recommendation Method Based on Personal Preferences by Monte-Carlo Tree Search

In this paper, we propose a route recommendation method, called P-UCT method, considering individual user's preferences utilizing Monte-Carlo tree search. In the proposed method, we firstly extract route features based on the route recommendation history of every user and construct a route evaluator based on Support Vector Machine (SVM). After that, the method generates a random route from a start point to an end point by Monte-Carlo tree search. The route evaluator determines how well every generated route matches the user's preferences. By repeating the evaluation, the method obtains the route, which must be closest to the user's preferences. Experimental results demonstrate that the proposed method outperforms the existing method from the viewpoint of the average evaluation scores.
Evaluation of Augmented Reality Occlusion Scheme Based on Analytic Hierarchy Process

In this paper, the metrics of virtual and real occlusion effects are proposed for different augmented reality scenarios, and seven occlusion schemes are proposed correspondingly. In order to evaluate the adaptability of various virtual and real occlusion schemes in different scenarios, the augmented reality scenes with label information are used to construct the evaluation model of the virtual and real occlusion schemes by analytic hierarchy process. The two-step experimental calculation is carried out to find the most suitable occlusion scheme in the scene.
Greedy Universal Cycle Constructions for Weak Orders

A weak order is a way to rank n objects where ties are allowed. In this paper, we extend the prefer-larger and the prefer-opposite algorithms for de Bruijn sequences to provide the first known greedy universal cycle constructions for weak orders.
A Soft, Distributed, Digital 3-axis Skin Sensor Employing a Hybrid Permanent-Adjustable Magnetic Field

In this letter, we present a new iteration of a prototype sensor module that can be used as a skin sensor for robots. The sensor uses a hybrid arrangement that consists in a small permanent magnet and an electromagnet working together above a 3-axis magnetic sensor. Both layers are separated by a flexible material that provides compliance to the sensor module. The sensitivity of the sensor can be adjusted by changing the flow of current on the electromagnet, while the permanent magnet increases the intensity of the overall magnetic field to optimize the utilization of the bandwidth of the sensor. Each sensor module includes a microcontroller with digital output that offers multimodal and distributed sensing capabilities. Problem areas of the previous version are indicated, the magnet array idea is presented and tested, and finally experiments are performed on different shapes of a flexible material to be used as middle layer.
The Library-Bibliographical Classification (LBC): current status and prospects

The chief editor of the LBC (from February 1997) talks about how he turned out to be in this position, what tasks he set for himself, what he managed to accomplish. LBC was highly appreciated by the world classification community. As the National Classification System of Russia, the LBC is used in the vast majority of libraries in the country – and not only in scientific or public, but also in many other libraries. By the end of the second decade of the XXI century the country has a new system, completely de-ideologized, updated, significantly different from the Soviet LBC. Published full, medium and abridged schedules, and specialized schedules version for school and children libraries. Complex and systematic scientific and methodological work were organized. Established a permanent consulting activity. The functions of the LBC have changed, but many librarians do not understand this, despite the constant explanatory work. Today the tasks of organizing an open library space and systematic arrangement of book collections provided to readers are put forward in the first place. The tasks of deep search in catalogs were relegated to the background due to the unwillingness of automated systems programmers to create an effective classification search. The quality of OPACs remains very low. Existing tools hardly provide a verbal search. Experts know that only a search using modern classification systems can be more effective – complete and deep. The author examines in detail the problems with which a small team of the LBC Development Research Center constantly meets. First of all, this is a misunderstanding of the importance of working with the National Classification System on the part of management bodies. The second most important problem is personnel training. If these problems are not solved, the country will not be able to maintain a unique system. In the final part of the article the author speaks about the medium-term program of activities in the field of LBC.
Vehicle Re-Identification: Logistic Triplet Embedding Regularized by Label Smoothing

The explosive increasing of vehicles cause amount of traffic problems. Although vehicle re-identification (Re-ID) can help to acquire and manage vehicles, some intrinsic difficulties hinder the application of vehicle Re-ID. For example, vehicles have little inter-instance discrepancy due to their rigid structures and finite models. To address this problem, in this paper, a logistic triplet loss is proposed to fuse a label-smoothing cross entropy to extract fine-grained feature embeddings. Via exploring deeper into the inter-instance variances, the novel loss combines advantages of classification and metric learning, and reveals more stable performance than popular triplet loss. The experimental results on public datasets demonstrate the effectiveness of the proposed loss compared with state-of-the-art approaches.
Faster Multibeam Sonar Data Cleaning: Evaluation of Editing 3D Point Clouds using Immersive VR

Remote sensing technologies routinely generate point cloud datasets with billions of points. While automatic data cleaning algorithms exist, safety-critical applications (such as waterway surveys) still require that data be processed and verified by a human. This presents a significant bottleneck in the pipeline from surveys into navigational maps. The recent proliferation of low-cost, high-quality virtual reality systems presents an opportunity to explore how these technologies might be integrated into the point cloud data processing pipeline. Prior research has shown that stereoscopic viewing, head-tracked perspective, and bimanual interactions can lead to faster 3D task completion times and lower errors compared to traditional monoscopic, mouse-and-keyboard desktop systems. In this paper, we present a human factors study that compares 3D point cloud editing performance between a traditional interface and type types of immersive virtual reality interfaces. Our results showed that for complex datasets, the immersive interfaces generally led to faster task completion times than when using the desktop interface. Participants also reported a strong subjective preference for the immersive interface.
Human Pose Estimation via Multi-Scale Intermediate Supervision Convolution Network

