Reviewerly/exHarmony huggingface dataset:
LEAF: Let’s Efficiently Make Adaptive Forwarding in Payment Channel Networks

Blockchain technology is widely applicable to modern payment systems but has inherent throughput limitations. Off-chain networks are proposed to solve scalability issues, which allows parties to efficiently perform micropayments without committing all of the payments to the blockchain. Off-chain payments with security and privacy protection requirements use the smart contract to ensure security and reduce the risk of sensitive information leakage. Although off-chain payments avoid expensive on-chain operations, it raises many concerns, such as the capacity limitation of payment channels and highly dynamic channel status, lowering the throughput of payment channel networks (PCNs). In our work, we explore the path overlap issues in PCNs and propose a decentralized payment routing scheme to improve the network throughput and reduce the redundant traffic overhead of PCNs, thereby guaranteeing efficient payments. The simulation results indicate that the proposed routing algorithm can achieve higher throughput than other routing schemes while guaranteeing short payment times.
Tolerate Failures of the Visual Camera With Robust Image Classifiers

Deep Neural Networks (DNNs) have become an enabling technology for building accurate image classifiers, and are increasingly being applied in many ICT systems such as autonomous vehicles. Unfortunately, classifiers can be deceived by images that are altered due to failures of the visual camera, preventing the proper execution of the classification process. Therefore, it is of utmost importance to build image classifiers that can guarantee accurate classification even in the presence of such camera failures. This study crafts classifiers that are robust to failures of the visual camera by augmenting the training set with artificially altered images that simulate the effects of such failures. Such a data augmentation approach improves classification accuracy with respect to the most common data augmentation approaches, even in the absence of camera failures. To provide experimental evidence for our claims, we exercise three DNN image classifiers on three image datasets, in which we inject the effects of many failures into the visual camera. Finally, we applied eXplainable AI to debate why classifiers trained with the data augmentation approach proposed in this study can tolerate failures of the visual camera.
De-Noising of Sparse Signals Using Mixture Model Shrinkage Function

In this work a new thresholding function referred to as ’mixture model shrinkage’ (MMS) based on the minimization of a convex cost function is proposed. Normally, thresholding functions underestimate larger signal amplitudes during the de-noising process. The proposed model is a more flexible shrinkage function as it solves the underestimation problem to a greater extent and thus efficiently de-noises the signal without affecting signal amplitudes. The Expectation minimization (EM) algorithm is used to find the model parameters along with the majorization-minimization (MM) algorithm that minimize the monotonic cost function. The proposed model is then applied for de-noising group sparse signals and Shepp Logan phantom images. Our experimental study shows that MMS outclasses current thresholding functions and overlapping group shrinkage algorithm without results suffering from underestimation. Furthermore, the proposed model has the smallest Root Mean Square Error (RMSE) for de-noising group sparse signals.
Effective implementation and improvement of fast labeled multi-Bernoulli filter

Effective implementation of the fast labeled multi-Bernoulli (FLMB) filter is addressed for target tracking with interval measurements. Firstly, a sequential Monte Carlo (SMC) implementation of the FLMB filter, SMC-FLMB filter, is derived based on generalized likelihood function weighting. Then, a box particle (BP) implementation of the FLMB filter, BP-FLMB filter, is developed, with a computational complexity reduction of the SMC-FLMB filter. Finally, an improved version of the BP-FLMB filter, improved BP-FLMB (IBP-FLMB) filter, is proposed, improving its estimation accuracy and real-time performance under the conditions of low detection probability and high clutter. Simulation results show that the BP-FLMB filter has a great improvement of the real-time performance than the SMC-FLMB filter, with similar tracking performance. Compared with the BP-FLMB filter, the IBP-FLMB filter has better estimation performance and real-time performance under the conditions of low detection probability and high clutter.
Boosting Generic Visual-Linguistic Representation with Dynamic Contexts

Pretraining large models on generous multi-modal corpora has accelerated the development of visual-linguistic (VL) representation and achieved great success on various vision-and-language downstream tasks. Learning these models is usually executed by predicting the randomly masked words of captions or patches in images. Such approaches, nevertheless, seldom explore the supervision of causalities behind the caption descriptions or the procedure of generating events beyond still images. In this work, we endow the pretrained models with high-level cognition by delving into dynamic contexts to model the visual and linguistic causalities uniformly. Specifically, we format the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic contexts</i> of an image as the sentences describing the events <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">before</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">after</i> image. Unlike traditional caption-wise similarity, we propose a novel dynamic contexts-based similarity (DCS) metric, in which the correlation of potential causes and effects besides immediate visual content are considered to measure the relevance among images. DCS can be further simplified by parameterizing event continuity to relax the requirements on dense contextual event annotations. A new pre-task is designed to minimize the feature distances of dynamically contextual relevant images and incorporate the event causality and commonsense knowledge into the VL representation learning. Models based on our dynamic contexts significantly outperform typical VL models on multiple cross-modal downstream tasks, including the conventional visual commonsense reasoning (VCR), visual question answering (VQA), zero-shot image-text retrieval, and extended image / event ordering tasks.
Deep Neural Networks With Data Rate Model: Learning Power Allocation Efficiently

