Reviewerly/exHarmony huggingface dataset:
Leveraging Syntactic Dependency and Lexical Similarity for Neural Relation Extraction

Relation extraction is an important task in knowledge graph completion, information extraction and retrieval task. Recent neural models (especially with attention mechanism) have been shown to perform reasonably well. However, they sometimes fail to: (i) understand the semantic similarity of words with the given entities; and (ii) capture the long-distance dependencies among the words and entities such as co-reference. Moreover, this paper proposes a novel relation extraction model, which leverages syntactic dependency and lexical similarity for enhancing attention mechanism, to get rid of dependence for rich labeled training data. We conduct experiments on widely-used real-world datasets and the experimental results demonstrate the efficiency of the proposed model, even compared with latest state-of-the-art Transformer-based models.
Artificial Intelligence and conscious brain waves

The human brain emits waves that we do not feel.The waves emanating from the brain are conscious and aware.The human brain is made up of a large number of nerve cells called neurons.The brain sends various messages to different parts of the body and receives messages from them through neurons.Human brain waves can also communicate directly with Artificial Intelligence.Artificial intelligence is concerned with intelligent behavior in artifacts and has three common characteristics with human being: learning, problem-solving and decision-making.The direct connection between human brain waves and artificial intelligence is a challengeable issue, because it has lots of advantages and disadvantages.One of the most challengeable issues is reading the human mind by artificial intelligence, that is, the privacy of people will be in danger.Another issue is that governments can control their people according to their own will, not the will of the people.On the other hand, direct connection between human brain waves and artificial intelligence has lots of advantages, among them, solving the problem of dumb people, resolving family disputes, creating peace and intimacy between people, increasing the power of memory and strengthening the immune system can be mentioned.As this technique will appear soon, it is necessary to inform people of it and codify its specific rules and regulations in order to avoid misuse it.
Augmented Reality Technologies

Can modern technology be used through the effect of augmented reality in advertising in different fields ? Does augmented reality present a difference from the real reality by adding stimuli that work to attract the recipient ? I found, as a researcher, presented in the advertising process and methods of presentation, which resulted in a desire to facilitate the process of finding distinctive characteristics of what will be advertised in three more ways than their counterparts in real reality .
Research on the Integration of Online-offline Hybrid English Teaching in Information Structure

Abstract As a kind of teaching strategy based on information technology, hybrid teaching has the typical characteristics of customization and multi dimension, which helps students free from spactio-temporal limitations in English learning process and carry out English learning more freely and flexibly. Based on this, this paper first studies the feasibility of English online-offline hybrid teaching integration, then analyses the implementation of English online-offline hybrid teaching, and finally gives the optimization strategy of English online-offline hybrid teaching based on informatization.
Effect Of Additional Variations Of Etanol Fuel On Exhaust Gas Emissions On Yamaha 125cc Motorcycles

The very rapid development of the automotive world nowadays has made it a daily necessity for the community, in everyday human life it cannot be far from the name automotive, both in terms of transportation, and all kinds of supporting household needs. This can be seen from the number of motorbikes operating more when compared to other types of land transportation vehicles such as: cars. As we know, all transportation vehicles today still use non-renewable fuels. In line with the growing demand for fuel in the transportation, industrial and household sectors. Then this will result in unfavorable impacts on the environment, namely the residual exhaust gases from combustion. The remaining exhaust gases cause air pollution which can pollute the environment and can even destroy ozone which is very useful for living things on earth. In this study we use a reference concept which is then used as a concept, how is the effect of variations in the addition of ethanol fuel to exhaust gas emissions of Yamaha 125cc motorcycles by using variations in the percentage of ethanol 10%, 20%, 30% and variations in engine speed during testing, at 4600rpm, 5700rpm, and 6200rpm. It was found that the best torque and power were produced at a percentage of 10% ethanol at 6200 rpm indicating a power of 6.4 Hp and a torque of 10.6 Nm, and the best results on 30% preentae ethanol at 4600rpm showed 0.01% CO gas, 37 ppm HC gas, 1.8% CO2 gas and 18.06% O2 gas.
exHide: Hiding Data within the exFAT File System

