Reviewerly/exHarmony huggingface dataset:
Research on Decentralized Trusted Data Sharing Technology Based on Blockchain

In the current information age, having more data means having more information. However, the complete sharing of data will increase the possibility of criminals using relevant data to commit crimes. With the development of cloud computing technology, data sharing can be realized based on the cloud, and the shared data can be stored in the data center. However, this will not ensure the security and privacy of the shared data, and there are security risks such as tampering and counterfeiting of the shared data. The decentralization of the blockchain and the non-tamperable characteristics of the data on the block can safely save data-sharing records without the need for a trusted third party. The paper will discuss the decentralized trusted data sharing technology based on blockchain to enhance the credibility of data sharing.This paper will use the characteristics of the decentralized trust management of the blockchain to study the decentralized solution of trusted data sharing. Aiming at the storage problem of shared data, the block structure of shared data oriented to the blockchain is proposed, and the design is based on the blockchain, and the information of the shared data is stored in the blockchain; in response to the data access request and service problem in data sharing, a block chain-based, decentralized data sharing mechanism is proposed; usingsmart contracts can adopt data variables as parameters to transfer and store the characteristics.Design a decentralized, trusted data sharing smart contract, form a blockchain-based data sharing prototype system, and finally simulate the real on the Ethereum open source platform Scenes.The data sharing system based on blockchain was simulated and run on the Ethereum blockchain platform, and the data sharing function test was carried out. Experiments have shown that the blockchain-based data sharing technology solution in a decentralized application environment and distributed storage of data to protect data security, to a certain extent, enhance the credibility of data sharing.
Object Detection Techniques Using Deep Learning: A Survey for Real-Time Applications

Technologies in the field of autonomous vehicles (AV) has seen great evolution in recent years. Many automakers are actively attempting to embed advanced technologies into their products, and it’s testing at real world scenarios. This field of Autonomous Vehicles can arguably be said to be one of the most daunting topics of today in the field of Intelligent Transportation System (ITS), in particular the aspects like reliability, security, etc and as well as pushing forward for the world’s transition towards a highly sustainable future. The sensor technologies of today, however, have several drawbacks; wherein there are high levels of complexity and set-up costs likewise. Thus, this paper aims to accomplish the objective of object detection using only Computer Vision, and specifically for real-time purposes. Thus, this paper aims to explore and compare all the available deep learning based object detection algorithms and arrive at the best model for real-time applications.
Eye Movements in Tablet-based Visual Foraging

In the visual foraging task, participants search for and collect target elements while ignoring others. This enables the assessment of attention guidance and visual search in a continuous task which is less constrained than traditional visual search tasks. When foraging is performed on a tablet-PC, participants can respond by pointing (or tapping) on targets, and collect items at a high rate. However, this setup makes it challenging to record eye movements, a key indicator of attention. Up to now, tablet-based visual foraging experiments have solely relied on analyzing the manual selection responses. Here we show a novel method and first data from tablet-based visual foraging with eye tracking, enabling an insight into the interplay between manual selections and eye movements. The results highlight a close relationship between eye movements and manual selections. The coupling seems particularly tight in "simple" displays, in which targets are defined via a single feature. Here, selections seem to follow each fixation in a one-to-one relationship. In displays with "conjunction" targets, and particularly within same-type selection runs, gaze and hand movements deviate more from each other. This might reflect increased effort in coordinating hand and eye movements, well in agreement with the finding that foraging efficiency is typically impaired in conjunction conditions.
Evaluation of school literation movements in improving student discipline

The goal of this study is to assess SMP Negeri 7 Palembang's School Literacy Movement in Improving Student Discipline. With the CIPP model, this analysis uses a qualitative approach that includes: 1. 1. Evaluating Meaning, 2. Evaluating Input, 3. Evaluation of Operation, and 4. Service Evaluation. The data analysis techniques used in this study are data reduction, presentation of data, conclusion and verification. The analyzed data is the product of SMP Negeri 7 Palembang citizens' interviews, observations, and documentation. This research shows that the School Literacy Movement's assessment of enhancing student discipline, The school has provided such facilities and resources in support of literacy programs, which include reading cornersBy providing 15 minutes to read before carrying out learning activities at the reading habit level, And with some students, the School Literacy Movement has a positive effect on the work of literacy programs carried out by student achievement, It can be done and in SMP Negeri 7 Palembang there is a growth of character as well as a decision that this School Literacy Movement must be strengthened.
Evaluating Prototype Augmented and Adaptive guidance system to support Industrial Plant Maintenance

