Python has become one of the most popular programming languages in the era of data science and machine learning, especially for its diverse libraries and extension modules. Python front-end with C/C++ native implement...
ISBN:
(数字)9781728151434
ISBN:
(纸本)9781728151441
Python has become one of the most popular programming languages in the era of data science and machine learning, especially for its diverse libraries and extension modules. Python front-end with C/C++ native implementation achieves both productivity and performance, almost becoming the standard structure for many mainstream software systems. However, feature discrepancies between two languages can pose many security hazards in the interface layer using the Python/C API. In this paper, we applied static analysis to reveal the evolution and usage statistics of the Python/C API, and provided a summary and classification of its 10 bug patterns with empirical bug instances from Pillow, a widely used Python imaging library. Our toolchain can be easily extended to access different types of syntactic bug-finding checkers. And our systematical taxonomy to classify bugs can guide the construction of more highly automated and high-precision bug-finding tools.
the following topics are dealt with: feature extraction; learning (artificial intelligence); object detection; image segmentation; mobile robots; convolutional neural nets; production engineering computing; image matc...
the following topics are dealt with: feature extraction; learning (artificial intelligence); object detection; image segmentation; mobile robots; convolutional neural nets; production engineering computing; image matching; neural nets; probability.
City image is one of the most important and significant issues in the urban management. Previous research has proposed an automatic destination brand and image detection model, however, the model failed to identify ta...
City image is one of the most important and significant issues in the urban management. Previous research has proposed an automatic destination brand and image detection model, however, the model failed to identify target-specific image symbol. An improved automated city image symbol identification and comparison method is put forward in this research. the method consists of text analysis, Natural Language Processing (NLP) and Similarity measurement techniques. In order to evaluate the method, the experiment which newspapers is selected as the data source is conducted. the experimental results show that the method is better performed in discerning city image symbol and can be used to help people make city image aware decision.
A continual rise in the usage of titanium alloy for structural applications calls for sustainability in its shaping processes. Machining is the most commonly utilized process of the manufacturing domain. As the temper...
ISBN:
(数字)9781728153322
ISBN:
(纸本)9781728153339
A continual rise in the usage of titanium alloy for structural applications calls for sustainability in its shaping processes. Machining is the most commonly utilized process of the manufacturing domain. As the temperature dependent modes of tool damage seriously limit the material removal rates in the machining of titanium alloys, cryogenic approaches of heat dissipation are required to make the process sustainable. the paper focuses on application of two kinds of cryogenic fluids, liquid nitrogen and carbon dioxide snow, for a reduction in tool wear rate, work surface roughness, specific cutting energy and cutting forces in a continuous machining process. Moreover, the effect of changing depth of cut in machining a given volume of the work material without a change in processing time is also quantified. the analyses of the experimental data reveal that boththe cryogenic approaches yielded positive results with respect to all the sustainability measures.
Predicting the functions of the proteins from their structure is an active area of interest. the current trends of the secondary structure representation use direct letter representation of the specific secondary stru...
详细信息
ISBN:
(数字)9783030331108
ISBN:
(纸本)9783030331108;9783030331092
Predicting the functions of the proteins from their structure is an active area of interest. the current trends of the secondary structure representation use direct letter representation of the specific secondary structure element of every amino acid in the linear sequence. Using graph representation to represent the protein sequence provides additional information about the structural relationships within the amino acid sequence. this study outlines the protein secondary structure with a novel approach of representing the proteins using protein secondary structure graph where nodes are amino acids from the protein sequence, and the edges denote the peptide and hydrogen bonds that construct the secondary structure. the developed model for protein function prediction Structure2Function operates on these graphs with a defined variant of the present idea from deep learning on non-Euclidian graph-structure data, the Graph Convolutional Networks (GCNs).
Natural language understanding is a critical module in task-oriented dialogue systems. Recently, state-of-the-art approaches use deep learning methods and transformers to improve the performance of dialogue systems. I...
