Real world music object recognition

We present solutions to two of the most pressing issues in contemporary optical music recognition (OMR).We improve recognition accuracy on low-quality, real-world (i.e. containing ageing, lighting, or dirt artefacts among others) input data and provide confidence-rated model outputs to enable efficient human post-processing. Specifically, we present (i) a sophisticated input augmentation scheme that can reduce the gap between sanitised benchmarks and realistic tasks through a combination of synthetic data and noisy perturbations of real-world documents; (ii) an adversarial discriminative domain adaptation method that can be employed to improve the performance of OMR systems on low-quality data; (iii) a combination of model ensembles and prediction fusion, which generates trustworthy confidence ratings for each prediction. We evaluate our contributions on a newly created test set consisting of manually annotated pages of varying real-world quality, sourced from International Music Score Library Project (IMSLP) / the Petrucci Music Library. With the presented data augmentation scheme, we achieve a doubling in detection performance from 36.0% to 73.3% on noisy real-world data compared to state-of-the-art training. This result is then combined with robust confidence ratings paving the way forOMR to be deployed in the realworld. Additionally, we showthe merits of unsupervised adversarial domain adaptation for OMR raising the 36.0% baseline to 48.9%. All our code and data are freely available at: https://github.com/raember/s2anet/tree/TISMIR_publication

Irony Detection in Bengali Tweets: A New Dataset, Experimentation and Results

Part 1: Computational Intelligence for Text AnalysisInternational audienceIrony detection is a difficult task because the intended meaning of a sentence differs from the literal meaning or sentiment of that sentence. Most existing work on this subject has focused on irony detection in the English language. Since no public dataset is available for this task in the Bengali domain, we have created a Bengali irony detection dataset that contains a total of 1500 labeled Bengali tweets. This paper presents the description of the Bengali irony detection dataset developed by us and reports some results obtained on our Bengali irony dataset using several widely used machine learning algorithms such as Naïve Bayes, Support Vector Machine, K-Nearest Neighbor and Random Forest

Relation preserving triplet mining for stabilising the triplet loss in re-identification systems

Object appearances change dramatically with pose variations. This creates a challenge for embedding schemes that seek to map instances with the same object ID to locations that are as close as possible. This issue becomes significantly heightened in complex computer vision tasks such as re-identification(reID). In this paper, we suggest that these dramatic appearance changes are indications that an object ID is composed of multiple natural groups, and it is counterproductive to forcefully map instances from different groups to a common location. This leads us to introduce Relation Preserving Triplet Mining (RPTM), a feature-matching guided triplet mining scheme, that ensures that triplets will respect the natural subgroupings within an object ID. We use this triplet mining mechanism to establish a pose-aware, well-conditioned triplet loss by implicitly enforcing view consistency. This allows a single network to be trained with fixed parameters across datasets while providing state-of-the-art results. Code is available at https://github.com/adhirajghosh/RPTM_reid.Comment: WACV 202

Green conversion of graphene oxide to graphene nanosheets and its biosafety study.

Chemical reduction of graphene oxide (GO) to graphene employs the use of toxic and environmentally harmful reducing agents, hindering mass production of graphene which is of tremendous technological importance. In this study we report a green approach to the synthesis of graphene, bio-reduced by crude polysaccharide. The polysaccharide reduces exfoliated GO to graphene at room temperature in an aqueous medium. Transmission electron microscopy image provides clear evidence for the formation of few layer graphene. Characterization of the resulting polysaccharide reduced GO by Raman spectroscopy, Fourier transform infrared spectroscopy and Energy dispersive X-ray analysis confirms reduction of GO to graphene. We also investigated the degree of biosafety of the reduced GO and found it to be safe under 100 μg/ml

A mononuclear N,N,N,O donor schiff base Cu (II) complex inhibits bacterial biofilm formation and promotes apoptosis and cell cycle arrest in prostate cancer cells

In this work, we report a distorted square pyramidal mononuclear copper (II) complex [Cu(L)(NCS)] (1) which was obtained by the reaction of the aqueous solution of ammonium thiocyanate to a methanolic solution of copper nitrate trihydrate and corresponding Schiff-base ligands. Schiff bases, HL (C12H19N3O) act as a tetradentate Schiff base, derived from 1:1 condensation of o-hydroxyacetophenone and diethylenetriamine. The synthesized complex has been successfully characterized based on elemental analysis and Infrared (IR) spectroscopy. The structure of complex 1 was confirmed by single-crystal X-ray diffraction study. In our study, we investigated synthesis, structural characterization, antimicrobial, anti-biofilm, and anti-cancer activity, and plausible mechanism of action of a novel mononuclear copper (II) schiff base complex. Increasing microbial resistance to several commercially available or traditional antimicrobial compounds has become a major global health concern at present time. The mononuclear copper (II) complex exhibited potential antibacterial activity against two strains of the gram-negative pathogen Pseudomonas aeruginosa. The copper compound dependent damage of bacterial cell membrane and inhibition of bacterial biofilm formation were also identified. Moreover, complex 1 inhibited prostate cancer cell growth, and migration by inducing apoptosis and arresting the cell cycle at the G2M phase. Based on the results, we are suggesting our novel mononuclear copper (II) compound as a potential candidate for the development of new antibacterial and anti-cancer drugs

From left, dispersed and exfoliated graphene oxide, aqueous solution of the polysaccharide, polysaccharide-reduced graphene oxide.

<p>From left, dispersed and exfoliated graphene oxide, aqueous solution of the polysaccharide, polysaccharide-reduced graphene oxide.</p

Green conversion of graphene oxide to graphene nanosheets and its biosafety study - Fig 8

<p>Raman Spectrum of PRGO (Curve A) and GO (Curve B).</p

XRD spectrum of PR-GO nanosheets.

<p>XRD spectrum of PR-GO nanosheets.</p

DLS elucidation of the synthesized PR-GO.

<p>DLS elucidation of the synthesized PR-GO.</p

ROS generation in human PBMCs induced by PR-GO.

<p>ROS generation in human PBMCs induced by PR-GO.</p