We evaluate AR for Plant maintenance by measuring how a prototype guidance system, tested under representative conditions, impacts performance. We are motivated to determine the cost-benefit of interactive guidance for hazardous, repetitive tasks and we observe an improvement of 21% efficiency, 50% accuracy and 19% reduced task load. AR has already been shown to deliver improvements in task performance, however, there is limited research exploring the integration of AR into complete task routines which presents a barrier to adoption. We apply mixed reality guidance via two within-group experiments. We measure efficiency and accuracy over a complete routine conducted under simulated conditions. Results compare AR versus Static and AR versus Adaptive. We conclude AR is best suited to demanding spatial translation and completion under pressure. We suggest AR offers potential in similar routines and propose further work to integrate in a live setting.
Application of information teaching methods in the innovation of E-commerce English curriculum

With the continuous development of education informatization, information technology and teaching are deeply integrated. The innovative E-commerce English curriculum shows different significance. Therefore, the information-based teaching method is applied to the innovation of E-commerce English curriculum. The traditional teaching structure is transformed and the information teaching approach is formed. The curriculum objectives are divided into three dimensions: knowledge and skill, process and method, emotional attitude and value. The E-commerce English curriculum objectives are formulated. The integration of E-commerce English teaching is taken as the construction criteria of curriculum elements. The evaluation standard of curriculum elements is constructed. Finally, the application research is completed. This paper selects E-commerce English major students from five universities as the experimental objects. It compares the three application modes of non application of information-based teaching method, application of traditional teaching methods and application of information-based teaching methods. The results show that the significance parameter of E-commerce English curriculum innovation after the application of information-based teaching method is the largest.
Novel avenues for robust free-space quantum communications

Quantum information processing and quantum communication are novel protocols that originate from the very fundamental and philosophical questions on superposition and entanglement raised since the early days of quantum mechanics. Strikingly, these new protocols offer capabilities beyond communication task possible with classical physics. One very important example is the secure key exchange based on the transmission of individual quantum signals between communication partners. The big vision and frontier in the field of quantum communication research is the development of a Quantum Internet, which establishes entanglement between many different users and devices. Extending today’s internet, the Quantum Internet will readily transfer quantum bits, rather than classical bits, between users near and far and over multiple different channels and could be used for secure communications, quantum computer networks and metrological applications. I will discuss recent advances on implementations and tools useful for generating and distributing photonic quantum entanglement over robust channels including. Time-bin encoding and reference-frame-free protocols. I will also present an overview of the upcoming Canadian quantum communication satellite QEYSSAT.
SSS-Net for Scene Text Recognition

CRNN has made extraordinary achievements in the field of natural scene text recognition, but there are still some cases of insufficient recognition accuracy. Aiming at the problem that similar characters in horizontal text are difficult to distinguish, Strip-Squeeze-Spatial-Network (SSS-Net) based on the attention mechanism is proposed. The key point of our proposed SSS-Net is SSS Block. It mainly includes strip convolution block, squeeze attention block and spatial attention block. Add the module directly to DenseNet, and connect with CTC to form a text recognition network SSS-Net. We conducted verification in four datasets: ICDAR2003, ICDAR2013, SVT and SCUT_FORU. The experimental results show that the accuracy of this method is better than the CRNN structure based on DenseNet as the backbone feature extraction network, and the parameter amount is only about 1/4 of CRNN.
NEREL: A Russian Dataset with Nested Named Entities and Relations.

In this paper, we present NEREL, a Russian dataset for named entityrecognition and relation extraction. NEREL is significantly larger thanexisting Russian datasets: to date it contains 56K annotated named entities and39K annotated relations. Its important difference from previous datasets isannotation of nested named entities, as well as relations within nestedentities and at the discourse level. NEREL can facilitate development of novelmodels that can extract relations between nested named entities, as well asrelations on both sentence and document levels. NEREL also contains theannotation of events involving named entities and their roles in the events.The NEREL collection is available via https://github.com/nerel-ds/NEREL.
AI Strict Liability Vis-à-Vis AI Monopolization

