Reviewerly/exHarmony huggingface dataset:
3D Human Body Reconstruction from a Single Image

In this paper, a generative adversarial network based on SMPL model is used to reconstruct three-dimensional (3D) human mesh from a single image. The generative adversarial network consists of two parts (generative network and adversarial network). The generative network is made up of a deep residual network and a multilayer perception network. The goal is to generate suitable SMPL model parameters and camera parameters which fit the 2D and 3D position annotation of human skeleton joints. The adversarial network judges whether the shape and pose parameters generated by the generative network match the real human body through a discriminator. The purpose is to guide the generative network output parameters that conform to the laws of human kinematics. A geometric prior of the 3D joint angle limitation of the human body is introduced into the network to further constrain the generated human pose. Experiments verify the effectiveness of the algorithm.
Pengembangan Skill Assesment Keamanan Jaringan

The development of Information and Communications Technology (ICT) also influences the education sector, ICT is encouraged to be able to support education as a medium that is quite effective to accommodate interaction between lecturers/teachers and students without the limits of space and time. The purpose of this research is to develop skills assessment using simulation software and to improve student understanding of each lecture material as well as student competency in the field of network security. The method used in this research is the experimental method. The results of the research are the development of learning media in the form of skills assessment and the prototype of a mobile-based learning application to measure learning achievement in the field of network security.
Research on the Innovative Teaching Method of Chorus Conducting in Colleges and Universities

With the continuous reform and innovation of modern university education, more and more educators realize the importance of innovation of practical teaching method. The most significant feature of chorus conducting teaching is its strong practicality. College teachers need to change their traditional teaching ideas in time, no longer blindly instill professional theoretical knowledge and skills into students, but ignore the cultivation of students' practical and innovative ability. Music teachers in colleges and universities should pay attention to improving their innovative consciousness and ability, and optimize and improve the teaching methods of chorus conducting according to the requirements of market talent training and students' actual learning situation, so as to fully stimulate students' interest in learning and promote the overall development of students' learning. This paper will further analyze and explore the innovation of chorus conducting teaching methods in colleges and universities, aiming to provide scientific reference for the same industry.
A Proactive Connection Setup Mechanism for Large Quantum Networks

Quantum networks use properties of quantum mechanics to transfer of data securely. Quantum entanglement is exploited in quantum networks to transfer information from one network node to another. Two nodes can transfer data using quantum teleportation when they share entangled pairs of qubits. Hence, efficient mechanism to distribute entanglement among quantum network nodes is a necessity. To the best of the knowledge of the authors, proposed entanglement distribution methods are primarily based on current state of network parameters, such as, fidelities, etc. None of them consider historical usage of entangled qubits in transferring data to set up entanglement between nodes. This paper presents a novel way to establish connection between two nodes using history of previous data transfers over quantum links and proactively distribute entanglement among intermediate nodes to speed up connection setup process. Results show, in a quantum network of 20 nodes, the proposed approach reduces connection failure rate from 25% for one connection to 5% for fifty connections.
HEDrone: Privacy-Preserving Proof-of-Alibi for Drone Compliance Based on Homomorphic Encryption

The proliferation of unmanned aerial Vehicles (UAVs) might be impeded because of rising concerns about citizen privacy in today's society. Though some protocols and standards like no-fly-zones (NFZs) have been proposed for drone compliance, they expose the operational logistics of the drone in terms of its flight data. AliDrone is a recent protocol that verifies NFZ violations by proof-of-alibi (PoA) via sharing of drone's trace with a trusted third party Auditor. The protocol leverages upon a trusted execution environment (TEE) to prevent malicious drone operators from forging geo-location information. In AliDrone, since the auditor learns the drone's flight trace, the privacy of the drone is compromised. HEDrone addresses this issue of drone privacy: it uses homomorphic encryption to enable PoA over encrypted traces.
Multi-robot Merged Formation Algorithm Simulation in Webots

This paper proposes a merged formation algorithm for multi-robot formation control, and completes the modeling of the multi-robot model and scene, and compiles the control program to realize the merged formation algorithm in Webots. The simulation experiment results prove the effectiveness of the merged formation algorithm realizing multi-robot formation and avoidance.
Design and Implementation of Cryptographic Instruction Set

