Reviewerly/exHarmony huggingface dataset:
Using 3D seismic exploration to detect ground fissure

As a kind of supergene geological phenomenon, ground fissure has brought great inconvenience to human life. In addition, it also has a close relationship with earthquake. However, it is very difficult to ascertain the extension depth of ground fissure since its concealment and uncertainty. In this paper, 3D seismic exploration is used to detect ground fissure in Shanxi Province of China. Specific parameters for seismic data acquisition, processing and interpretation are analysed. Firstly, seismic data acquisition method and its corresponding parameters are discussed. Small dose explosive sources and high frequency geophones are used. Small trace interval and appropriate fold are also adopted. Secondly, seismic data processing is processed from shot record to seismic profile. Multi-domain loop iteration de-noising is used to get high signal-to-noise ratio data. Accurate near surface model, interactive iteration and residual static correction are used to eliminate the impact of low velocity zone and the static correction problem. Large common middle point bin and small velocity analysis interval are used for high accuracy velocity spectrum analysis. The mute parameter of stretching distortion and the migration aperture are researched for shallow ground fissure detection. Thirdly, seismic data interpretation is processed to get ground fissure distribution. Fault enhanced filter is used to improve the signal-to-noise ratio effectively and the chimney cube is used to identify ground fissure automatically. Thus, the specific 3D seismic exploration method used in this paper is suitable for ground fissure detection. Cited as : Shi, S., Liu, Z., Feng, J., Feng, G., Li, M. Using 3D seismic exploration to detect ground fissure. Advances in Geo-Energy Research, 2020, 4(1): 13-19, doi: 10.26804/ager.2020.01.02
Implementation of modulation classifier over software defined radio

Blind signal modulation recognition is an essential block for designing a cognitive radio. Different algorithms are developed in the literature, but few are given with detailed implementation. This study proposes a software-defined radio based implementation of blind signal modulation recogniser (BSMR) on field-programmable gate array (FPGA), which works without any prior knowledge of the received signal. The algorithm estimates carrier frequency offset, symbol rate, symbol timing offset, and corrects the signal for these offsets to extract constellation points. It uses clustering structure formed by constellation signature in I/Q plane to detect the modulation for different orders of ASK, PSK, and QAM. The proposed algorithm is deployed on FPGA, using LabVIEW, for a reliable and reconfigurable platform. The algorithm is optimised to use minimum hardware resources and facilitate future up-gradation. The system developed by implementing the algorithm on NI-FlexRIO-7975 FPGA module with NI-5791 adapter detects modulation type in real time without any training. Signals for testing are generated using NI-PXIe-5673 (RF transmitter), and BSMR identifies the modulation type in 81.451 ms under additive white Gaussian noise channel.
A stochastic model and social optimization of a blockchain system based on a general limited batch service queue

<p style='text-indent:20px;'>Blockchain is well known as a database technology supporting digital currencies such as Bitcoin, Ether and Ripple. For the purpose of maximizing the overall revenue of the blockchain system, we propose a pricing policy to impose on transactions. Regarding the mining process as a vacation, and the block-verification process as a service, we establish a type of non-exhaustive queueing model with a limited batch service and a possible zero-transaction service. By selecting the beginning instant of a block-verification process as a Markov point and using the method of a generating function, we obtain the stationary probability distribution for the number of transactions in the system at the Markov points and analyze the elapsed time for the mining cycle. Based on the model analysis results, we derive the average latency of transactions and demonstrate how the average latency of transactions changes in relation to the arrival rate of transactions. With a reward-cost structure, we construct an individual benefit function and a social benefit function. By improving the Grasshopper Optimization Algorithm (GOA), we search for the Nash equilibrium and the socially optimal arrival rates of transactions. Numerical results show that the Nash equilibrium arrival rate of transactions is always higher than the socially optimal arrival rate of transactions for a given mining parameter and a specific block capacity. For this, we propose a pricing policy that forces the transactions to accept the socially optimal arrival rate and maximize the overall revenue of the blockchain system, including all transactions and miners.
Artificial Intelligence Knowledge Graph for Dynamic Networks: An Incremental Partition Algorithm

