Relationship detection based on object semantic inference and attention mechanisms

Detecting relations among objects is a crucial task for image understanding. However, each relationship involves different objects pair combinations, and different objects pair combinations express diverse interactions. This makes the relationships, based just on visual features, a challenging task. In this paper, we propose a simple yet effective relationship detection model, which is based on object semantic inference and attention mechanisms. Our model is trained to detect relation triples, such as , . To overcome the high diversity of visual appearances, the semantic inference module and the visual features are combined to complement each others. We also introduce two different attention mechanisms for object feature refinement and phrase feature refinement. In order to derive a more detailed and comprehensive representation for each object, the object feature refinement module refines the representation of each object by querying over all the other objects in the image. The phrase feature refinement module is proposed in order to make the phrase feature more effective, and to automatically focus on relative parts, to improve the visual relationship detection task. We validate our model on Visual Genome Relationship dataset. Our proposed model achieves competitive results compared to the state-of-the-art method MOTIFNET

Learning-based composite metrics for improved caption evaluation

The evaluation of image caption quality is a challenging task, which requires the assessment of two main aspects in a caption: adequacy and fluency. These quality aspects can be judged using a combination of several linguistic features. However, most of the current image captioning metrics focus only on specific linguistic facets, such as the lexical or semantic, and fail to meet a satisfactory level of correlation with human judgements at the sentence-level. We propose a learning-based framework to incorporate the scores of a set of lexical and semantic metrics as features, to capture the adequacy and fluency of captions at different linguistic levels. Our experimental results demonstrate that composite metrics draw upon the strengths of standalone measures to yield improved correlation and accuracy

Novel deep learning approach to model and predict the spread of COVID-19

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally, producing new variants and has become a pandemic. People have lost their lives not only due to the virus but also because of the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop robust artificial intelligence techniques to predict the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models are trained and tested on publicly available novel coronavirus dataset. The proposed models are evaluated by using Mean Absolute Error and compared with the existing methods for the prediction of the spread of COVID-19. Our experimental results demonstrate the superior prediction performance of the proposed models. The proposed DSPM and NRM achieve MAEs of 388.43 (error rate 1.6%) and 142.23 (0.6%), respectively compared to 6508.22 (27%) achieved by baseline SVM, 891.13 (9.2%) by Time-Series Model (TSM), 615.25 (7.4%) by LSTM-based Data-Driven Estimation Method (DDEM) and 929.72 (8.1%) by Maximum-Hasting Estimation Method (MHEM)

Synthesis and molecular docking of new hydrazones derived from ethyl isonipecotate and their biological activities

Purpose: To investigate the antibacterial and α-glucosidase inhibitory activities of hydrazone derivatives (8a-h) of ethyl isonipecotate.Methods: The reaction of ethyl isonipecotate (2) with 3,5-dichloro-2-hydroxybenzenesulfonyl chloride (1) in an aqueous basic medium yielded ethyl 1-[(3,5-dichloro-2-hydroxyphenyl)sulfonyl]piperidin-4- carboxylate (3). Compound 3 was subsequently converted to ethyl 1-[(3,5-dichloro-2-ethoxyphenyl) sulfonyl]piperidin-4-carboxylate (5) via O-alkylation. Compound 5 on reaction with hydrated hydrazine yielded 1-[(3,5-dichloro-2-ethoxyphenyl)sulfonyl]piperidin-4-carbohyrazide (6) in MeOH. Target compounds 8a-h were synthesized by stirring 6 with different aromatic aldehydes (7a-h) in MeOH. All the synthesized compounds were structurally elucidated by proton nuclear magnetic resonance (1H-NMR), electron impact mass spectrometry (EI-MS) and infrared (IR) spectroscopy. For antibacterial activity, solutions of the synthesized compounds were mixed with bacterial strains, and the change in absorbance before and after incubation was determined. For enzyme inhibitory activity, change in the absorbance of mixtures of synthesized compounds and enzyme before and after incubation with substrate was determined.Results: The target compounds were synthesized in appreciable yields and well characterized by spectral data analysis. Salmonella typhi was inhibited by 8e (MIC 8.00 ± 0.54 μM), Escherichia coli by 8f (8.21 ± 0.83 μM), Bacillus subtilis by 8c (8.56 ± 0.63 μM) and Staphylococcus aureus by 8c (8.86 ± 0.29 μM). Two compounds, 8e and 8d, were very effective inhibitors of α-glucosidase with IC50 values of 40.62 ± 0.07 and 48.64 ± 0.08 μM, respectively.Conclusion: Low IC50  values of the synthesized compounds against α-glucosidase demonstrates their potential in type-2 diabetes treatment. Furthermore, these compounds exhibit substantial antibacterial activity against the bacterial strains tested.Keywords: Antibacterial activity, α-Glucosidase inhibition, Ethyl isonipecotate, Hydrazo