Some argue that applying a strict liability regime on AI-inflicted damages may lead to the monopolization of the AI industry. Strict liability is considered to be a regime that inherently stifles innovation given the costs it may inflict on companies. Therefore, the argument continues, it should not be applied on emerging technologies, such as AI. A strict liability regime can erect barriers to entry in the AI market in a way that will prevent small AI companies from competing. This is because these small companies lack the necessary resources to pay for damages inflected by their AI technology, leaving the industry to be monopolized by well-financed big AI companies.This Article rejects this argument and the implied correlation between an AI strict liability regime and the monopolization of the AI market. It does so for two reasons. First, there is no substantial connection between a strict liability regime and AI monopolization that is already underway. Second, insurance policies are available to mitigate the effects a strict liability regime may have on the capabilities of small AI companies to enter and compete in this important market. Therefore, the ongoing process of monopolization of the AI market should not render strict liability as a non-viable regime when AI-inflicted damages occur.
Simulating subatomic physics on a quantum frequency processor

Photonics is at the forefront of quantum computing and simulations. It enabled a host of pioneering demonstrations of the variational quantum eigensolver (VQE) algorithm, of molecular vibronic spectra and dynamics simulations, and of experimental Hamiltonian learning. It offers a versatile platform to process quantum information with low noise in a multitude of encodings, ranging from spatial or polarization degrees of freedom, to temporal modes. We discuss how frequency encoding, where qubits are represented by photons in narrow frequency bins, can be utilized for simulating complex many-body quantum phenomena in nuclear physics and quantum field theory. Using an all-optical quantum frequency processor, the ground-state energies of light nuclei including the triton, 3He, and the alpha particle are computed. Complementing these calculations and utilizing a 68-dimensional Hilbert space, our photonic simulator is used to perform sub-nucleon calculations of the two-body and three-body forces between heavy mesons in the Schwinger model. This work is a first step in simulating subatomic many-body physics on quantum frequency processors---augmenting classical computations that bridge scales from quarks to nuclei.
Kinect Sensor- based LMA Motion Recognition Model Development

This study conducted on ICT motion recognition games for intellectually disabled based on movement learning of LMA. The characteristics of the movement through Laban's LMA include the change of time in which movement occurs through the human body that recognizes space and the tension or relaxation of emotional expression. The design and implementation of the motion recognition model will be described, and the possibility of using the proposed motion recognition model is verified through a simple experiment. As a result of the experiment, 24 movement expression activities conducted through 10 learning sessions of 5 participants showed a concordance rate of 53.4% or more of the total average.Learning motion games that appear in response to changes in motion had a good effect on positive learning emotions. The purpose of this study is to suggest that the movement expression activity of intellectually disabled people is effective in the learning process of LMA motion recognition based on Kinect sensor.
Interlinking Valency Frames and WordNet Synsets in the LiLa Knowledge Base of Linguistic Resources for Latin

This paper describes the steps taken to model a valency lexicon for Latin (Latin Vallex) according to the principles of the Linked Data paradigm, and to interlink its valency frames with the lexical senses recorded in a manually checked subset of the Latin WordNet. The valency lexicon and the WordNet share lexical entries and are part of the LiLa Knowledge Base, which interlinks multiple linguistic resources for Latin. After describing the overall architecture of LiLa, as well as the structure of the lexical entries of Latin Vallex and Latin WordNet, the paper focuses on how valency frames have been modeled in LiLa, in line with a submodule of the Predicate Model for Ontologies (PreMOn) specifically created for the representation of grammatical valency. A mapping of the valency frames and the WordNet synsets assigned to the lexical entries shared by the two resources is detailed, as well as a number of queries that can be run across the interoperable resources for Latin currently included in LiLa.
Temperature and Rainfall Trends in Central Highland and Mid Rift Valley of Ethiopia

The study was carried out to identify the trends of rainfall, minimum and maximum temperature for Bishoftu, Adama, Meki, Metehara, and Holeta regions of Ethiopia in the years from 1970 to 2018. Mann Kendall test and Sen’s slope estimators’ methods, were used to detecting the trends of rainfall, minimum temperature, and maximum temperature. The result reveals that annual rainfall shows no trend at Metehara station but all stations have significant trend of declining annual rainfall Except for Adama station where significant trend was detected with an increasing rate of 1.148mm/annum. On the other hand, the trend of maximum temperature has significantly increasing in all stations with rates of 0.032, 0.24,0.05, and 0.46 Holeta, Bishofta, Metehara and Mieso stations respectively. While the trend of annual minimum temperature of all stations has significantly increasing trend at 70% confidence interval but at 95% significant level Metehara, Bishoftu and Adama 0.023, 0.01 and 0.048 0 C/annual show increasing trend respectively. Overall, the estimated values indicated that there is climate change in the study area. From this, it can be concluded the effect of global warming is present in both the highland and low land of Ethiopia.
What you see is what you get: VR as tool for travel experiences trials