Recently, steganographic techniques for hiding data in file system metadata gained focus. Tools for commonly used file systems were published but the exFAT file system did not get much attention – probably because its structure provides only few suitable locations to hide data. In this work we present two approaches to hide data in the exFAT file system. While the first approach is more flexible regarding embedding locations, it is rather fragile and provides a lower embedding rate. The second approach, called exHide, has stricter requirements for embedding, but is rather robust and provides a reasonable embedding rate. We describe the design of both approaches, evaluate them, and discuss their weaknesses and advantages.
Distance Metrics in Clustering and Weighted Scoring Algorithm

One of the current challenges for the supervised classification methods is to obtain acceptable values of the performance measures for an imbalanced dataset. There is a significant disproportion in the number of objects from different class labels in datasets with a high imbalanced ratio. This article analyzes the clustering and weighted scoring algorithm based on estimating the number of clusters that consider the minimum number of objects from the minority class label in each cluster. This algorithm uses the distance metric when determining the value of the score function. Therefore, this article aims to analyze the impact of selecting the distance metric on the six classification performance measures’ value. The performed experiments show that the Euclidean distance allows obtaining the best classification results for imbalanced datasets.
Iprnn: An Information-Preserving Model For Video Prediction Using Spatiotemporal Grus

Videos are typically encoded to low-dimensional features to save computation resources for video prediction models. However, the unacceptable information loss while encoding is restricting the performance of the predictive models. To solve this problem, in this paper, we propose an Information-Preserving Spatiotemporal Predictive Model for video prediction, denoted as IPRNN. In our method, we apply multiple skip-connections between the corresponding layers between the encoders and decoders. In this way, more useful information from the encoders can be recalled by the decoders to achieve a more satisfactory performance. Moreover, to further save the computation resources for predictive models, we design a spatiotemporal gated recurrent unit (STGRU), which can efficiently capture the spatial appearance information and temporal motion information for videos. Experimental results show that the proposed method can obtain better performance compared with other state-of-the-art methods.
Research on Collaborative Filtering Recommendation Algorithm for Improving User Similarity Calculation

The structural similarity and numerical similarity in the similarity calculation of the traditional user-based collaborative filtering recommendation algorithm have their own shortcomings. Structural similarity only uses the structural information of user ratings without considering the numerical information for user similarity, and numerical similarity only uses the ratings to calculate the similarity without considering the structural information, which makes the similarity calculation lacks accuracy. Aiming at this problem, a method that considers both the structural similarity of numerical information and the numerical similarity of structural information is proposed. When improving the numerical similarity, the influence of item quality and item popularity on the numerical similarity is considered. Simultaneously, the commonly used numerical similarity calculation uses the improved structural similarity to optimize. The five-fold cross-validation method is tested on the Movielens100K public data set. Compared with the traditional user-based collaborative filtering recommendation algorithm, the experiment proves that the proposed method effectively improves the recommendation performance.
Research on Application of Computer Artificial Intelligence in Charging Pile

research-article Research on Application of Computer Artificial Intelligence in Charging Pile Share on Authors: Huawei Hong State Grid Fujian Electric Power Co., Ltd, China State Grid Fujian Electric Power Co., Ltd, ChinaView Profile , Yuquan Su State Grid Fujian Electric Power Co., Ltd, China State Grid Fujian Electric Power Co., Ltd, ChinaView Profile , Peng Zheng State Grid Fujian Electric Power Co., Ltd, China State Grid Fujian Electric Power Co., Ltd, ChinaView Profile , Nuo Cheng State Grid Fujjan Power Economic Research Institute, China State Grid Fujjan Power Economic Research Institute, ChinaView Profile , Jinbo Zhang State Grid Fujjan Power Economic Research Institute, China State Grid Fujjan Power Economic Research Institute, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 125Pages 1–6https://doi.org/10.1145/3469213.3470328Online:18 August 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Android-Based Public Transport Violation Reporting Application

Abstract The rise of violations of traffic regulations and standard operating procedures for Batik Solo Trans public transport by public transport drivers is caused by weak law enforcement with limitations of law enforcement officials in its implementation. In this regard, a tool is needed to support law enforcement, particularly in relation to the violation reporting system of the traffic and standard operating procedures for Batik Solo Trans public transport. The reporting tool must be effective, efficient and transparent in its reporting system. The aim of the study is to develop an Android-based public transport violation reporting application called BST Watch App. Android is a Linux-based operating system designed for touch screen mobile devices such as smartphones and tablet computers. Android is an operating system with open source and Google releases the code under the Apache License. The application system is created by using the java programming language. The results of public transport violations characteristic analysis in the study area indicate that the type of violation which is often occurs is picking up and dropping off passengers at outside the bus stop. The implementation of the BST Watch App shows that this application is easy to use, practical, accurate, and transparent.
Deep Color Mismatch Correction In Stereoscopic 3d Images