Real Time Surveillance for Low Resolution and Limited-Data Scenarios: An Image Set Classification Approach

This paper proposes a novel image set classification technique based on the concept of linear regression. Unlike most other approaches, the proposed technique does not involve any training or feature extraction. The gallery image sets are represented as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate regression models for each image of the test image set. Images of the test set are then projected on the gallery subspaces. Residuals, calculated using the Euclidean distance between the original and the projected test images, are used as the distance metric. Three different strategies are devised to decide on the final class of the test image set. We performed extensive evaluations of the proposed technique under the challenges of low resolution, noise and less gallery data for the tasks of surveillance, video-based face recognition and object recognition. Experiments show that the proposed technique achieves a better classification accuracy and a faster execution time compared to existing techniques especially under the challenging conditions of low resolution and small gallery and test data

Attention in Convolutional LSTM for Gesture Recognition

Convolutional long short-term memory (LSTM) networks have been widely used for action/gesture recognition, and different attention mechanisms have also been embedded into the LSTM or the convolutional LSTM (ConvLSTM) networks. Based on the previous gesture recognition architectures which combine the threedimensional convolution neural network (3DCNN) and ConvLSTM, this paper explores the effects of attention mechanism in ConvLSTM. Several variants of ConvLSTM are evaluated: (a) Removing the convolutional structures of the three gates in ConvLSTM, (b) Applying the attention mechanism on the input of ConvLSTM, (c) Reconstructing the input and (d) output gates respectively with the modified channel-wise attention mechanism. The evaluation results demonstrate that the spatial convolutions in the three gates scarcely contribute to the spatiotemporal feature fusion, and the attention mechanisms embedded into the input and output gates cannot improve the feature fusion. In other words, ConvLSTM mainly contributes to the temporal fusion along with the recurrent steps to learn the long-term spatiotemporal features, when taking as input the spatial or spatiotemporal features. On this basis, a new variant of LSTM is derived, in which the convolutional structures are only embedded into the input-to-state transition of LSTM. The code of the LSTM variants is publicly available2

Biological screening and docking studies of unique hybrids synthesized by conventional versus microwave assisted techniques

Purpose: To carry out the synthesis of various hybrids of 1,2,4-triazole in search of potential therapeutic enzyme inhibitory agents, and carry out docking and bovine serum albumin (BSA) binding studies on docking and bovine serum albumin (BSA) binding studies on the hybrids. Methods: The target compounds were synthesized by following a multistep protocol. Compound 1 was synthesized from 4-methoxybenzenesulfonyl chloride (a) and ethyl isonipecotate (b). Compound 1 was refluxed with hydrazine to synthesize compound 2, which was converted to compound 3 through two consecutive steps. Compound 4 and different amines (5a-5i), were utilized to synthesize an array of electrophiles (6a-6i). A series of 1,2,4-triazole hybrids (7a-7i) were synthesized at room temperature by stirring together 3 and 6a-6i. The final structures of 7a-7i were elucidated through 1H-NMR, 13C-NMR and EI-MS spectroscopy. The BSA binding studies were performed by fluorometric titration. Furthermore, antioxidant and enzyme inhibition activities were determined colorimetrically. Results: Compound 7d was the most active antioxidant agent, compared to butylated hydroxyanisole (BHA), while compounds 7d, 7e, 7f, 7g and 7i proved to be potent urease inhibitors with half-maximal inhibitory concentration (IC50) values of 19.5 ± 0.12, 21.1 ± 0.68, 18.2 ± 0.78, 19.9 ± 0.77 and 17.9 ± 0.10 µM, respectively, compared to thiourea with an IC50 of 24.3 ± 0.24 µM. Compounds 7a, 7b, 7d, and 7e exhibited high butyrylcholinesterase inhibition potential, compared to eserine. Conclusion: The synthesized compounds require studies further as potential therapeutic enzyme inhibitory agents in view of their urease inhibition as well as antioxidant activity

The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. METHODS: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. FINDINGS: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). INTERPRETATION: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden

Automatic number plate recognition: A detailed survey of relevant algorithms

Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area

Spatial hierarchical analysis deep neural network for RGB-D object recognition

Deep learning based object recognition methods have achieved unprecedented success in the recent years. However, this level of success is yet to be achieved on multimodal RGB-D images. The latter can play an important role in several computer vision and robotics applications. In this paper, we present spatial hierarchical analysis deep neural network, called ShaNet, for RGB-D object recognition. Our network consists of convolutional neural network (CNN) and recurrent neural network (RNNs) to analyse and learn distinctive and translationally invariant features in a hierarchical fashion. Unlike existing methods, which employ pre-trained models or rely on transfer learning, our proposed network is trained from scratch on RGB-D data. The proposed model has been tested on two different publicly available RGB-D datasets including Washington RGB-D and 2D3D object dataset. Our experimental results show that the proposed deep neural network achieves superior performance compared to existing RGB-D object recognition methods