Learning-based resource allocation can be implemented in real-time, but deep neural networks (DNNs) developed in other fields such as computer vision are with high training complexity and weak generalizability. Leveraging domain knowledge in communications is promising for learning wireless policies efficiently. In this paper, we propose a framework of integrating the Shannon formula with DNNs, and derive a data rate-based DNN (DRNN), for learning resource allocation by taking power allocation as an example. The DRNN is with an iterative structure with multiple update layers, each consisting of a pre-determined model function, an update network, and a dimension reduction network. To justify the iterative structure, we prove the existence of an iteration function that converges to the optimal policy for the update layer to learn. To justify the structure of each update layer, we provide the conditions for the iteration function to be a composite function of the model function. We further incorporate permutation equivariance properties into the DRNN. Simulation results show that the numbers of training samples and free parameters, and the training time to achieve a desired system performance can be reduced remarkably by harnessing the data rate model and PE prior.
Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

Unmanned air vehicles (UAVs) have been regularly employed in modern wars to conduct different missions. Instead of addressing mission planning and route planning separately, this study investigates the issue of joint mission and route planning for a fleet of UAVs. The mission planning determines the configuration of weapons in UAVs and the weapons to attack targets, while the route planning determines the UAV's visiting sequence for the targets. The problem is formulated as an integer linear programming model. Due to the inefficiency of CPLEX on large scale optimization problems, an effective learning-based heuristic, namely, population based adaptive large neighborhood search (P-ALNS), is proposed to solve the model. In P-ALNS, seven neighborhood structures are designed and adaptively utilized in terms of their historical performance. The effectiveness and superiority of the proposed model and algorithm are demonstrated on test instances of small, medium and large sizes. In particular, P-ALNS achieves comparable solutions or as good as those of CPLEX on small-size (20 targets) instances in much shorter time.
Novelty Detection of Leukocyte Image via Mean-Shifted Feature and Directly Optimized Subspace

Novelty detection of leukocyte images aims to learn effective data description from in-distribution leukocyte samples and detect out-of-distribution ones that deviate from the expected patterns. Recently, the methods of fine-tuning on the pretrained model have demonstrated excellent performance in novelty detection. However, those methods are prone to collapse or feature deterioration due to the training data of limited categories and tiny differences in medical images such as leukocyte images. To alleviate the above issues, we propose an unsupervised feature adaptation method based on the mean-shifted feature (MSF) and the directly optimized subspace (SUB) for leukocyte novelty detection, named mean-shifted subspace (SubMSF). Specifically, we first use MSFs to alleviate the catastrophic collapse when fine-tuning the pretrained model via contrastive learning. Then, we combine the MSF with the SUB to recognize tiny differences between leukocytes. Compared with several state-of-the-art methods, the results on real-world leukocyte datasets demonstrate the effectiveness of the proposed SubMSF.
Annotation Cost Minimization for Ultrasound Image Segmentation using Cross-domain Transfer Learning

Deep learning techniques can help minimize inter-physician analysis variability and the medical expert workloads, thereby enabling more accurate diagnoses. However, their implementation requires large-scale annotated dataset whose acquisition incurs heavy time and human-expertise costs. Hence, to significantly minimize the annotation cost, this study presents a novel framework that enables the deployment of deep learning methods in ultrasound (US) image segmentation requiring only very limited manually annotated samples. We propose SegMix, a fast and efficient approach that exploits a segment-paste-blend concept to generate large number of annotated samples based on a few manually acquired labels. Besides, a series of US-specific augmentation strategies built upon image enhancement algorithms are introduced to make maximum use of the available limited number of manually delineated images. The feasibility of the proposed framework is validated on the left ventricle (LV) segmentation and fetal head (FH) segmentation tasks, respectively. Experimental results demonstrate that using only 10 manually annotated images, the proposed framework can achieve a Dice and JI of 82.61% and 83.92%, and 88.42% and 89.27% for LV segmentation and FH segmentation, respectively. Compared with training using the entire training set, there is over 98% of annotation cost reduction while achieving comparable segmentation performance. This indicates that the proposed framework enables satisfactory deep leaning performance when very limited number of annotated samples is available. Therefore, we believe that it can be a reliable solution for annotation cost reduction in medical image analysis.
Orchestrating Networked Machine Learning Applications Using Autosteer

