Reviewerly/exHarmony huggingface dataset:
The Token Economy: Assets for the Blockchain Era

The advent of technology and its advance have impacted the world in a radical form. The digital transformation is redefining markets, industries, and making new business, investment models possible and providing opportunities before unthinkable. This article is intended to talk on a technology of blockchain and a token economy in a digital age with robust databases and the dematerialization has proven a reinvention of the financial market that we currently know. In this research we present the blockchain technology and its growing adoption around the world, the current ecosystem of icos (initial currency offer) and stos (security token offer). The blockchain technology, digital currencies and a token economy enables a break from the financial market mainstream because such technologies make the process faster, less costly and allow international investment.
Agile Application for Innovation Projects in Science Organizations - Knowledge Gap and State of Art

The paper examines the process of innovation development in science organizations and its project management. As a methodological approach, the study focuses on Agile method as a technique for improving some old-school practices for innovation, product and project management. The research methodology goes through a systematic review of the literature of the Agile application in project management and innovation in scientific organizations to prove the knowledge gap. The results of the research are of interest to scientists from a wide range of fields and offer knowledge and insight for more effective project management for innovative projects in research organizations. In addition, the methodology applied in this study is based on a specially developed data analytics tool for word and context analysis (based on the principles of literature review) and might be re-used in many other studies which aim at identifying knowledge gap and state of art in interdisciplinary fields of study. The tool is Advanced Analytical Literature Review (AALR) built on Qlik software.
Classification of Cultural Heritage Mosque of Bangladesh Using CNN and Keras Model

Now-a-days convolutional neural network is widely used in categorizing cultural heritage and other images. Important image features can be easily extracted using this technique before classification. So, we can utter that convolutional neural network acts as a complete package. Again, keras is a neural network API which works as a package in R interface. Along with convolutional neural network, keras supports faster execution. To do this, a new dataset has been formed and this has to be passed through several layers of convolutional network for training and test purposes. Promising accuracy has been achieved as a result and it is considered as one of the best techniques for the restoration and preservation of cultural heritage mosques and other resources.
The effects of super-resolution on object detection performance in an aerial image

Image super-resolution (SR) has a positive effect on the problem of detecting objects on low resolution (LR) images. In this study, we train custom RCAN to create SR images from LR, and at the same time train Practice common object detection methods Faster RCNN, Cascade-RCNN, DetectoRS, Retina, SSD on both SR, LR datasets. Experimental results, proving that SRx2 significantly improves subject detection results of LRx2 images. And We make a comparison between the results of SR and HR.
Development of a Technological Solution for Automated Reintroducing of Mixed Lot Goods in the Trading Process

Today, the amount of orders made via internet shops is increasing continuously. From books, over clothes, up to technology and media, each consumer group can be served. Major enterprises such as Amazon, Zalando SE, etc. are offering their customers the possibility to receive a perfect shopping experience. Whereas during a personal shopping experience, the customer can inspect their products directly in the store, this is not possible when ordering via online marketplaces. Products, which are not sufficient to the customers attitude, like and taste, are returned to the seller. Not all of the returned goods can be sold again, as some are damaged or the value for reselling is too low. These products are packed on pallets and sold blind as mixed lot goods. Reintroducing these goods is currently still done manually. This paper is focusing on the development of a technological solution to reintroduce these mixed lot goods into the economic traffic in an automated way. To fulfill this task, a technological model has been developed. This model is using text processing aspects as well as Artificial Intelligence based algorithms such as Deep Learning and Big Data aspects, combined with simple digital workflows to reintroduce and digitalize the goods part by part. Applying the planned algorithms, the concept model is using a scanner device (e.g. a smartphone camera). Pictures of the products of the mixed lot goods, will be analyzed with neural networks and compared with similar goods in the internet, by using automated robots.
Quantum Communication Networks: Final Considerations and Use Cases

This chapter ends the book with final considerations on quantum communication networks, underlining how they can exceed classical telecommunications. Moreover, practical use cases of quantum communication networks (with their latest results) are surveyed, such as the quantum Internet of Things and quantum distributed synchronization.
Hopfield Neural Network based Stereo Matching Algorithm

Traditional feature-based stereo matching methods fail to obtain dense disparity map, and in this paper we directly treat each pixel point as a matching primitive, treating the stereo-matching problem as a combinatorial optimization problem of finding labels for each pixel point First, an energy function is introduced to represent the matching constraint cost function for the stereo matching. Then, based on the energy minimization algorithm, a three-dimensional matching algorithm based on a two-dimensional Hopfield neural network is proposed that considers the polar constraint, uniqueness constraint, similarity constraint, and smoothness constraint, mapping the variability directly to the neural state space and introducing these constraints into the energy function through the neural state. Finally, iterations are used to ensure that the energy function converges to a local minimum. In addition, constructing a Hopfield neural network on each epipolar line, based on the epipolar constraint, not only reduces the number of neurons but also increases the computational speed. The experimental construction was performed on Middlebury and the results showed good convergence of the energy function and good accuracy of the matching results.
Loch Prospector: Metadata Visualization for Lakes of Open Data

