Simple Random Sampling-Based Probe Station Selection for Fault Detection in Wireless Sensor Networks

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate

Efficient Probing Techniques for Fault Diagnosis

Abstract — Increase in the network usage and the widespread application of networks for more and more performance critical applications has caused a demand for tools that can monitor network health with minimum management traffic. Adaptive probing holds a potential to provide effective tools for end-toend monitoring and fault diagnosis over a network. In this paper we present adaptive probing tools that meet the requirements to provide an effective and efficient solution for fault diagnosis. In this paper, we propose adaptive probing based algorithms to perform fault localization by adapting the probe set to localize the faults in the network. We compare the performance and efficiency of the proposed algorithms through simulation results

Human-in-the-loop: Role in Cyber Physical Agricultural Systems

With increasing automation, the ‘human’ element in industrial systems is gradually being reduced, often for the sake of standardization. Complete automation, however, might not be optimal in complex, uncertain environments due to the dynamic and unstructured nature of interactions. Leveraging human perception and cognition can prove fruitful in making automated systems robust and sustainable. “Human-in-the-loop” (HITL) systems are systems which incorporate meaningful human interactions into the workflow. Agricultural Robotic Systems (ARS), developed for the timely detection and prevention of diseases in agricultural crops, are an example of cyber-physical systems where HITL augmentation can provide improved detection capabilities and system performance. Humans can apply their domain knowledge and diagnostic skills to fill in the knowledge gaps present in agricultural robotics and make them more resilient to variability. Owing to the multi-agent nature of ARS, HUB-CI, a collaborative platform for the optimization of interactions between agents is emulated to direct workflow logic. The challenge remains in designing and integrating human roles and tasks in the automated loop. This article explains the development of a HITL simulation for ARS, by first realistically modeling human agents, and exploring two different modes by which they can be integrated into the loop: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks, and classification accuracy are measured and compared for different collaboration protocols. The results show the statistically significant advantages of HUB-CI protocols over the traditional protocols for each integration, while also discussing the competitive factors of both integration modes. Strengthening human modeling and expanding the range of human activities within the loop can help improve the practicality and accuracy of the simulation in replicating a HITL-ARS

Pesan Emosional Fear Appeals dalam Produk Kecantikan Innisfree terhadap Sikap Konsumen Mendukung Pemakaian Produk Ramah Lingkungan

Penelitian ini dilatarbelakangi oleh green message mencakup green message abstractness dan green message framing, sering dilakukan brand kecantikan seperti Innisfree dalam melakukan kampanye ramah lingkungan. Untuk itu, tujuan penelitian ini adalah untuk menguji apakah green message pada pesan kampanye Innisfree akan bisa secara signifikan mempengaruhi sikap konsumen dalam menggunakan produk ramah lingkungan dan seberapa besar pengaruhnya. Konsep yang digunakan pada penelitian ini yaitu green message terdiri dari green message abstractness dan green message framing, serta konsep sikap konsumen. Penelitian ini menggunakan pendekatan kuantitatif yang bersifat eksplanatif, dengan metode survei. Populasi adalah followers akun Instagram @innisfreeindonesia dengan mengambil sampel sebanyak 200 responden secara purposive sampling. Data dianalisis dengan menggunakan regresi linear sederhana. Hasil penelitian menunjukkan bahwa terdapat pengaruh yang signifikan dari green message terhadap sikap konsumen sebesar 43.4%. Kesimpulannya, hasil penelitian ini merekomendasikan bagi kampanye brand yang mengaitkan dengan lingkungan untuk mengubah sikap, bahwa green message yang mencakup green message abstractness dan green message framing akan bisa digunakan secara lebih meluas

CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering

Videos often capture objects, their visible properties, their motion, and the interactions between different objects. Objects also have physical properties such as mass, which the imaging pipeline is unable to directly capture. However, these properties can be estimated by utilizing cues from relative object motion and the dynamics introduced by collisions. In this paper, we introduce CRIPP-VQA, a new video question answering dataset for reasoning about the implicit physical properties of objects in a scene. CRIPP-VQA contains videos of objects in motion, annotated with questions that involve counterfactual reasoning about the effect of actions, questions about planning in order to reach a goal, and descriptive questions about visible properties of objects. The CRIPP-VQA test set enables evaluation under several out-of-distribution settings -- videos with objects with masses, coefficients of friction, and initial velocities that are not observed in the training distribution. Our experiments reveal a surprising and significant performance gap in terms of answering questions about implicit properties (the focus of this paper) and explicit properties of objects (the focus of prior work).Comment: Accepted to EMNLP 2022; https://maitreyapatel.com/CRIPP-VQA