With the development of embedded devices, power consumption and area have gradually become the bottleneck restricting chip design. Chip designers are more concerned about how to achieve higher performance under limited area and power consumption. This paper analyzes the cryptographic operations of all types of cryptographic algorithms including grouping, sequence, hashing, and public key. Then we extracts fine-grained common logic, designs corresponding cryptographic arithmetic units, follows the RISC-V instruction set architecture, and designs cryptographic extended instruction sets. Ariane, a low-power general-purpose cryptographic processor, was selected for transformation, and a low-power, high-performance cryptographic processor core was designed. The logic synthesis is performed under the 55nm CMOS process, and the results show that we have exchanged for a higher cryptographic operation speed with an area overhead of 1162718.09 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and a critical delay of 2.5 ns. And the experiments show that AES-128 takes about 290ns, and ECC in the 256-bit prime field takes about 0.224ms.
Gravity-Direction-Aware Joint Inter-Device Matching and Temporal Alignment between Camera and Wearable Sensors

To analyze human interaction behavior in a group or crowd, identification and device time synchronization are essential but time demanding to be performed manually. To automate the two processes jointly without any calibration steps nor auxiliary sensor, this paper presents an acceleration-correlation-based method for multi-person interaction scenarios where each target person wears an accelerometer and a camera is stationed in the scene. A critical issue is how to remove the time-varying gravity direction component from wearable device acceleration, which degrades the correlation of body acceleration between the device and video, yet is hard to estimate accurately. Our basic idea is to estimate the gravity direction component in the camera coordinate system, which can be obtained analytically, and to add it to the vision-based data to compensate the degraded correlation. We got high accuracy results for 4 person-device matching with only 40 to 60 frames (4 to 6 seconds). The average timing offset estimation is about 5 frames (0.5 seconds). Experimental results suggest it is useful for analyzing individual trajectories and group dynamics at low frequencies.
WITHDRAWN: Augmented reality in aerospace engineering real-time application of AR iOS applications

Increased realism technologies annotate real-world CG. They render the experience immersive by incorporating automated 3D models or other details regarding artefacts in their real-world radius. In the case of aerospace engineering, AR is a next-gen of designing and engineering components and structure for aircraft and spacecraft. So, next-gen of designing and engineering components and structure for aircraft and spacecraft is introduced.
Introduction to the second edition of Handbook for Scientometrics: Science and Technology Development Indicators

In December 2020, the second edition of the Handbook for Scientometrics: Science and Technology Development Indicators will be published. Over the six years since the release of the first edition, interest in scientometrics and its practical application has noticeably grown in Russia, and Russian centers for scientometrics have appeared. The introduction for the new edition is of interest itself. The Russian version is published with the kind permission of the copyright holder, Clarivate Analytics.
Negative confidence recommendation system avoids local solution based on user’s negative reactions

There are situations that one cannot get enough negative data for the machine learning. We show that if one can equip data with confidence values, one can successfully learn even there are only limited negative data. We numerically illustrate the behaviour of our proposed method on synthetic datasets firstly. The accuracy of the proposed method is 24.4% and 10.1% higher than two conventional methods on average. We further conducted movie recommendation experiment using MovieLens dataset to show the usefulness of the proposed method. Experimental results show that 33.6 % of previously-watched movies are reduced from the recommendation and genre-rating value is 4.67% higher in our proposed method.
‘Who is this body?’ – A qualitative user study on ‘The Machine to be Another’ as a virtual embodiment system

Like no other medium, virtual reality (VR) offers new possibilities to alter the perception of reality. These possibilities are mainly related to the feeling of presence in a virtual environment. With the VR performance ‘The Machine to be Another’ (TMTBA), we find an innovative embodiment system that enables a virtual body swap between two users. Hence, we conceptualise the performance as some kind of breaching experiment in order to alter self- and body perception. With the use of TMTBA and a qualitative research approach, we hope to gain a closer insight into the formation, alteration and persistence of body images. This challenges the phenomenological idea that our sense of bodily presence is essentially anchored in our physical or ‘objective’ body as we know it and seems to potentially expand our notion of what bodily presence can mean.
Fusing Low-Level Visual Features and High-Level Semantic Features for Breast Cancer Diagnosis in Digital Mammograms

Mammography is still the most effective procedure for early diagnosis of breast cancer. Computer-aided Diagnosis (CAD) systems can be very helpful in this direction and an assistance tool for radiologists to recognize abnormal and normal regions of interest in digital mammograms faster than traditional screening program. In this paper, we propose a new method for breast cancer recognition of all types of lesions in digital mammograms using multimodal features in a quaternionic representation. The proposed method consists of two steps: First, a feature extraction module utilizes two dimensional discrete transforms based on ART, Shapelets and Gabor filters to extract low-level visual descriptors of ROS. In addition, semantic mammogram information provided by radiologists, is encoded into a 16-bit length word used as high-level feature vector. All multimodal features are fused in a compact quaternionic representation prior to their presentation to the classification module in the second step. For the classification task, ROSs are diagnosed using trained Support-Vector-Machines (SVMs). The proposed method is evaluated on regions taken from the DDSM database. The proposed feature set achieves a mean accuracy of 87.22% (±2.678) and AUC = 0.98, that is 34% higher than using only visual descriptors, proving the significant information that semantic descriptors contain.
Probability-Based Time-Optimal Motion Planning of Multi-robots Along Specified Paths

