Line Probe Assay (LPA) is a widely used method for diagnosing drug-resistant tuberculosis (DRTB), but it is a time-consuming and labor-intensive process that requires expert interpretation. DRTB is a significant threat to global TB control efforts and its prompt diagnosis is critical for initiating appropriate treatment. In this paper, we present an automated LPA test interpretation solution that uses computer vision techniques to extract and analyze strips from LPA sheets and uses machine learning algorithms to produce drug sensitivity and resistivity outcomes with extremely high precision and recall. We also develop OCR models to eliminate manual data entry to further reduce the overall time. Our solution comprises a rejection module that flags ambiguous and novel samples that are then referred to experienced lab technicians. This results in increased trust in the solution. To evaluate our solution, we curate an extensive and diverse dataset of LPA strips annotated by multiple microbiologists across India. Our solution achieves more than 95% accuracy for all drugs on this dataset. The proposed solution has the potential to increase the efficiency, standardization of LPA test interpretation, and fast-tracking the dissemination of results to end-users via a designated Management Information System (MIS).

The recent emergence of multilingual pretraining language model (mPLM) has enabled breakthroughs on various downstream cross-lingual transfer (CLT) tasks. However, mPLM-based methods usually involve two problems: (1) simply fine-tuning may not adapt general-purpose multilingual representations to be task-aware on low-resource languages; (2) ignore how cross-lingual adaptation happens for downstream tasks. To address the issues, we propose a meta graph learning (MGL) method. Unlike prior works that transfer from scratch, MGL can learn to cross-lingual transfer by extracting meta-knowledge from historical CLT experiences (tasks), making mPLM insensitive to low-resource languages. Besides, for each CLT task, MGL formulates its transfer process as information propagation over a dynamic graph, where the geometric structure can automatically capture intrinsic language relationships to guide cross-lingual transfer explicitly. Empirically, extensive experiments on both public and real-world datasets demonstrate the effectiveness of the MGL method. © 2020 Association for Computational Linguistics

Packaging is one of the most important aspects in the food industry. The problems faced during packaging can classified into two categories, defects in the packaging before the substrate is filled and after the substrate is filled. There are methods to determine the defects in the food containers after the food has been packed by means of measuring the phenomenon caused by the defects such as use of moisture sensor for detecting any leaking or the use of pH indicators. But these methods, although cost effective, slows down the plant as well as increases the risk of damaging the system. In this paper, we have implemented a defect detection and rectification system using image processing on tin cans to check for any irregularities apart from the ones that are intended to be on it. The decision-making capability is provided by a classifier neural network. The case study takes up the problems faced in coconut oil industry. A similar set up can be used after the package to detect if any defect is present. For other fluids such as soft drinks and other drinks a pH indicator can be used for detecting defects.

This paper describes a data aggregation and consensus algorithm for object location and tracking applications deployed on wireless sensor networks. This consensus algorithm permits ad-hoc, in-network, group formation in response to a detected event. By reaching a consensus in the network, only a single message indicating the detected event needs to be forwarded to the tracking application at a base station, leading to significant savings in communication costs and prolonging of the life of the network. Our ad-hoc approach offers superior detecting power to other approaches, which statically partition the network into detection regions. The use of a quorum mechanism to achieve consensus leads to higher availability under failure conditions. Moreover, the algorithm exploits the broadcast communication and synchrony properties of wireless sensor networks. Our experimental results show that the algorithm exhibits low overhead despite faults and converges quickly to a single consensus even if multiple nodes initiate the consensus protocol.