A platform for orchestrating networked machine learning (ML) applications over distributed environments is described. ML applications are transformed into automated pipelines that manage the whole application lifecycle and production-grade implementations are automatically constructed. We present AUTOSTEER, a software platform that can deploy ML applications on various hardware resources—interconnected using heterogeneous network resources—across cloud and edge devices. Device placement optimization and model adaptation are used as control actions to support application requirements and maximize the performance of ML model execution over heterogeneous computing resources. The performance of deployed applications is continually monitored at runtime to overcome performance degradation due to incorrect application parameter settings or model decay. Three real-world applications are used to demonstrate how AUTOSTEER can support application deployment and runtime performance guarantees.
BQ-Bank: A Quantum Software for Finance and Banking

The power of quantum computing may bring a revolution in finance and banking. Here, we present quantum software, BQ-Bank for option pricing, Value at Risk, portfolio optimization, and others. BQ-Bank can be run on a real quantum computer such as superconducting system or on an emulation system based on a classical computer with an interface. BQ-Bank, such as other quantum types of software, represents a new generation of the toolbox that likely brings disruptive innovations to the financial industry and banking market in the future. BQ-Bank also provides the classical Monte Carlo solution, so that users can compare their quantum results with classical ones directly. Our simulation results for a variety of examples show the superiority of quantum solutions.
Sahlqvist Completeness Theory for Hybrid Logic with Downarrow Binder

Abstract In the present paper, we continue the research in Zhao (2021, Logic J. IGPL) to develop the Sahlqvist completeness theory for hybrid logic with satisfaction operators and downarrow binders $\mathcal {L}( @, {\downarrow })$. We define the class of restricted Sahlqvist formulas for $\mathcal {L}( @, {\downarrow })$ following the ideas in Conradie and Robinson (2017, J. Logic Comput., 27, 867–900), but we follow a different proof strategy which is purely proof-theoretic, namely showing that for every restricted Sahlqvist formula $\varphi $ and its hybrid pure correspondence $\pi $, $\textbf {K}_{\mathcal {H}( @, {\downarrow })}+\varphi $ proves $\pi $; therefore, $\textbf {K}_{\mathcal {H}( @, {\downarrow })}+\varphi $ is complete with respect to the class of frames defined by $\pi $, using a modified version $\textsf {ALBA}^{{\downarrow }}_{\textsf {Modified}}$ of the algorithm $\textsf {ALBA}^{{\downarrow }}$ defined in Zhao (2021, Logic J. IGPL).
Improved breast cancer detection using fusion of bimodal sonographic features through binary firefly algorithm

Breast cancer is a major health issue and causes a larger number of deaths in women. As per GLOBOCAN statistics, breast cancer has surpassed lung cancer in 2020 with 2.3 million anticipated new cases which is 11.7% of total cancer cases. Sonography is used in addition to mammography to detect, describe and locate breast lesions effectively. Elastography and Echography are widely used sonographic techniques as they provide different and distinctive information for identifying breast diseases. To obtain improved detection performance, this work focuses on the fusion of Local Binary Pattern (LBP) texture features from ultrasound elastogram and echogram images followed by feature selection using Binary Firefly Algorithm (BFA) with Optimum Path Forest (OPF) classifier accuracy as a fitness function for feature selection. This method produces 97.3% accuracy, 96.2% sensitivity, 98.2% specificity, 97.3% precision, 96.2% F1 score, 94.71% Balanced Classification Rate, and Mathews Correlation Coefficient of 0.884 outperforming existing works..
Maximal Robust Neural Network Specifications via Oracle-Guided Numerical Optimization

Analyzing the robustness of neural networks is crucial for trusting them. The vast majority of existing works focus on networks’ robustness in $$\epsilon $$ -ball neighborhoods, but these cannot capture complex robustness specifications. We propose MaRVeL, a system for computing maximal non-uniform robust specifications that maximize a target norm. The main idea is to employ oracle-guided numerical optimization, thereby leveraging the efficiency of a numerical optimizer as well as the accuracy of a non-differentiable robustness verifier, acting as the oracle. The optimizer iteratively submits to the verifier candidate specifications, which in turn returns the closest inputs to the decision boundaries. The optimizer then computes their gradients to guide its search in the directions the specification can expand while remaining robust. We evaluate MaRVeL on several datasets and classifiers and show that its specifications are larger by 5.1x than prior works. On a two-dimensional dataset, we show that the average diameter of its specifications is 93% of the optimal average diameter, whereas the diameter of prior works’ specifications is only 26%.
Bayesian Parameter Estimation with Guarantees via Interval Analysis and Simulation

