Reviewerly/exHarmony huggingface dataset:
Online design, or self-empowerment through informatics classes

The article presents an organisation of teen's learning activities where students get familiar with the elements of graphic design, learn your use an online graphical app, create projects and learn to review each other's work. The informatics course contents meets the psychological needs of teenagers. The desire to be heard and recognized encourages the development of graphic projects in the form of themed posters. There are described in detail contents of three lessons: "Me and my strengths", "My greatest strength", "Sayings and proverbs". Students fulfill mini-projects while keeping in mind the key to successfully completing the project: need — motive — goal — planning — acting on the plan — producing result — analysis of the result by comparing it to the goal and reflecting on performed actions — evaluation of other projects — ideas and useful tricks for future projects.
Visual Weather Property Prediction by Multi-Task Learning and Two-Dimensional RNNs

We attempted to employ convolutional neural networks to extract visual features and developed recurrent neural networks for weather property estimation using only image data. Four common weather properties are estimated, i.e., temperature, humidity, visibility, and wind speed. Based on the success of previous works on temperature prediction, we extended them in terms of two aspects. First, by considering the effectiveness of deep multi-task learning, we jointly estimated four weather properties on the basis of the same visual information. Second, we propose that weather property estimations considering temporal evolution can be conducted from two perspectives, i.e., day-wise or hour-wise. A two-dimensional recurrent neural network is thus proposed to unify the two perspectives. In the evaluation, we show that better prediction accuracy can be obtained compared to the state-of-the-art models. We believe that the proposed approach is the first visual weather property estimation model trained based on multi-task learning.
Generalized Linear Models

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A Supernova Detection Implementation based on Faster R-CNN

Object detection is the basis of many computer vision applications. It combines two tasks which are object classification and location. With the development of astronomical observation technology, using object detection methods to find more novae and supernova is becoming an interesting and practical issue. In this paper, a model-based supernova object detection framework including pseudo color image compositing technology is proposed by using faster R-CNN. And by balancing the uneven positive and negative sample, data enhancement, feature extraction, focal loss modification and neural network training, the model gives a suitable method for supernova detection. The deep learning network is realized by Tensorflow and the network test results show that the proposed method can achieve better accuracy under the given prediction standard calculation method. The training and testing sample are provided by the Popular Supernova Project(PSP).
Discussion the effect of non-collimated light path on moiré deflectometry based on diffraction theory

In this paper, the effect of non-collimated light path on moiré deflectometry is studied based on scalar diffraction theory. The complex amplitude of non-collimated light path is initially deduced, which shows it has a magnification factor compared with that of the collimated light path. Then, the intensity distribution and fringe equation are obtained, analyzed and discussed under different orders filtering. Based on which, it is found that a series of linear function under non-collimated light without phase object causes the moiré fringes to tilt, and the effect of phase object's position on final moiré fringe is compared. Finally, the relevant theoretical derivation results are validated by comparing with that of experiment, which proves our theoretical deduction is reasonable. In conclusion, the results could provide some references for expanding the application range of moiré deflectometry.
Recent Trend in the Education System of India

Education is meant to prepare an individual for life, equip him physically, intellectually, emotionally and spiritually to deal with the challenges of life with self-assurance, audacity, making the right decisions, and broadening their perspectives. The primary objective of education is to direct the individual to live his life in a useful and in a meaningful way. One of the prevailing problems in the education system in the present existence is the variation in the quality of education; the quality of education tends to be the reflection of the prosperity. For a person to prosper in the society, education is considered imperative. The main purpose of this research paper is to understand the problems in the Indian education system, the main areas that have been highlighted are, need and significance of education, issues in the Indian education system, and challenges in the Indian education system and suggestions for making improvements in the Indian education system.
Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization

In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).
Guided Deep Reinforcement Learning for Path Planning of Robotic Manipulators