The quick and intelligent requests and answers in artificial intelligence (AI) are inseparable from intelligent data. Knowledge graph makes data more intelligent by establishing association among data, which provides convenience for intelligent search, reasoning and analysis of data. Resource Description Framework (RDF) is an effective data representation model of knowledge graph. This paper takes RDF as the research object and proposes an incremental partition method of intelligent data (IPID) to realize the distributed storage of large-scale AI data. First, IPID gives a mixed object function integrating edge cut and load balancing. Second, IPID devises the initial and incremental partitioning algorithms of RDF. The initial partition divides the original RDF graph into kernel vertices, boundary vertices and free vertices. The boundary and freedom nodes select the kernel vertex with the maximum gain of object function to form a sub-partition. And the incremental partition is in charge of the selection of sub-partition of new and deleted data by the object function. Meanwhile, the incremental partition algorithm would also execute a dynamic adjustment strategy at a certain time interval according to the balance and tightness of sub-partition to satisfy the partitioning object. Finally, IPID is tested on the knowledge graph datasets. The experimental results show that the object function guarantees the quality of knowledge graph partition in edge cut and load balancing, and effectively realizes the incremental partition.
Enhancing underwater images using piecewise linear smoothing gradient guided filter

Poor visibility owing towards illumination absorption and scattering is challenging for processing undersea descriptions.However, for enhancing true scene from such degradation is more important.Unfortunately, existing methods cause gradient reversal artifact particularly near boundaries.To get better insight of undersea imagery, we project a piecewise linear smoothing Gradient Guided Filter (P-GGF) technique is to defeat the difficulties caused by conventional schemes, hence produce sharper boundaries based on GGF and smoothed output based on piecewise linear model.The projected technique mainly functional for smoothing, flash and feathering.Tentative results prove that the resultant algorithm can produce imagery with improved ocular excellence than existing methods.
PANDA: Prototypical Unsupervised Domain Adaptation.

Previous adversarial domain alignment methods for unsupervised domain adaptation (UDA) pursue conditional domain alignment via intermediate pseudo labels. However, these pseudo labels are generated by independent instances without considering the global data structure and tend to be noisy, making them unreliable for adversarial domain adaptation. Compared with pseudo labels, prototypes are more reliable to represent the data structure resistant to the domain shift since they are summarized over all the relevant instances. In this work, we attempt to calibrate the noisy pseudo labels with prototypes. Specifically, we first obtain a reliable prototypical representation for each instance by multiplying the soft instance predictions with the global prototypes. Based on the prototypical representation, we propose a novel Prototypical Adversarial Learning (PAL) scheme and exploit it to align both feature representations and intermediate prototypes across domains. Besides, with the intermediate prototypes as a proxy, we further minimize the intra-class variance in the target domain to adaptively improve the pseudo labels. Integrating the three objectives, we develop an unified framework termed PrototypicAl uNsupervised Domain Adaptation (PANDA) for UDA. Experiments show that PANDA achieves state-of-the-art or competitive results on multiple UDA benchmarks including both object recognition and semantic segmentation tasks.
Using Artificial Intelligence Approaches for Image Steganography: A Review

Security of confidential information has been always an important issue. It has always been an interesting topic for researchers to develop secure techniques to send and receive data without revealing it to anyone rather than the receiver. Researchers are working every day to achieve a high level of security and consistency for their data during its transmission over the network. Cryptography is a way used to encrypt the data during its transmission so that when there is any attack, the message seen is not the real message, as it is encrypted. On the other hand, Steganography is a method of hiding secret messages in a cover object (image, audio, video, text) while communication takes place between sender and receiver. Steganography is often used with cryptography so that the information is doubly protected; First, it is encrypted and then hidden so that the attacker has to first detect the presence of information that is already hidden (a difficult task) and then decrypt it. In this paper, we will discuss different artificial intelligence techniques used to achieve maximum information hidden and high quality of image as the low quality is a sign of distortion which means that there is something hidden.
The Usability Evaluation of Academic Progress Information System (SIsKA-NG)

