Advantage Of Different Features Research Articles

A Non-Intrusive Load Monitoring Method Based on Feature Fusion and SE-ResNet

In the study of non-intrusive load monitoring, using a single feature for identification can lead to insignificant differentiation of similar loads; however, multi-feature fusion can pool the advantages of different features to improve identification accuracy. Based on this, this paper proposes a recognition method based on feature fusion and matrix heat maps, using V-I traces, phase and amplitude of odd harmonics, and fundamental amplitude. These are converted into matrix heat maps, which can retain both large and small eigenvalues of the same feature for different loads and can retain different features. The matrix heat map is recognized by using SE-ResNet18, which avoids the problem of the classical CNN depth being too deep, causing network degradation and being difficult to train, and achieves trauma-free monitoring of home loads. Finally, the model is validated using the PLAID and REDD datasets, and the average recognition accuracy is 96.24% and 96.4%, respectively, with significant recognition effects for loads with similar V-I trajectories and multi-state loads.

Learning multi-scale features for speech emotion recognition with connection attention mechanism

Speech emotion recognition (SER) has become a crucial topic in the field of human–computer interactions. Feature representation plays an important role in SER, but there are still many challenges in feature representation such as the inability to predict which features are most effective for SER and the cultural differences in emotion expression. Most previous studies use a single type of feature for the recognition task or conduct early fusion of features. However, a single type of feature cannot well reflect the emotions of speech signals. Also, different features contain different information, direct fusion cannot integrate the advantages of different features. To overcome these challenges, this paper proposes a parallel network for multi-scale SER based on a connection attention mechanism (AMSNet). AMSNet fuses fine-grained frame-level manual features with coarse-grained utterance-level deep features. Meanwhile, it adopts different speech emotion feature extraction modules according to the temporal and spatial features of speech signals, which enriches features and improves feature characterization. The network consists of a frame-level representation learning module (FRLM) based on the time structure and an utterance-level representation learning module (URLM) based on the global structure. Besides, improved attention-based long short-term memory (LSTM) is introduced into FRLM to focus on the frames that contribute more to the final emotion recognition result. In URLM, a convolutional neural network with the squeeze-and-excitation block (SCNN) is introduced to extract deep features. In addition, the connection attention mechanism is proposed for feature fusion, which applies different weights to different features. Extensive experiments are conducted on the IEMOCAP and EmoDB datasets, and the results demonstrate the effectiveness and performance superiority of AMSNet. Our code will be publicly available at https://codeocean.com/capsule/8636967/tree/v1.

Design of a Sustainable Blockchain-Oriented Software for Building Workers Management

The building workers sector is one of the most challenging for Human Resources (HR) management. In this work we propose a solution relying on the Blockchain technology and present the design of a Blockchain-Oriented Software system conceived for managing the building workers sector with a focus on workers' safety and guided by sustainable and Agile Methodologies in software design. The proposed approach takes advantage of different features of the Blockchain technology and provides transparency for labour inspectors, grants data integrity and immutability, relies in tamper proof time stamps for any recorded activity, allows the implementation of Smart Contracts where clauses are automatically respected without the need of a trusted control authority, acknowledges the legal requirements in the field, including the possibility of creating the Operational Safety Plans, which construction companies have to provide and finally implements the creation of vacant job positions which workers can find and apply to. In order to achieve these goals we adopt the Blockchain-Oriented Software Engineering (BOSE) methodology to design Blockchain software applications and apply an Agile methodology centered on Blockchain Software development, (called ABCDE) for the design and development of the decentralized application. Such methodology allows to center the software development around the actors of the system in the specific domain, such as Building Workers, Construction Companies, Labour Inspectors and so on. In addition we rely on the software sustainability analysis, based on the five dimensions of sustainability, to evaluate the approach and to avoid mistakes in the system development. We design system elements with specific diagrams and we divided our system in the on-chain and the out-of-chain components. The implementation of the system, done by using Ethereum and the ERC721 standard, allows us to improve some aspect of the design, to know the deployment and usage costs, and to evaluate the effect of the user interface. Finally, we discuss about the effects of our system and its sustainability, and provide a comparison of our system with a similar per aims but centralized system.

Identifying short disorder-to-order binding regions in disordered proteins with a deep convolutional neural network method.

Molecular recognition features (MoRFs) are key functional regions of intrinsically disordered proteins (IDPs), which play important roles in the molecular interaction network of cells and are implicated in many serious human diseases. Identifying MoRFs is essential for both functional studies of IDPs and drug design. This study adopts the cutting-edge machine learning method of artificial intelligence to develop a powerful model for improving MoRFs prediction. We proposed a method, named as en_DCNNMoRF (ensemble deep convolutional neural network-based MoRF predictor). It combines the outcomes of two independent deep convolutional neural network (DCNN) classifiers that take advantage of different features. The first, DCNNMoRF1, employs position-specific scoring matrix (PSSM) and 22 types of amino acid-related factors to describe protein sequences. The second, DCNNMoRF2, employs PSSM and 13 types of amino acid indexes to describe protein sequences. For both single classifiers, DCNN with a novel two-dimensional attention mechanism was adopted, and an average strategy was added to further process the output probabilities of each DCNN model. Finally, en_DCNNMoRF combined the two models by averaging their final scores. When compared with other well-known tools applied to the same datasets, the accuracy of the novel proposed method was comparable with that of state-of-the-art methods. The related web server can be accessed freely via http://vivace.bi.a.u-tokyo.ac.jp:8008/fang/en_MoRFs.php .