To improve the efficiency of deep reinforcement learning (DRL)-based methods for robotic path planning in the unstructured environment with obstacles, we propose a Guided Deep Reinforcement Learning (GDRL) for path planning of robotic manipulators. Firstly, we introduce guided path planning to accelerate approaching process. Secondly, we design a brand-new dense reward function in DRL-based path planning. To further improve learning efficiency, the DRL agent is only trained for collision avoidance, rather than for the whole path planning process. Many useless explorations in RL process can be eliminated with these three ideas. In order to evaluate the proposal, a Franka Emika robot with 7 joints has been considered in simulator V-Rep. The simulation results show the effectiveness of the proposed GDRL method. Compared to the pure DRL method, the GDRL method has much fewer training episodes, and converges 4× faster.
Real-Time Physical Prototyping Tool Design Based on Shape-Changing Display

Prototyping during the early design phases has become an essential part of conceptualization and product development. Recent advances in digital design tools have enabled active user participation in the design process and direct interaction with prospective products. Despite the rapid advancements in prototyping, immediate prototyping methods remain unavailable. Creating a working prototype and evaluating its user interactions is an effective design strategy. If a prototype can be created immediately for designers to sensorially experience a model, they can test and simulate various design ideas with immediate user feedback in the early design phases. Therefore, this paper aims to develop a real-time prototyping method that enables designers to evaluate a physical model of a design. Accordingly, we demonstrated a complete design and proof of concept for closed surface-based shape-changing displays (SCDs) that can assist designers in realizing conceptual design development. Experiments were conducted to verify the robustness and accuracy of the shapes displayed using the proposed SCD. Simulation-experiment results reveal that complex organic shapes (rabbits or human faces) and man-made shapes (chairs, cars, and buildings) could be accurately reconstructed using the proposed closed surface-based SCD with numerous actuators. Furthermore, an experiment with a physical SCD prototype (2V icosphere) demonstrated accurate reconstruction of the optimized shapes of a digital model.
Webarchívum mint a tudományos kutatások tárgya

Web archives perform today not only tasks of content development, but are increasingly important as the subject of scientific research. It is important too to demonstrate their added value at the level of society. Defining new research directions and exploring new forms of external partnership offer new perspectives on public collections. Participation in well-established projects increases the prestige of institutions both in science and society. In higher education, new synergies can open up in the fields of education in and research into digital humanities, information science and information technology. This study was compiled during the works on a PhD dissertation. It presents the beginnings of Hungarian internet archiving and its wider professional environment. It offers an introduction to the various forms of web archives as subjects of scientific research. Prior to this, the framework of research activities focusing on web archives is discussed.
A note on estimation of diffuse radiation collected by all‐glass evacuated solar tube collectors

To simplify the estimation of diffuse radiation from the sky and ground collected by all-glass evacuated solar tube collectors (ETCs), four methods were proposed and used in the past. Among them, the one by assuming the directional intensity of diffuse radiation projected on the plane coplanar with cross-section of solar tubes (2-D model) to be isotropic is simpler as mathematical expressions for the estimate of diffuse collectible radiation can be directly derived in this case. However, such assumption is not true for tilted evacuated solar tubes (ET), and the three-dimensional isotropic diffuse radiation (3-D model) should be used. In this note, a trail is made to estimate diffuse radiation collected by tilted-arranged ET collectors (T-type ETC) based on 3-D model and compare with those obtained based on the 2-D model. Results show that the diffuse radiation collected by T-type ETCs estimated based on 2-D and 3-D models are highly in agreement, and the difference between two estimates is dependent on the tilt-angle (β) of ETCs. For the case of β = 0 and 90°, the both estimates are completely identical as expected. For T-type ETCs with ETs sizing 47/58 mm in diameter of inner/outer tubes and spacing (B) between centers of two adjacent ETs less than 80 mm, the relative deviation between two estimates for diffuse radiation from the sky is less than 0.65% and that for diffuse radiation reflecting from the ground is less than 1.7% when β > 20°. This means that the 2-D model is reasonable and advisable for a quick estimation of collectible diffuse radiation of ETCs.
3D Pots Configuration System by Optimizing over Geometric Constraints

While potteries are common artifacts excavated in archaeological sites, the restoration process relies on manual cleaning and reassembly of shattered pieces. Since the number of possible 3D configurations is considerably large, the exhaustive manual trial may result in abrasion on fractured surfaces and even failure to find the correct matches. As a result, many recent works suggest virtual reassembly from 3D scans of the fragments. The problem is challenging in the view of the conventional 3D geometric analysis, as it is hard to extract reliable shape features from the thin break lines. We propose to optimize for the global configuration by combining geometric constraints with information from noisy shape features. Specifically, we enforce bijection and continuity of sequence of correspondences given estimates of corners and pair-wise matching scores between multiple break lines. We demonstrate that our pipeline greatly increases the accuracy of correspondences, resulting in stable restoration of 3D configurations from irregular and noisy evidence.
Application of Virtual Reality Technology in the Health Field Based on the Background of Big Data