Limited functionalities of the Academic Progress Information System (SIsKA) has direct consequences on the quality of thesis management service at the authors' magister study program of the authors' university.This research focused on the significant improvement from the previous User Experience Questionnaire (UEQ) result of SIsKA.That significant improvement was based on the recommendations from the previous Heuristic Evaluation (HE) and two additional usability evaluation methods.All of those improvements have been applied to the new Academic Progress Information System that was called SIsKA Next Generation (SIsKA-NG).Overall, there are three usability evaluation methods were used in this research, namely the Performance Measurement (PM), the Retrospective Think Aloud (RTA), and the UEQ.The PM through the effectiveness analysis and the efficiency analysis each provided 11 and 16 recommendations, while The RTA provided 39 recommendations.The UEQ provided the assessment on SIsKA-NG with level excellent on aspect of attractiveness, efficiency, dependability, stimulation, and novelty; and with level good on aspect of perspicuity.It was considered as a significant improvement of the UEQ result of SIsKA-NG compared to the previous UEQ result of SIsKA because of the better assessment on four aspects with level excellent on SIsKA-NG, namely attractiveness, efficiency, dependability, and novelty.Same assessments are on two aspects, namely perspicuity and stimulation.SIsKA-NG still need to be improved based on the aspect of perspicuity, additional recommendations from the conducted Focus Group Discussion, and procedural White Box testing for code evaluation.
Energy-Efficient Resource Allocation in Data Centers Using a Hybrid Evolutionary Algorithm

Energy efficient scheduling in a cloud data center aims at effectively utilizing available resources and improving the energy utilization. Resource scheduling is NP-hard and often requires substantial computational resources. This chapter presents an interactive PSO-GA algorithm that performs parallel processing of particle swarm optimization (PSO) and genetic algorithm (GA) using multi-threading and shared memory for information exchange to enhance convergence time and global exploration. With the proposed approach, this chapter demonstrates an efficient virtual machine placement in a data center that greatly reduces the total energy consumption up to 34% and the convergence time up to 50% compared to PSO, GA, and modified best-fit decreasing approaches. Further, this method achieves average parallel efficiency of 90% and a speed up of 1.5. This chapter also evaluates effectiveness of the proposed algorithm on benchmark optimization test problems over the state-of-the art algorithms.
Average Neural Face Embeddings for Gender Recognition

In recent years, with the rise of artificial intelligence and deep learning, facial recognition technologies have been developed that operate with high accuracy even in adverse conditions.However, extracting demographic information such as gender, age and race from facial features has been a hot research area.In this study, a new Average Neural Face Embeddings (ANFE) method that uses facial vectors of people for gender recognition is presented.Instead of training deep neural network from scratch, a simple, fast and effective solution has been developed that performs a distance calculation between the average gender vectors and the person's face vector.The method proposed as a result of the study carried out provided a high and successful recognition performance with with 96.47% of the males and 99.92% of the females.
Detection of shot boundaries and extraction of key frames for video retrieval

The prerequisite steps for summarizing, retrieval of video are detection of shot transitions and extraction of key frames. We hypothesized an advanced, ultra-modern Scale Invariant Feature Transform (SIFT). This SIFT method captures statistical modifications of various shot transitions, next the ke y frames or representative frames are extracted from those segmented shot with the calculation of entropy for each frame in the shot. We can amplify the performance of over proposed system by removing the repeated representative frames using the technique called edge matching rate. This intensified algorithm is applied to variable classes of videos to perceive shot transitions and getting of the key frame. Thus, the proposed algorithm proves its efficacy and accuracy in exhibiting its experimental results.
Reversion of weak-measured quantum entanglement state*

We theoretically study the reversible process of quantum entanglement state by means of weak measurement and corresponding reversible operation. We present a protocol of the reversion operation in two bodies based on the theory of reversion of single photon and then expend it in quantum communication channels. The theoretical results demonstrate that the protocol does not break the information transmission after a weak measurement and a reversible measurement with the subsequent process in the transmission path. It can reverse the perturbed entanglement intensity evolution to its original state. Under the condition of different weak measurement intensity the protocol can reverse the perturbed quantum entanglement system perfectly. In the process we can get the classical information described by information gain from the quantum system through weak measurement operation. On the other hand, in order to realize complete reversibility, the classical information of the quantum entanglement system must obey a limited range we present in this paper in the reverse process.
Analysis of Image Steganography Performance Check Using Bit Selection

Hidden communication is important since ancient times to provide the secrecy of information which may be for personal work or for professional work. In the ancient times cryptography was the main method for secret communication. In cryptography message is encrypted in some way but the existence of message is known to the third party which gives the possibility of hacking of the message by third party easily. Here comes the use of steganography which overcomes the drawback of cryptography. In steganography the existence of message is not known to the third party that makes it easy for two parties to communicate in an imperceptible manner. There are various techniques using steganography. Following work has been done using bit steganography in which bits of one image have been used which is called cover image to hide message image. This technique has been used on every bit of each pixel of cover image starting from LSB to MSB and then comparison of results has been done on the basis of mean square error ad peak signal to noise ratio.
The Immersive Power of Augmented Reality