We give a method to compute guaranteed estimates of Bayesian a posteriori distributions in a model where the relation between the observation y and the parameters $$\theta $$ is a function, possibly involving additive noise parameters $$\psi $$ , say $$y=f(\theta )+h(\psi )$$ . This model covers the case of (noisy) ode parameters estimation and the case when f is computed by a neural network. Applying a combination of methods based on uncertain probability (P-boxes), Interval Arithmetic (IA) and Monte Carlo (MC) simulation, we design an efficient randomized algorithm that returns guaranteed estimates of the posterior CDF of the parameters $$\theta $$ , and moments thereof, given that the observation y lies in a (small) rectangle. Guarantees come in the form of confidence intervals for the CDF values and its moments. Comparison with state-of-the-art approaches on odes benchmarks shows significant improvement in terms of efficiency and accuracy.
Battleship, tomography and quantum annealing

Abstract The classic game of Battleship involves two players taking turns attempting to guess the positions of a fleet of vertically or horizontally positioned enemy ships hidden on a $10\times 10$ grid. One variant of this game, also referred to as Battleship Solitaire, Bimaru or Yubotu, considers the game with the inclusion of X-ray data , represented by knowledge of how many spots are occupied in each row and column in the enemy board. This paper considers the Battleship puzzle problem : the problem of reconstructing an enemy fleet from its X-ray data. We generate non-unique solutions to Battleship puzzles via certain reflection transformations akin to Ryser interchanges. Furthermore, we demonstrate that solutions of Battleship puzzles may be reliably obtained by searching for solutions of the associated classical binary discrete tomography problem which minimise the discrete Laplacian. We reformulate this optimisation problem as a quadratic unconstrained binary optimisation problem and approximate solutions via a simulated annealer, emphasising the future practical applicability of quantum annealers to solving discrete tomography problems with predefined structure.
An Offline Weighted-Bagging Data-Driven Evolutionary Algorithm with Data Generation Based on Clustering

In recent years, a variety of data-driven evolutionary algorithms (DDEAs) have been proposed to solve time-consuming and computationally intensive optimization problems. DDEAs are usually divided into offline DDEAs and online DDEAs, with offline DDEAs being the most widely studied and proven to display excellent performance. However, most offline DDEAs suffer from three disadvantages. First, they require many surrogates to build a relatively accurate model, which is a process that is redundant and time-consuming. Second, when the available fitness evaluations are insufficient, their performance tends to be not entirely satisfactory. Finally, to cope with the second problem, many algorithms use data generation methods, which significantly increases the algorithm runtime. To overcome these problems, we propose a brand-new DDEA with radial basis function networks as its surrogates. First, we invented a fast data generation algorithm based on clustering to enlarge the dataset and reduce fitting errors. Then, we trained radial basis function networks and carried out adaptive design for their parameters. We then aggregated radial basis function networks using a unique model management framework and demonstrated its accuracy and stability. Finally, fitness evaluations were obtained and used for optimization. Through numerical experiments and comparisons with other algorithms, this algorithm has been proven to be an excellent DDEA that suits data optimization problems.
TECHNOLOGY BASED TEACHER EDUCATION PROGRAMME IN COVID-19 PANDEMIC SITUATION: A BANE OR BOON

Today is the era of technology. Technology plays a pivotal role in our day to day life. We cannot deny the presence of technology in our practical work. Use of electronic gadget and many other modern digital devices changed the scenario of education in today’s world.ICT based education system was introduced nearly after recommendations of NPE-1986 and its follow-up programmes in the year 1991.by the Government of India.But digital content for teaching-learning was not so solid and effectively powerful since it was introduced nearly more than three decades ago. Union Government of India also introduced the idea of “Digital India” (2015) and the digitized education system creates a new impact in the field of education also. The advancements of technology have changed the world greatly. There is no denying of the fact that they make lives better and easier, especially in the fields of science, medicine and education etc. During the critical circulation of pandemic COVID – 19, the livelihood of the people totally collapsed. It changes the scenario of our society. Like other field of educations, its effects the teacher education system also. From the time of lockdown the face to face contact education system totally stopped and also collapsed. So an alternative option of digital platform enhancing day by day. In some near recent times this types of teaching-learning platforms (Banglar Siksha Portal, Swayam, Swayam-prabha, E-Pathsala, Bharta Digital class,Mooc etc.) will become convenient through time-being tests also. Various E-Content may be used for digital classroom learning. Though it introduces many opportunities, but it also has its drawbacks. In this article the presenter tries to analyse the Challenges and Possibilities on technology based teacher education. The article also concludes that the perfect amalgamation of technology and education can create a new era in the field of teacher education.