Abstract With the development of big data and artificial intelligence, virtual reality technology has penetrated into every aspect of daily life. Among them, the application of virtual reality technology in the health field is of great significance. Virtual reality technology can assist clinical medicine in the treatment of patients, and can also simulate the physical feelings of panic disorder and some disabilities to induce emotions similar to patients, produce empathy effects, enhance normal people’s understanding of patients, or improve health The effect of dissemination. This article discusses the current status of the application of virtual reality technology in the context of big data, and analyzes how virtual reality technology can promote health communication, disperse pain, treatment of psychological disorders, and begin to provide services for the elderly, which has great social and practical significance. This article reviews and compares the recent applications of virtual reality technology in the health field at home and abroad, and analyzes its prospects and shortcomings.
Decentralized Remote Voting System

The democratic technique is the instrument for carrying out individuals' view to more readily manage the framework. All through late years, regular votes have satisfied neither individuals nor government specialists. They are not altogether protected since voting forms are easy to strike. It additionally challenges elector wellbeing and straightforwardness. Also, checking the votes takes excessively long adjustment of casting a ballot overall is a charming issue in current casting a ballot framework. To tackle these issues Computerized innovation is utilized in the democratic stage for residents in numerous countries. Digitalization alone can not fix the issues completely. There are additionally numerous methods of controlling or on the other hand adjusting advanced innovation and blocking casting a ballot. There ought to be decency, autonomy and unprejudiced nature in the casting a ballot technique. By breaking down the previously mentioned issues, this research work joins the digitalization with the blockchain innovation to give a democratic system. The primary objectives of our casting a ballot system are to give uprightness, obscurity, protection, furthermore, security of voters. With the utilization of markle tree and finger impression hash, the information trustworthiness and namelessness, protection, security of the citizens has been accomplished in our proposed advanced democratic frameworks.
The Role of Technology and Network Externalities in the Long-Term Performance of ICOs

This study examines the impact of technology and network externalities on exchange-listed Initial Coin Offerings (ICOs). ICOs are a new fundraising instrument used to finance technological innovation against a digital voucher or receipt. They diffuse the ownership of claim for a digital good. Utilising an online database comprising of self-reported ICO characteristics, measures of post-ICO performance, along with information on business social networks, higher fundraising figures are found to contribute to the ICO long-term success, but this impact is multiplied by six times when fundraising is conducted to existing, proprietary blockchain. This is explained by the network effect. The modified information ratio measure is used to approximate the quality signalling of ICO organisations using price timeseries and benchmarking these to already functioning blockchain technology, e.g. ethereum in the long-term. The ICO sample’s mean trading period on exchanges is 1.5 years and is used for long-period asset analysis. Additionally, the cointegration to the market technology benchmark is found to have a large, significant negative effect on long-term ICO organisational success as this indicates lower ICO intrinsic value.
Coherence and Identity Learning for Arbitrary-length Face Video Generation

Face synthesis is an interesting yet challenging task in computer vision. It is even much harder to generate a portrait video than a single image. In this paper, we propose a novel video generation framework for synthesizing arbitrary-length face videos without any face exemplar or landmark. To overcome the synthesis ambiguity of face video, we propose a divide-and-conquer strategy to separately address the video face synthesis problem from two aspects, face identity synthesis and rearrangement. To this end, we design a cascaded network which contains three components, Identity-aware GAN (IA-GAN), Face Coherence Network, and Interpolation Network. IA-GAN is proposed to synthesize photorealistic faces with the same identity from a set of noises. Face Coherence Network is designed to re-arrange the faces generated by IA-GAN while keeping the inter-frame coherence. Interpolation Network is introduced to eliminate the discontinuity between two adjacent frames and improve the smoothness of the face video. Experimental results demonstrate that our proposed network is able to generate face video with high visual quality while preserving the identity. Statistics show that our method outperforms state-of-the-art unconditional face video generative models in multiple challenging datasets.
How to Make Smart Contract Smarter