Color mismatch in stereoscopic 3D (S3D) images can create visual discomfort and affect the performance of S3D image processing algorithms, e.g., for depth estimation. In this paper, we propose a new deep learning-based solution for the problem of color mismatch correction. The proposed solution consists of a multi-task convolutional neural network, where color correction is the primary task and correspondence estimation is the secondary task. For the training and evaluation of the proposed network, a new S3D image dataset with color mismatch was created. Based on this dataset, experiments were conducted showing the effectiveness of our solution.
Safety Testing of Neural Network for Image Classification

Artificial intelligence is an important driving force for the development of unmanned autonomous system. With the development of unmanned autonomous system, its safety problems are gradually exposed. Image classification is an important embodiment of the perception ability of unmanned autonomous system. In this paper, neural network of image classification, its safety test is discussed and studied. Firstly, it defines and introduces the safety and its related attributes. Based on the relevant attributes of functional safety, the safety testing framework of neural network for image classification is established, hoping to be able to make a comprehensive analysis of the safety of neural network for image classification. Finally, a data augmentation algorithm based on threshold is proposed to effectively enhance the safety of neural network model for image classification.
Kinect-Based Substation Operation and Maintenance Action Recognition Method

Aiming at the problem of traditional substation operation and maintenance training method, this paper proposes a new method for identifying substation operation and maintenance actions based on Kinect depth camera. The method is to establish a data set through a hybrid feature extraction method of human bone joint angles and the normalized distances of joint points; establishing three models (CART, KNN and BP) to identify ten typical operation and maintenance actions of substations. The experimental results show that the accuracy of using hybrid feature recognition is higher than that of single feature, and the recognition accuracy of CART algorithm based on hybrid features reaches 98.1%. Its effect is better than KNN algorithm and BP neural network method, and it can accurately identify ten selected substation operation and maintenance actions.
Self-supervised Learning for Semantic Sentence Matching with Dense Transformer Inference Network

Semantic sentence matching concerns predicting the relationship between a pair of natural language sentences. Recently, many methods based on interaction structure have been proposed, usually involving encoder, matching, and aggregation parts. Although some of them obtain impressive results, the simple encoder training from scratch cannot extract the global features of sentences effectively, and the transmission of information in the stacked network will cause certain loss. In this paper, we propose a Densely-connected Inference-Attention network (DCIA) to maximize the use of the feature from each layer of the network by dense connection mechanism and to get robust encoder by self-supervised learning (SSL) based on contrastive method, which can maximize the mutual information between global features and local features of input data. We have conducted experiments on Quora, MRPC, and SICK dataset, the experimental results show that our method owns competitive results on these dataset where we drive 89.13%, 78.1% and 87.7% accuracies respectively. In addition, the accuracy of DCIA with SSL will surpass the one of DCIA without SSL by about 2%.
Chopping off the Tail: Bounded Non-Determinism for Real-Time Accelerators

Modern data centers run web-scale applications on tens of thousands of servers, generating tens of thousands of Remote Procedure Calls (RPCs) to backend services for each incoming user request. Tail latency, due to a small fraction of randomly slow RPCs, decreases the performance of these incoming requests, degrades users' quality of experience, and limits disaggregation (applications' ability to scale across a data center). We argue that current approaches to improve tail latency (especially, those bounding computation time) are insufficient, even with (reconfigurable-) hardware accelerators. Instead, to chop off the tail, datacenter services should dynamically trade correctness (or result quality) for timeliness, providing bounded latency with near-ideal accuracy. In this paper, we discuss how the increasing prevalence of machine learning (including search techniques like approximate nearest neighbor and PageRank), perceptual algorithms (like computational photography and image/video caching), and natural language processing lets modern hardware accelerators make these dynamic correctness tradeoffs while improving users' quality of experience.
Reinforcement Learning-Based Adaptive Operator Selection