In this paper, we study the time-optimal motion planning problem for multi-robots under their kinodynamic constraints along specified paths. Unlike previous approaches that coordinate their motions only at a road intersection and without considering noise influence, we for the first time use the probabilistic motion model caused by the noise when planning robot motion. By deriving the conflict probability, we design a scheme to sufficiently guarantee conflict probability below our expectation. In particular, we map the potential conflict to the infeasible rectangle region in robot's path-time coordinate plane, which enables us to apply velocity interval propagation to find a time-optimal motion for each robot. Then we integrate our approach into two popular multi-robot motion planners, i.e., conflict-based search and prioritized planning approach. Experimental results demonstrate that, conflict-based search needs more computation time to achieve less motion time than prioritized motion planner.
The Death of the Big Deal and Implications for Technical Services

In recent years, a growing number of libraries have canceled or unbundled their “Big Deal” journal subscriptions – those subscriptions that include a full package of digital journal titles for one discounted cost. This started as an affordability problem but has slowly morphed into a challenge from libraries demanding a new pricing structure that accommodates and spurs the growing open access movement.The change has caused a variety of challenges for technical services units including the increased need for user data, increasingly complicated workflows as they manage partial subscriptions, new interactions with consortia, and ongoing campus conversations. Whether the library is seeking to simply unbundle due to budget constraints, or push for new models such as “read and publish”, there is a tremendous impact on the work of technical services units.This chapter will explore the rationale and growth of the Big Deal, how it is breaking, four case studies on breaking Big Deals, a brief discussion of new transformative agreements, new challenges for consortia, and implications for technical services units moving forward.
Design of a Reusable Controller for Pushing Unliftable Objects Using a Multicopter

Research on aerial manipulation, or the manipulation of objects using multicopters, has been attracting attention in recent years. Commonly, aerial manipulation is performed with a manipulator mounted on a multicopter. However, these research projects focus on manipulating objects which can be grasped and lifted. This research focuses on manipulating unliftable objects using a multicopter. With the proposed method, the multicopter pushes a target object. To push an object automatically, a physical model for the pushing motion is constructed. An attitude control part of an existing flight controller is reused for reducing the effort of implementing the controller. The pushing motion is achieved by controlling the attitude using the physical model. In the evaluation experiments, the effectiveness of the proposed method is verified in simulation.
Visual Analysis of Textual Information on the Frequencies of Joint Use of Nouns and Adjectives

This paper presents the results of numerical experiments on the study of data volumes consisting of frequencies of joint use of adjectives and nouns. The volumes of data were obtained from samples from text collections in Rus-sian. The aim of the research is to analyze the cluster structure of the studied volume and semantic proximity of words in clusters and subclusters. The hypothesis was used that words with similar meaning should occur in approximately the same context. In this regard, in the space of features, they will be at a relatively close distance from each other, while differing words will be at a more distant distance from each other. Research is carried out using elastic maps, which are effective tools for visual analysis of multidimensional data. The construction of elastic maps and their extensions in the space of the first three principal components makes it possible to determine the cluster structure of the studied multidimensional data volumes. The analysis of the cluster structure for the considered volume of multidimensional data is carried out. The influence of transposition of the initial data array is considered.
Geological Interpretation of the Results of Factor Analysis of XRF- and XRD-Data on Carbonatites and Aluminosilicate Rocks of the Kontozero Alkaline Complex (Kola Peninsula, NW Russia)