Data lakes are an emerging storage paradigm that promotes data availability over integration. A prime example are repositories of Open Data which show great promise for transparent data science. Due to the lack of proper integration, Data Lakes may not have a common consistent schema and traditional data management techniques fall short with these repositories. Much recent research has tried to address the new challenges associated with these data lakes. Researchers in this area are mainly interested in the structural proper-ties of the data for developing new algorithms, yet typical Open Data portals offer limited functionality in that respect and instead focus on data semantics. We propose Loch Prospector, a visualization to assist data management researchers in exploring and understanding the most crucial structural aspects of Open Data - in particular, metadata attributes - and the associated task abstraction for their work. Our visualization enables researchers to navigate the contents of data lakes effectively and easily accomplish what were previously laborious tasks. A copy of this paper with all supplemental material is available at osf.io/zkxv9.
An Optical Accelerator for Deep Neural Network Based on Integrated Nanophotonics

The emergence of nanophotonic devices has enabled to design light-speed on-chip optical circuits with extremely low latency. This paper proposes an optical implementation of scalable Deep Neural Networks (DNNs) enabling light-speed inference. The key issue in optical neural networks is the scalability limited by area, power and the number of available wavelengths. Due to the scalability, it is thus difficult to design an all-optical hardware accelerator for a large-scale DNN. To solve this problem, this paper firstly proposes an optical Vector Matrix Multiplier (VMM) structure operating with a low latency. The multipliers in a VMM are highly parallelized based on the Wavelength Division Multiplexing (WDM) technique, which reduces the area overhead without sacrificing the ultra-high speed nature. This paper then proposes the electrical digital interfaces for storing and handling intermediate VMM data without sacrificing the ultra-high speed nature, which enables to reuse the VMM multiple times with a low latency.
The Application of Blockchain in Intelligent Power Data Exchange

The State Grid ETL(SG-ETL) tool, based on Extract, Transform and Load(ETL) technology, has important application value in intelligent power data exchange. As an important part of the transmission component in the power grid system, it realizes the data exchange between power systems, but in the process of data exchange, problems are gradually exposed such as unguaranteed data quality and uncontrolled business process. In this respect, this thesis studies the blockchain technology and analyzes its feasibility in the application of intelligent power data exchange, optimizes the power transaction order management business process from the blockchain perspective as example, proposes the Decentralized Transaction credit model based on Blockchain P2P(DTBP) according to the process, and describes its evaluation algorithm, introduces the idea of "chain on the chain" to plan the scheme of data to chain in the data exchange process, designs the smart contract life cycle, formulates contract rules and interface function conventions, uses the JSPIN tool to design the third convention's Promela mode, verifies the data and analyse the result to solve the data exchange problems finally.
Deep Forest Regression Based on Dynamic State Transition Optimization Algorithm

As a deep algorithm of non-neural network structure, deep forest regression (DFR) can be used to build soft measuring models of difficult-to-measure key parameters. However, as a kind of deep learning, the optimization of hyperparameters has become an inevitable problem in DFR. To solve above problem, an improved dynamic state transition algorithm (DSTA) is used to optimize the hyper-parameters of the model. To achieved more accurate optimization process, the error change rate is used to fine-tuning the state factor during the iteration process, which is further improved with gradient-based refinement. Finally, simulation experiments are performed on the benchmark data set, and satisfactory simulation results show the effectiveness of the proposed approach.
Comparative Study of Object Recognition Algorithms for Effective Electronic Travel Aids

Object recognition involves detecting the probability of presence and class of one or more objects in a given image. In this paper we carried out a comparison between different state of art approaches and concluded which approach is more user-friendly, cost-effective and produces more accurate results for object recognition. We deployed various algorithms like You Only Look Once (YOLO), Single Shot Multibox Detector (SSD), Faster RCNN (Region Convolution Neural Network) on diverse Convolution neural network models (CNNs) namely Mobile Net, Inception, Res Net that act as base networks when used with detection algorithms. Subsequently, using varying development boards like Jetson Nano and Raspberry Pi 3 B+ the speed, latency and accuracy values were evaluated for different combinations of base networks and detection algorithms.
Enhanced elementary operations for quantum computing