Metaheuristic and swarm intelligence approaches require devising optimisation algorithms with operators to let produce neighbouring solutions to conduct a move. The efficiency of algorithms using single operator remains recessive in comparison with those with multiple operators. However, use of multiple operators require a selection mechanism, which may not be always as productive as expected; therefore an adaptive selection scheme is always needed. In this study, an experience-based, reinforcement learning algorithm has been used to build an adaptive selection scheme implemented to work with a binary artificial bee colony algorithm in which the selection mechanism learns when and subject to which circumstances an operator can help produce better and worse neighbours. The implementations have been tested with commonly used benchmarks of uncapacitated facility location problem. The results demonstrates that the selection scheme developed based on reinforcement learning, which can also be named as smart selection scheme, performs much better that state-of-art adaptive selection schemes.
Research and Practice on Reform of Experimental Teaching System Based on Computer

Abstract The teaching environment of experimental teaching has also changed with the continuous development of social economy. Traditional experimental teaching is mostly based on book teaching and multimedia, or supplemented by analyzers and simulation systems, but there are still certain limitations. This article is based on computer technology through the process of experimental teaching, all-round exercise of students’ experimental teaching professional knowledge and application of skills, aiming to improve students’ comprehensive ability.
On Machine Learning Methods for Genome Enabled Prediction of Complex Phenotypes

We present a video outlining the potential of applying machine learning techniques to genome enabled prediction of complex phenotypes with agricultural value, in a context of increasing genetic data availability. It is part of an ongoing research project on the subject, and aims to introduce the problem and main motivations behind this line of research to other researchers and the general public.
Application Of Data Mining On Sidoarjo Leather Crafts Sales With Apriori Algorithm To Assist Marketing Strategies

Sidoarjo is one of the regencies that is developing in a forward direction. This is known by the increase in the tourism sector and Small and Medium Enterprises. One of them is Sidoarjo leather handicraft. UD Qory Jaya is a business actor in the leather retail industry. Orders that continue to increase make the turnover of goods uneven resulting in a buildup of stock in stores. Due to these problems the compilers took the initiative to deliver messages by carrying out a method of processing data using the Apriori algorithm. This method is used to maximize the sales potential of a combination system of goods so that the target item for sale is appropriate. From the results of research conducted by testing a minimum support of 15% and a minimum of 20% confidence, the association rules are produced if you buy a slingbag then buy men's leather shoes and vice versa. And from the test results with a minimum support of 70% and a minimum confidence of 70% an association rule is not generated because the existing data does not exceed the minimum value of support and minimum confidence. With the implementation of this method, researchers hope to have a better impact for developing future marketing strategies based on previously researched data.
Effect of Loss Functions on Language Models in Question Answering-Based Generative Chat-Bots

Question answering mechanism plays a pivotal role in helping generative chat-bots provide accurate answers to the questions asked by the questioner. Language models are an integral part of question/answer mechanisms where they answer two things, namely classifying the question correctly and also help provide the most suitable answer for the question asked. This paper takes into account the effect of various loss functions on the answer-predicting capability of a language model. Various loss functions were implemented like cross-entropy loss, negative log-likelihood loss, cosine embedding loss, and Kullback–Leibler divergence loss and found that there is a significant impact due to selection of loss functions on accuracy. Based on the research and implementation in this paper, it is found that choosing the multi-label cross-entropy loss for all the general question answering problems has a 0.5–1% raise.
Abnormality Detection and Identification Algorithm for High-Speed Freight Train Body

Abnormality detection and identification for high-speed freight train body is an indispensable part of the Train Operation Status Monitoring System. Generally, abnormality detection and data recording are performed manually, which is very prone to cause problems such as false detections, missing detections and recording errors because of lots of freight trains passing through the station simultaneously. In order to tackle these problems, we proposes an Improved-YOLO model, based on the YOLOv4, adding the SE-Block to optimize the feature selection method, switching to Cascade PConv Module and Integrated BN combination instead of PANet for multi-level feature fusion, using random data augmentation to improve the generalization. Meanwhile, model training is assisted by introducing negative sample mechanism. With 4594 positive samples (including freight train body abnormalities) and 5406 negative samples (excluding freight train body abnormalities) collected at the actual station as the train set, and 4705 images of freight train body within 24 h as the test set, the Improved-YOLO has dropped by 18.72% and 7.10% in false detection rate and missing detection rate respectively compared to the original YOLO model and reached 8.95% and 9.38%.
The Innovation Model and Practice of English Teaching Based on Information Technology

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.