详细信息
ISBN:
(数字)9781665404419
ISBN:
(纸本)9781665404426
Natural language understanding is a critical module in task-oriented dialogue systems. Recently, state-of-the-art approaches use deep learning methods and transformers to improve the performance of dialogue systems. In this work, we propose a natural language understanding model with a specific-shopping named entity recognizer using a joint learning-based BERT transformer for task-oriented dialogue systems in the Persian Language. Since there is no published available dataset for Persian online shopping dialogue systems, to tackle the lack of data, we propose two methods for generating training data: fully-simulated and semi-simulated method. We created a simulated dataset with a hybrid of rule-based and template-based generation methods and a semi-simulated dataset where the language generation part is done by a human to increase the quality of the dataset. Our experiments withthe natural language understanding module show that a combination of the datasets can improve results. these dataset generation methods can apply in other domains for low-resource languages in task-oriented dialogue systems too to solve the cold start problem of datasets.
Electricity theft causes significant harm to social and economic development. In recent years, as a powerful technique in data mining, deep learning has attached much attention and become popular in electricity consum...
详细信息
ISBN:
(数字)9781728165790
ISBN:
(纸本)9781728165806
Electricity theft causes significant harm to social and economic development. In recent years, as a powerful technique in data mining, deep learning has attached much attention and become popular in electricity consumption sequence analysis. Nevertheless, existing methods mainly focus on short-term numerical data modeling, while the records in real-world scenarios (1) usually consist of multiple temporal features and (2) are often of large scale. In this paper, to overcome the two fundamental challenges, we propose a novel method called Deep Attention-based Neural Network for Electricity theft Detection (DANN-ETD). Specifically, we first respectively decompose the electricity sequences into the trend, seasonal and residual views to fully exploit the temporal features. To effectively and efficiently model the large-scale time series, we then split the series into several snapshots and further design the deep attention-based recurrent neural networks which can detect the fine-grained evolution of electricity consumption. Experimental results on realworld datasets demonstrate that our method outperforms the state of the arts.
the following topics are dealt with: mobile robots; learning (artificial intelligence); neural nets; object detection; pattern classification; convolutional neural nets; feature extraction; control engineering computi...
the following topics are dealt with: mobile robots; learning (artificial intelligence); neural nets; object detection; pattern classification; convolutional neural nets; feature extraction; control engineering computing; optimisation; pattern clustering.
the proceedings contain 52 papers. the topics discussed include: a novel method of suppressing multipath interference based on brewster effect;self-similarity analysis of sea clutter under the existence of different m...
ISBN:
(纸本)9781510635456
the proceedings contain 52 papers. the topics discussed include: a novel method of suppressing multipath interference based on brewster effect;self-similarity analysis of sea clutter under the existence of different moving target;online recovery of time-varying signals based on sparse bayesian learning;feature extraction and classification of UAV’s acoustic signal using 4-microphones array in a real noisy environment;a rotor feature extraction method with clutter suppression and high precision;frame-level speech enhancement based on Wasserstein GAN;and acoustic spectrum and signature analysis on underwater radiated noise of a passenger ship target based on the measured data.
Agriculture is the foundation factor of many developing countries' economy. Now we are in the era of industrial revolution and especially the developing countries are shifted their vision completely to this revolu...
详细信息
ISBN:
(数字)9781728168517
ISBN:
(纸本)9781728168524
Agriculture is the foundation factor of many developing countries' economy. Now we are in the era of industrial revolution and especially the developing countries are shifted their vision completely to this revolution. But, they are not thinking about smart cultivation and more production from this ancient key factor of economic growth. Still farmers are following the ancient techniques for cultivation without thinking different dimensions or key factors for better production. Farmers should understand the present context for cultivating any crops to get proper profit against any specific cultivation. And to do this study they have to analysis statistical data of production report over a long period considering climate, region, availability of pesticides, period specific demand and so many dimensions. So we have to employ the context-aware computing to estimate the reasonable balanced production of any crops. In many countries like; Bangladesh in every cultivation season many farmers are not getting proper price of their crops due to over production. Sometimes farmers fail to return their invested money for their cultivation where their time and physical effort is completely a loss. To address this vital issue our research work will do proper context-aware analysis. this paper's agricultural context-aware computing for balanced production deals with extract-transform-loading (ETL), data warehousing (DWH), dimension (period, weather, pesticides etc.) specific data visualization, and supervised learning process to estimate future production.
暂无评论