Human pose estimation is a technique for locating the key points of a human in the picture and video. It can be used in the fields of human-computer interaction, motion recognition, Character behavior analysis and so on. At present, the supervision information is a series of single-scale heat map without matching the multi-scale key points of ground truth while training the CNN model. This type of supervision makes it more likely that the predicted key points will deviate from the real position. To improve the prediction accuracy, this paper proposes a novel model named human pose estimation via multi-scale intermediate supervision convolution network. Heatmaps are generated on three scale which are determined by the standard deviation of 2D Gaussian distribution. Residual network model is composed of three stage using ResNet50 as the backbone network. Each stage includes a ResNet50 and three deconvolution layers. The output from ResNet50 in three stages corresponds to the heat maps annotation of large, medium and small sizes respectively, and the intermediate supervision is realized twice in the output of the first and second stages. In the test phase, the output from the last stage is used for calculating the final key point's coordinates by non-maximum suppression. To demonstrate effectiveness of our network, two benchmark datasets are used for training and testing: the key point detection subset of COCO dataset and MPII Human Pose dataset. The test result of PCK@0.1 reached 37.2% on the MPII validation dataset, which is 2.1% higher than other methods. The results of the mAP test on the COCO validation dataset reached 75.5%, an increase of 1.2% compared with other methods. The results indicate that the multi-scale relay supervised convolutional network model proposed in this paper can reduce the influence of the non-correspondence between the size of key points and the size of heatmap ground truth in human pose estimation, thus improving the accuracy and achieving better performance when the evaluation criteria are stricter.
A Novel Ensemble Approach to Multi-label Classification for Electric Power Fault Diagnosis

State Grid Corporation of China has accumulated a large number of electric power fault textual data based on ICT customer services. Effectively classifying and analyzing these texts can provide important clues for resolving new faults, and therefore helps customer service staffs provide more accurate fault diagnosis solutions. Because an occurred fault is possibly related to multiple classification labels, it is challenging to effectively classify the faults. In this paper, we present an ensemble learning based multi-label classification approach to analyzing electric power fault text data. Firstly, the power fault report data is pre-processed by word segmentation and stop word removal according to the structure of fault data. Each of fault text is represented as a TF-IDF vector. Then, we combine Binary Relevance with the Gradient Boosting ensemble learning algorithm for multi-label classification of fault texts. At last, the related experiments were made, and the experimental results show that our method is better than the traditional approaches such as Binary Relevance based on Logistic Regression and ML-KNN for fault text classification.
Speech Encryption Based on 5D Hyperchaotic System for AMR-WB Codec

In this paper, we propose an hyperchaos-based speech encryption algorithm for AMR-WB Codec. The algorithm adopts a 5D hyperchaotic system, and the generated key stream is employed for confusion and diffusion processes. In order to strengthen the security of the cryptosystem, a chaotic shift keying mechanism is employed and keys are assigned for confusion and diffusion processes. Whereas the keys x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> are employed for the confusion of the frame's parameters using permutation, the keys y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> , z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> , w <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> and u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> are used for diffusion that is performed by the Xor operation with the parameters of the 4 codec's sub-frames. Simulation and statistical analysis through the Enhanced Modified Bark Spectral Distortion (EMBSD) and Mean Opinion Score (MOS) prove that this approach works well and confirm the efficiency of our presented schemes.
Mechanical Stabilization in Robotic Bionic Eyes with Gravity and Disturbance Compensations

The vibration of the robot during the movement brings various irregular motion disturbances to the robot's head, these vibration results into unstable images captures by the head-mounted vision system and therefor the vision system cannot accurately perceive the environment. This paper presents a mechanical stabilization method based on gravity and disturbance compensations, which considers and compensates the gravity influences of the mechanical structure and external disturbances. Finally, the current command of the joint motor is generated based on the compensations, which can quickly stabilize the image. We performed simulation and physical experiments to verify the performance of our proposed method.
Joystick Mapping in Virtual Reality Shooting Game

A method to use for mapping VR-Box joystick with shooting game input controls that include key mapping of every joystick key from the joystick with the game itself. Key mapping was observed and then the source code keys we're mapped with the joystick keys. The joystick holds major 4 buttons with a 360-degree moveable joystick with 2 supporting keys making them total of 6 keys. In this paper we will discuss mapping of Google Virtual Reality plugin with First Person Shooting plugin. The integration of joystick with VR-Box helped the gameplay even more controllable. The joystick keys are responsible for invoking key features of game as it can be connected via Bluetooth with the mobile headset to ensure long range wireless connectivity. The device converts the keys triggered from joystick to enable the features inside the game.