Superconducting circuits are one of the most promising and rapidly developing platforms for implementing quantum bits and their control operations for the purpose of large-scale quantum computing. A quantum algorithm is run by executing a series of precise operations on the quantum bits, or qubits. The most important of these operations are the quantum gates along with the measurement and initialization of the states of the qubits and auxiliary components. In this dissertation, we develop techniques to enhance each of these three operations. The work employs systems of coupled qubits and resonators in the framework of circuit quantum electrodynamics. Firstly, we derive a theoretical lower bound for the error of a single-qubit gate implemented with a linear oscillator mode and show how to reach this bound. Secondly, we experimentally demonstrate a technique for measuring the state of a qubit by simultaneously driving the qubit and the accompanying readout resonator. Compared to our method, the standard technique involving only the resonator drive yields up to 100% larger error. Moreover, we experimentally and theoretically develop quantum devices and protocols that may be used to initialize or reset auxiliary quantum circuits such as resonators. By coupling a quantum circuit to a strongly dissipative environment through a coupler, the decay rate of the circuit may be tuned on demand by several orders of magnitude. Finally, we propose a general method to analytically solve the dynamics of a complex, open bosonic system that may be used to describe, for example, the experimental systems of this dissertation. The experimental techniques developed in this dissertation may potentially be utilized in future implementations of a quantum computer, and the proposed theoretical protocols pave the way for future experiments aiming to improve qubit control and heat management in large-scale quantum processors.; Suprajohtavat kvanttipiirit ovat yksi lupaavimmista ja nopeimmin kehittyvista alustoista, joilla voi mahdollisesti toteuttaa suuren kokoluokan kvanttilaskentaa. Kvanttilaskennassa keskeisessa asemassa ovat kvanttibitit eli kubitit seka niilla tehtavat tarkat operaatiot, joista kvanttialgoritmit muodostuvat. Operaatioista tarkeimmat ovat loogiset kvanttiportit seka kubittien ja muiden tarvittavien komponenttien tilojen mittaus ja alustus.Tassa vaitoskirjassa kehitetaan tapoja naiden kolmen operaation parantamiseksi. Tyossa kasitellaan kvanttielektrodynamiikkaan perustuvia, kubittien ja resonaattorien muodostamia kytkettyja systeemeja. Ensimmaiseksi johdamme teoreettisen alarajan lineaarisella varahtelymoodilla toteutetulle yhden kubitin kvanttiportin virheelle seka naytamme, miten alaraja on mahdollista saavuttaa. Toiseksi toteutamme kokeellisesti menetelman, jolla voi mitata kubitin tilan ajamalla kubittia ja siihen kytkettya lukuresonaattoria samanaikaisesti. Yleisesti kaytetty mittaustapa, jossa ajetaan ainoastaan resonaattoria, tuottaa jopa 100% suuremman virheen kuin toteuttamamme menetelma.…
An Antenna Diversity-Aided Channel Network for Wireless Channel Estimation

To estimate channel response is a remarkably challenging task for the wireless communication systems due to the extremely fast time-varying characteristic of channel, such as vehicle-to-vehicle (V2V) channel. In this paper, an antenna diversity-aided neural network is proposed for wireless channel estimation. A demodulation reference signals-aided maximal ratio combining estimator is presented to extend the demodulation reference signals into tentative channel response. A super-resolution residual network is utilized to track the variations of V2V channel by extracting the time-frequency correlation of data symbols and a denoise network is used to removing the noise by extracting the noise distribution information of data symbols. Numerical simulation results show the effectiveness of the proposed an antenna diversity-aided channel network.
A New Intrusion Detection Algorithm Based on PSO and Kernel Extreme Learning Machine

Aiming at solving the problems of low accuracy and high false alarm rate for network intrusion detection, an algorithm of intrusion detection based on PSO and kernel Extreme learning is proposed in this article. Particle Swarm Optimization (PSO) is a kind of swarm intelligent algorithm. Kernel extreme learning machine (KELM) is a kind of classical learning method of kernel machine with its fast learning rate and strong generalization ability, but the selection of kernel function and parameters of KELM directly affects its classification performance. In this article, PSO is used to optimize the kernel parameters of kernel limit learning machine. The global kernel function with strong generalization ability of linear combination and the local kernel function with strong learning ability are used to form multi-kernel limit learning machine, which can improve the performance of single kernel Extreme Learning Machine (ELM) classifier. Finally, the performance of the algorithm is compared and analyzed through experiments and the experimental results verify the validity of this algorithm.
A New Non-random Property of 4.5-Round PRINCE