The smart contract is a self-executing code that is managed by blockchain nodes, providing coordination and enforcement framework for agreements between network participants. However, smart contracts are not particularly “smart” since the virtual machine (VM) does not support the running environment of machine learning. To make the smart contract smarter, we propose a decentralized blockchain oracle framework to support smart contract training the machine learning model. In view of malicious nodes which may attack the process of training, we propose a consensus algorithm to prevent the malicious attack from malicious nodes. At the end of this paper, we do an experiment on two datasets: MNIST and CIFAR10. The result shows that our framework can prevent malicious attack efficiently and keep high accuracy. With our proposed framework, smart contracts have an ability to train or call machine learning model, making a smart contracts “smarter”.
Comparative study of Kalman filter-based target motion analysis by incorporating Doppler frequency measurements

Abstract Target motion analysis is a key requirement of autonomous and self-driving machines like drones and robots. However, with strict weight limits, the aerospace industry is always on the hunt for simpler and lighter sensing solutions. Continuous-wave Doppler radars are the simplest radars that can easily obtain a target’s relative velocity using the Doppler shift in the received wave. However, these radars cannot provide the target’s range. In this work, we address the problem of obtaining target’s range and velocity by incorporating Doppler frequency measurements from a simple continuous wave Doppler radar. To this end, we find out the movement patterns and maneuvers that an observer can make to converge to the target’s location. After presenting the observability requirements, we design and compare various non-linear Kalman filter-based target trackers. We experimented with different simulation scenarios to compare the tracking results with bearings-only, frequency-only, and bearings-frequency measurement sets. In our analysis, Unscented Kalman Filter with bearings-frequency measurements performed best. Experiments show that an observer can locate the target accurately within 10 cm by incorporating Doppler frequency measurements. Moreover, it also reduced the convergence time to a fraction of a second.
Single Image Dehazing Using Dark Channel Fusion and Dark Channel Confidence

Sometimes dark channel prior for single image dehazing algorithms may fail in sky areas and suffer from the darkness and residual artifacts. In this paper, we have proposed a new method to solve these problems which based on dark channel fusion and dark channel confidence. Firstly, the original dark channel and the pixel-wise dark channel is used to obtain the fusion dark channel, secondly we use dark channel confidence to correct for transmittance of the sky area. Experimental results show that the proposed method can obviously eliminate the color cast and distortion phenomenon in sky regions and the details o f restored image become more distinct after removing the residual artifacts.
BN-254 Based Multi-Core, Multi-Pairing Crypto-Processor for Functional Encryption

We have designed and fabricated a multi-core, multi-pairing crypto-processor on BN curve over 254bit prime field in 65nm CMOS process. We have designed optimal 2- layer sequencer for flexible operation, realizes up to 96.7% computation efficiency, with canceling data I/O overhead by double buffer architecture. Measurement results demonstrate 3.0 times faster with optimal 12-thread scheduling, and 2.9 times higher throughput and 1.7 times smaller energy consumption with 3-parallel scheduling than ever reorted. Flexible architecture enables to be utilized and accelarate the advanced crypto- algorithm such like inner product encryption and searchable encryption.
Named-Entity Recognizer for Common Nouns in Filipino Text

The Named Entity Recognizer for Filipino Text is a study that aims on detecting and classifying named entities present in a given text utilizing the Filipino language. The named entities are classified into four, namely: person, place, organization, date & time. Named entities that do not fall in the four classes are tagged as etc. To measure the accuracy of the system, solving for the precision, recall, error rate, and F-Measure, and accuracy in terms of error rate was used, both for every named entity and all the named entities as a whole. The approach of the Conditional Random Field was used to classify the named entities. Filipino short stories were used for the testing of the system. The results, based on solving for the F-Measure, indicate that the system is 88.05% accurate and best in identifying named entity Organization with 0% error rate, but with an acceptable or good enough error rate for the named entity Person, Place, and Date & Time, with an average of 3.24% error rate respectively. The results, based on solving for the accuracy in terms of error rate, indicate that the system is 97.01% accurate.