Abstract We applied factor analysis (FA) for statistical comparison of XRF and XRD data on 198 carbonatite and aluminosilicate rock samples of the Kontozero Devonian paleovolcano. This method helps to identify main, secondary, and some accessory minerals, as well as to estimate the approximate contents of these minerals and to assess their contribution on the distribution of major and trace elements (Fomina et al., 2019). With the view of control purposes, qualitative and semiquantitative analysis of diffraction data were performed. Several dozen factors quickly yielded to mineralogical interpretation. For the studied samples, a functional relationship was established between the calculated mineral content and the score of the factor (FS) corresponding to the mineral. Thus, the FS can serve as a handy tool for assessing the contents of minerals. Thereby we visualise the distribution of minerals in the space of the studied complex. We also found out which mineral phases are ubiquitous, and which present only in certain parts of the section or occur sporadically. These data allowed us to make a mineralogical interpretation of the results of FA, performed solely for the geochemical variables. The main trends in the mineralogical and geochemical evolution of carbonatite and aluminosilicate rocks of the Kontozero Devonian paleovolcano were thus determined and statistically substantiated. All these results were obtained in a short time at the earliest stages of the research. A “blind” statistical method guarantees the independence of the results on a priori hypothesis of the researcher. The time-consuming classical techniques (e.g., petrography, mineralogical studies, and quantitative analysis of diffraction data) verified the geological model described by statistics.
QoS-Aware Power Management with Convolutional Neural Network

Seeking an autonomous optimal power allocation strategy is one of the most important research issues in machine-to-machine communication networks. Recently, deep learning-based methods provide a promising way to address this research issue. In this paper, we focus on exploring the optimal performance of the convolutional neural network (CNN) by making use of the grid-like topology of the channel information. Extensive comparative experiments have been conducted to compare the advantages of different deep learning methods in allocating power resources. It is shown that, under a similar training setting, on average the optimal value of objective function of CNN is about a quarter better than feedforward neural network (FNN). For several different optimization models, CNN acquired this desirable performance with only about 1 millisecond ultra computational time.
Integration of Accent Sandhi and Prosodic Features Estimation for Japanese Text-to-Speech Synthesis

In recent years, Japanese text-to-speech (TTS) synthesis methods have been actively researched. We need to estimate appropriate prosodic information for generating a high-quality synthetic speech. However, manual annotation is costly, and automatic annotation introduces estimation errors. This paper examines the integration of accent sandhi and prosodic feature estimation in the acoustic modeling for Japanese TTS to overcome the problems. The proposed method achieves total optimization of the F0 model by using the linguistic features from a dictionary. Objective and subjective evaluations confirmed that the cost of creating accent labels was reduced, and the accuracy of the prosodic feature estimation was improved.
Development of cluster models for municipal educational institutions of Tomsk region

Abstract This paper describes the cluster models construction for schools in the Tomsk region. K-means method is used to distribute objects between clusters using the multidimensional vector of variables (socio-economic status of students, qualification and age of teaching staff, students’ participation in academic competitions, students in difficult living conditions, etc.). The cluster models were constructed for urban schools (regional center), country and ungraded schools. The computer simulation using STATISTICA system was performed due to large amount of source data and complexity of the developed models. The analysis showed the relationship between the values of cluster variables and the results of Unified State Exams (USE).
Qu-Antri: Multi-tenant Architecture for Bandung Public Services to Reduce the Crowd

This paper explains how to build a prototype of a multi-tenant system which supports many aspects on the complete multi-tenant, single-tenant database, and single-tenant multi services, respectively. The design process of complete multi-tenant uses a unified modeling language (UML) which has case diagrams, activity diagram, class diagrams, and sequence diagrams. Singletenant databases are constructed by using cloud servers, SQL databases, and application programming interface (API). The single-tenant multi services which conduct the interface, are designed by using the mockup of application parts. At the end, an interactive testing evaluates all of multi-tenant systems. The result shows that our proposed prototype has a good performance in queuing system of public services.
A New Approach for Analyzing the Cycle Structure of a Class of LFSRs

De Bruijn sequences have many applications in communication system, coding theory, and cryptography due to their attractive characteristics, such as long period, being balanced, large linear complexity, and so forth. For any given nonsingular feedback shift registers, its cycle structure is key point for the construction of de Bruijn sequences by using the cycle joining algorithm, which is the most popular method to construct de Bruijn sequences. In this paper, a new approach for analyzing the cycle structure of a class of linear feedback shift registers is proposed, and we come to a conclusion how many cycles each of these linear feedback shift registers can produce. In addition, one example is presented to demonstrate the algorithm.
Analysis of Refactored Code Vulnerability and Assessment

Refactoring is the method of code factor's restructuring but it is not changed its external behavior. The reason for this method is to improve software quality. However, it may be an unintended impact of software security level changes. The system analyzes code refactoring with two perspectives such as access level principles and measure security index that informs the refactoring is more secure or not. Access level principles emphasize the target class and its members' access level changed. Security index measures readability and writability from code structure. By combining these two methods, the refactored code is precisely analyzed security level changes.