Understanding customers' motivations is not an easy task, for that reason this article focus to study deep meanings in virtual reality (VR) experiences as accounted by users who have recently had V...
Analysis Process MILAR: Mining Indirect Least Association Rule Algorithm

One of the interesting and meaningful information that is hiding in transactional database is indirect association rule. It corresponds to the property of high dependencies between two items that are rarely occurred together but indirectly emerged via another items. Since indirect association rule is nontrivial information, it can implicitly give a new perspective of relationship which cannot be directly observed from the common rule. Therefore, we proposed an algorithm for Mining Indirect Least Association Rule (MILAR) from the real and benchmarked datasets. MILAR is embedded with our scalable least measure namely Critical Relative Support (CRS). The experimental results show that MILAR can generate the desired rules in term of least and indirect least association rules. In addition, the obtained results can also be used by the domain experts to do further analysis and finally reveal more interesting findings
Long-Term Visual Tracking of an Unmanned Surface Vehicle in Complex Environment Based on Adaptive Multi-Feature Fusion with Correlation Filtering

In this paper, a long-term visual tracking scheme of an unmanned surface vehicle (USV) based on adaptive multi-feature fusion is proposed for visual tracking in complex environment. The salient features of the proposed approach are: (1) A scale filter is used to solve the problem of performance degradation caused by scale change of the USV in visual tracking and the dimension reduction strategy is applied to the scale filter to accelerate scale detection in the tracking process. (2) To alleviate problem such as illumination variation, deformation and background color similarity in complex environment, the Histogram of Gradient (HOG) and Color Names (CN) features are successfully combined to improve reliability and accuracy of the visual tracking scheme. (3) The anti-interference ability of visual tracking is enhanced by training Support Vector Machine (SVM) re-detection module online in order to eliminate external factors such as occlusion and moving out of sight during long-term visual tracking thereby strengthening robustness of the visual tracking scheme. Experimental results and comparative studies demonstrate that the proposed scheme is superior in tracking.
Multi-photon and side-channel attacks in mistrustful quantum cryptography

New types of attacks on practical implementations of mistrustful quantum cryptographic schemes are identified, showing that countermeasures previously believed to ensure security in fact can leave schemes completely vulnerable.
Research on the Status and Causes of the Problems of Mobile Network Games in Junior High School Students

Based on the relevant research on online games, this paper takes junior high school students as the research object, analyzes the status of junior high school students' mobile online games from four aspects: the time of playing mobile games, the types of mobile games they prefer, the reasons and feelings of playing mobile games, and the cognition of the influence of mobile games on themselves.At the same time, through semi-structured interviews, we can deeply understand the influencing factors junior high school students' questions on mobile online games systematically.According to its, this paper hopes to provide reference for future research.
Design and Implementation of Monitoring and Evaluation System for Online Publicity of Natural Resources Government Information

In order to deal with the problems of long time consuming, heavy workload, complexity of statistical analysis in the process of evaluating publishing natural resources government-information online, the present paper designs and implements the system of online monitoring and evaluation of natural resources government affairs information. The system is designed and adopts a micro-service architecture, which automatically scores some indicators based on full-text indexing technology, and visually displays the evaluation results by means of GIS graphics services. The practice shows that the system provides the effective support for the evaluation, which generally improves the efficiency and reduces the cost.
A3C for drone autonomous driving using Airsim

In this work, we apply artificial intelligence to guide a drone to a certain point autonomously. Unreal engine creates a virtual environment where the drone can fly, and the algorithm is trained simulating the drone dynamics thanks to Airsim plugin. The implemented algorithm is Asynchronous Actor-Critic Advantage (A3C), which trains a neural network with less computing resources than standard reinforcement learning algorithms that normally needs costly GPUs. To prove these advantages, several experiments are run using a different number of parallel simulations (threads). The drone should reach a point randomly generated each episode. The reward, the value and the advantage function are used to evaluate the performance. As expected, these experiments show that a higher number of threads helps the leaning process improve and become more stable. These learning results are of interest to optimize the computing resources in future applications.
Goal-oriented adaptive e-learning

Adaptive learning has a lot of potential as it provides personalized information and learning material for learners of various fields and areas. The adaptive e-learning systems are now an ingrained vehicle of modern education. It accommodates an extensive range of learners with varied backgrounds who register with their specific learning objectives. The main challenge in the current situation is to produce adaptive learning paths so that learners can attain their goals successfully. Previous works used static features such as learners&apos; level of knowledge, browsing preferences, grasping ability, and learning styles to determine personalized learning materials.