Facial Expression Recognition Based on Fusion Features of Center-Symmetric Local Signal Magnitude Pattern

Local feature descriptors play a fundamental and important role in facial expression recognition. This paper presents a new descriptor, Center-Symmetric Local Signal Magnitude Pattern (CS-LSMP), which is used for extracting texture features from facial images. CS-LSMP operator takes signal and magnitude information of local regions into account compared to conventional LBP-based operators. Additionally, due to the limitation of single feature extraction method and in order to make full advantages of different features, this paper employs CS-LSMP operator to extract features from Orientational Magnitude Feature Maps (OMFMs), Positive-and-Negative Magnitude Feature Maps (PNMFMs), Gabor Feature Maps (GFMs) and facial patches (eyebrows-eyes, mouths) for obtaining fused features. Unlike HOG, which only retains horizontal and vertical magnitudes, our work generates Orientational Magnitude Feature Maps (OMFMs) by expanding multi-orientations. This paper build two distinct feature maps by dividing local magnitudes into two groups, i.e., positive and negative magnitude feature maps. The generated Gabor Feature Maps (GFMs) are also grouped to reduce the computational complexity. Experiments on the JAFFE and CK+ facial expression datasets showed that the proposed framework achieved significant improvement and outperformed some state-of-the-art methods.

Separating astrophysical sources from indirect dark matter signals

Indirect searches for products of dark matter annihilation and decay face the challenge of identifying an uncertain and subdominant signal in the presence of uncertain backgrounds. Two valuable approaches to this problem are (i) using analysis methods which take advantage of different features in the energy spectrum and angular distribution of the signal and backgrounds and (ii) more accurately characterizing backgrounds, which allows for more robust identification of possible signals. These two approaches are complementary and can be significantly strengthened when used together. I review the status of indirect searches with gamma rays using two promising targets, the Inner Galaxy and the isotropic gamma-ray background. For both targets, uncertainties in the properties of backgrounds are a major limitation to the sensitivity of indirect searches. I then highlight approaches which can enhance the sensitivity of indirect searches using these targets.

Application of Evidence Reasoning in Multi-Class Image Steganalysis

In steganalysis, it is important to ascertain the stego algorithm. In this paper, a blind steganalysis scheme based on evidence reasoning, which is capable of assinging stego images to known steganagraphic programs, is put forward. The evidences are extracted from the dct domain, pixel domain and dwt domain. The BPA function is obtained by transforming the svm based a posterior class probability. Three different scheme are tested and compared. Experimental results reveal the fact that our scheme is more effective than others and applying the evidence reasoning into steganalysis can integrate the advantages of different features, and improve the detection rate accordingly

Application of Evidence Reasoning in Multi-Class Image Steganalysis

In steganalysis, it is important to ascertain the stego algorithm. In this paper, a blind steganalysis scheme based on evidence reasoning, which is capable of assinging stego images to known steganagraphic programs, is put forward. The evidences are extracted from the dct domain, pixel domain and dwt domain. The BPA function is obtained by transforming the svm based a posterior class probability. Three different scheme are tested and compared. Experimental results reveal the fact that our scheme is more effective than others and applying the evidence reasoning into steganalysis can integrate the advantages of different features, and improve the detection rate accordingly

An Analysis of Normalization Methods for Drosophila RNAi Genomic Screens and Development of a Robust Validation Scheme

Genome-wide RNA interference (RNAi) screening allows investigation of the role of individual genes in a process of choice. Most RNAi screens identify a large number of genes with a continuous gradient in the assessed phenotype. Screeners must decide whether to examine genes with the most robust phenotype or the full gradient of genes that cause an effect and how to identify candidate genes. The authors have used RNAi in Drosophila cells to examine viability in a 384-well plate format and compare 2 screens, untreated control and treatment. They compare multiple normalization methods, which take advantage of different features within the data, including quantile normalization, background subtraction, scaling, cellHTS2 (Boutros et al. 2006), and interquartile range measurement. Considering the false-positive potential that arises from RNAi technology, a robust validation method was designed for the purpose of gene selection for future investigations. In a retrospective analysis, the authors describe the use of validation data to evaluate each normalization method. Although no method worked ideally, a combination of 2 methods, background subtraction followed by quantile normalization and cellHTS2, at different thresholds, captures the most dependable and diverse candidate genes. Thresholds are suggested depending on whether a few candidate genes are desired or a more extensive systems-level analysis is sought. The normalization approaches and experimental design to perform validation experiments are likely to apply to those high-throughput screening systems attempting to identify genes for systems-level analysis.

The role of apoptotic mimicry in host-parasite interplay: is death the only alternative for altruistic behavior?

The role of apoptotic mimicry in host-parasite interplay: is death the only alternative for altruistic behavior?