PRINCE is a widely analyzed block cipher proposed in 2012. Subspace trail cryptanalysis is a new cryptanalytic technique to generalize the invariant subspace attack. So far, two subspace trails that exist with probability 1 are known for 2.5 rounds of PRINCE. In this paper, we first describe a new non-random property for 4.5 rounds of PRINCE based on subspace trail with certain probability, which is independent of the secret key, the details of the Linear layer and of the S-Box layer. Then, we obtain that by appropriate choices of difference for a number of input pairs, it is possible to make sure that the number of times that the difference of the resulting output pairs lie in a particular subspace is always a multiple of 8. Later, a detailed proof is given as why it has to exist. Relying on this property, a new distinguisher can be set up to distinguish the 4.5-round PRINCE from a random permutation.
Visual Images Memory Recall Based on ClassRBM and Free Energy Minimization

Mounting evidence suggests that the brain works in a Bayesian way. Probabilistic inference models based on Bayesian theory have been widely applied in human memory modeling, such as search of associative memory (SAM) and retrieving effectively from memory (REM). However, these lines of memory models can only determine the class of the test images during memory recall. They fail to recall the test visual image, which is referred to as mental imagery in cognitive psychology. In order to address this issue, in this paper, we first propose a memory model based on classification RBM (ClassRBM), in which a three-layer neural network is constructed with 500-800-500 units. The input layer and output layer represent the visual sensory images and corresponding labels, respectively, while the hidden layer stores the encoded visual information. Then we apply free energy minimization principle to train the model, in which we assume that the storage and recall processes of memory in human brain involve free energy minimization. Finally, the learned visual images can be reconstructed by probabilistically sampling the visual units over the hidden units. Comprehensive experiments show that the proposed memory model is capable of recalling images studied and can be applied to the modeling of mental images in the mind.
Automatic Information Infrastructure Planning

In order to improve the information infrastructure planning efficiency, it's necessary to develop an intelligent system to realize automatic planning. This work presents a smart planning system which includes three modules: the geographic information recognition module, which is able to identify different categories of land using maps, photos and satellite images; the scoring module, which calculates the location and various parameters of the required urban information infrastructures, such as the base stations, the pipelines, etc., and generates a certain number of candidate location coordinates to provide the scores of each facility to be planned, which arise from certain technical, economical, principles; The optimization module, which uses the optimization algorithms like gradient descent method, Newton method, Levenberg-Marquardt method, simulated annealing method, genetic algorithm, etc. to optimize the infrastructure locations and choose the best plan combination eventually.
Facial Image Super Resolution on 3 Architectures of Generative Adversarial Network

Facial images are widely used for biometric recognition such as facial recognition, analysis or reconstruction. Consequently, it is necessary to increase the resolution of facial images from low resolution to high resolution or super resolution. Generative Adversarial Network (GAN) is a machine-learning-based method that can be used to increase image resolution. GAN is composed of two CNN, namely Generator and Discriminator. This research focuses on examining the effect of Generator configuration, specifically the number of feature extractor blocks named basic blocks and filters on Generator, which are built based on three different architectures, which are Inception, Resnet, and Inception-Resnet. Our finding shows that Resnet with 128 number of filter and 32 number of basic blocks gives the best result.
Knowledge Discovery on Blockchains: Challenges and Opportunities for Distributed Event Detection Under Constraints

We study the applicability of blockchain technology for distributed event detection under resource constraints. Therefore we provide a test-suite with several promising consensus methods (Proof-of-Work, Proof-of-Stake, Distributed Proof-of-Work, and Practical Proof-of-Kernel-Work). This is the first work analyzing the communication costs of blockchain consensus methods for knowledge discovery tasks in resource constraint devices. The experiments reveal that our proposed implementations of Distributed Proof-of-Work and Practical Proof-of-Kernel-Work provide a benefit over Proof-of-Work in CPU usage and communication costs. The tests show further that in cases of low data rates, where latencies by mining do not cause harm proposed blockchain implementations could be integrated. However, usage of blockchain requires data broadcasts, which leads to communication overhead as well as memory requirements based on the address list.
Students’ Morphological Errors in Using Word Formation in Writing an Essay

Abstract This research was aimed to find out students’ morphological errors in using word formation in writing an essay. Word formation that was found in the students’ writing are derivation and inflection. The type of this research was descriptive qualitative research. The source of data was the English Department students of Universitas Putra Indonesia Yayasan Perguruan Tinggi Komputer ( UPI YPTK) Padang who were in the fourth semester that consist of 23 students. The data were gotten through writing test. The finding of this research showed that the students made morphological errors in using derivation and inflection in writing the essay. Errors in derivation were 10 data, while errors in inflection were 83 data. Based on those findings, the students still made errors in using word formation in their writing.