Augmented reality is one of the technologies that have received great attention and interest in recent years. In a world where the boundaries between the real and the virtual are blurring, this immersive technology enriches and complements the reality with digital content and allows people to gain a complete and real sense of the objects around them. Currently, the applications of augmented reality go beyond the domains of games and entertainment and are aimed at taking the full advantage of the technology in areas such as medicine, architecture, business, tourism, education and more. The current paper presents the essence of technology and types of augmented reality systems. The basic approaches for creating augmented reality applications are discussed. Specific examples of the application of the technology in the field of education are given-an augmented book and augmented reality educational projects, whose purpose is to make learning an interesting, immersive, engaging and motivating process.
Bounds for nonadiabatic transitions

We discuss bounds for nonadiabatic transitions from the viewpoints of the adiabatic perturbation theory and the quantum speed limit. We show that the amount of nonadiabatic transitions from the $n\mathrm{th}$ level to the $m\mathrm{th}$ level is bounded by a function of the quantum geometric tensor for the $m\mathrm{th}$ level. We analyze this bound from the viewpoint of the adiabatic perturbation theory. In addition, this bound and the viewpoint of the quantum speed limit suggest a nontrivial relationship between the dynamical transformation and the adiabatic transformation. We also derive a universal bound for any nonadiabatic transition. This bound is written in terms of the counterdiabatic Hamiltonian.
Distributed Online Course for Educator Training

The general digitalization of contemporary society leads to a change in the use of learning technology in engineering education. The aim of this article is to present the advantages of the corporate online course for faculty members’ advanced training developed at Tomsk Polytechnic University. The general goal of this course is to supply engineering teachers with the new tools for their professional activities. Based on the analysis the different types of Massive Open Online Courses (MOOCs), the authors dwell on the pedagogical aspects of the new course, such as openness, transparency, internal communication, and external deposition of the educational products obtained by participants in the courses. The proposed model of online course is distinguished by a distributed and problem-based character coupled with high level of the transparency and internal openness. Within the framework of the course the participants share Google Documents and Blogs with each other and with the tutor performing peer-to-peer communication. It is shown that proposed model of educator training leads to more effective results than other commonly used MOOCs. After the course is finished, all educational products achieved by participants are archived and become accessible for external users. Their rights are provided by Creative Commons Attribution. As a result of the implementation of the course, the University’s resource of open access educational and didactic materials is being maintained and developed.
Authenticated Key Agreement Protocol Based on Provable Secure Cryptographic Functions

The vulnerable part of communications between user and server is the poor authentication level at the user's side.For example, in e-banking systems for user authentication are used passwords that can be lost or swindled by a person maliciously impersonating bank.To increase the security of e-banking system users should be supplied by the elements of public key infrastructure (PKI) but not necessary to the extent of standard requirements which are too complicated for ordinary users.In this paper, we propose two versions of authenticated key agreement protocol (AKAP) which can be simply realized on the user's side.AKAP is a collection of cryptographic functions having provable security properties.It is proved that AKAP1 is secure against active adversary under discrete logarithm assumption when formulated certain conditions hold.AKAP2 provides user's anonymity against eavesdropping adversary.The partial security of AKAP2 is investigated which relies on the security of asymmetric encryption function.
Incremental Evolution and Development of Deep Artificial Neural Networks

NeuroEvolution (NE) methods are known for applying Evolutionary Computation to the optimisation of Artificial Neural Networks (ANNs). Despite aiding non-expert users to design and train ANNs, the vast majority of NE approaches disregard the knowledge that is gathered when solving other tasks, i.e., evolution starts from scratch for each problem, ultimately delaying the evolutionary process. To overcome this drawback, we extend Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) to incremental development. We hypothesise that by transferring the knowledge gained from previous tasks we can attain superior results and speedup evolution. The results show that the average performance of the models generated by incremental development is statistically superior to the non-incremental average performance. In case the number of evaluations performed by incremental development is smaller than the performed by non-incremental development the attained results are similar in performance, which indicates that incremental development speeds up evolution. Lastly, the models generated using incremental development generalise better, and thus, without further evolution, report a superior performance on unseen problems.