Efecto de la Glicina sobre la incidencia del síndrome de resección transuretral: Revisión sistemática

Evaluar el efecto de la glicina sobre la incidencia del síndrome de resección transuretral después de RTUP. Métodos: Búsqueda de artículos en 5 bases de datos (Pubmed, Scopus, Web of Science, Ebsco, Ovid) que cumplieron los criterios de selección. El riesgo de sesgo se evaluó con la herramienta de la colaboración Cochrane para ensayos controlados aleatorios (ECA). Las variables primarias fueron: síndrome de resección transuretral y glicina; las variables secundarias que se evaluaron son: edad, tiempo operatorio, cantidad de tejido resecado. Resultados: Se encontró 781 artículos; sin embargo, sólo 5 ensayos clínicos aleatorios (ECAs) cumplieron los criterios de selección, con una población total de 868 pacientes. Se dividió en dos grupos: en el primer grupo se utilizó glicina (n=380) y en el segundo glucosa 5% y/o solución salina 0.9% (n= 488). La incidencia de síndrome de resección transuretral se dio en 27 (7.1%) pacientes del grupo de glicina y ningún caso en el segundo grupo; se determinó un RR= 8.02 (p= 0,002) en el grupo de glicina, demostrando que su uso incrementa en 8 veces el riesgo de síndrome de resección transuretral. La influencia de la edad (p=0.85); tiempo operatorio (p=0.82) y cantidad de tejido resecado (p=0.96), en ambos grupos no mostró diferencias significativas en el desarrollo del síndrome de resección transuretral. Conclusiones: La glicina en comparación con otros irrigantes en la RTUP, incrementa la incidencia del síndrome de resección transuretral. La edad, tiempo operatorio y la cantidad de tejido resecado, no influyen en el desarrollo del síndrome de resección transuretraTo evaluate the effect of glycine on the incidence of transurethral resection syndrome after TURP. Methods: Search of articles in 5 databases (Pubmed, Scopus, Web of Science, Ebsco, Ovid) that met the selection criteria. Risk of bias was assessed with the Cochrane collaboration tool for randomized controlled trials (RCTs). The primary variables were transurethral resection syndrome and glycine; the secondary variables evaluated were age, operative time, amount of resected tissue. Results: We found 781 articles; however, only 5 randomized clinical trials (RCTs) met the selection criteria, with a total population of 868 patients. It was divided into two groups: in the first group, glycine was used (n=380) and in the second group glucose 5% and/or saline 0.9% (n= 488). The incidence of transurethral resection syndrome occurred in 27 (7.1%) patients in the glycine group and no cases in the second group; an RR= 8.02 (p= 0.002) was determined in the glycine group, demonstrating that its use increases the risk of transurethral resection syndrome by 8 times. The influence of age (p=0.85); operative time (p=0.82) and amount of resected tissue (p=0.96), in both groups did not show significant differences in the development of transurethral resection syndrome. Conclusions: Glycine in comparison with other irrigants in TURP, increases the incidence of transurethral resection syndrome. Age, operative time and the amount of resected tissue do not influence the development of transurethral resection syndromeTesi

Hybrid Transformer Based Feature Fusion for Self-Supervised Monocular Depth Estimation

With an unprecedented increase in the number of agents and systems that aim to navigate the real world using visual cues and the rising impetus for 3D Vision Models, the importance of depth estimation is hard to understate. While supervised methods remain the gold standard in the domain, the copious amount of paired stereo data required to train such models makes them impractical. Most State of the Art (SOTA) works in the self-supervised and unsupervised domain employ a ResNet-based encoder architecture to predict disparity maps from a given input image which are eventually used alongside a camera pose estimator to predict depth without direct supervision. The fully convolutional nature of ResNets makes them susceptible to capturing per-pixel local information only, which is suboptimal for depth prediction. Our key insight for doing away with this bottleneck is to use Vision Transformers, which employ self-attention to capture the global contextual information present in an input image. Our model fuses per-pixel local information learned using two fully convolutional depth encoders with global contextual information learned by a transformer encoder at different scales. It does so using a mask-guided multi-stream convolution in the feature space to achieve state-of-the-art performance on most standard benchmarks.Comment: Presented at the Advances in Image Manipulation Workshop at ECCV 202