Reviewerly/exHarmony huggingface dataset:
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling

Zero-shot cross-domain slot filling alleviates the data dependence in the case of data scarcity in the target domain, which has aroused extensive research.However, as most of the existing methods do not achieve effective knowledge transfer to the target domain, they just fit the distribution of the seen slot and show poor performance on unseen slot in the target domain.To solve this, we propose a novel approach based on prototypical contrastive learning with a dynamic label confusion strategy for zero-shot slot filling.The prototypical contrastive learning aims to reconstruct the semantic constraints of labels, and we introduce the label confusion strategy to establish the label dependence between the source domains and the target domain on-the-fly.Experimental results show that our model achieves significant improvement on the unseen slots, while also set new state-of-the-arts on slot filling task.
Well-typed programs can go wrong: a study of typing-related bugs in JVM compilers

Despite the substantial progress in compiler testing, research endeavors have mainly focused on detecting compiler crashes and subtle miscompilations caused by bugs in the implementation of compiler optimizations. Surprisingly, this growing body of work neglects other compiler components, most notably the front-end. In statically-typed programming languages with rich and expressive type systems and modern features, such as type inference or a mix of object-oriented with functional programming features, the process of static typing in compiler front-ends is complicated by a high-density of bugs. Such bugs can lead to the acceptance of incorrect programs (breaking code portability or the type system's soundness), the rejection of correct (e.g. well-typed) programs, and the reporting of misleading errors and warnings. We conduct, what is to the best of our knowledge, the first empirical study for understanding and characterizing typing-related compiler bugs. To do so, we manually study 320 typing-related bugs (along with their fixes and test cases) that are randomly sampled from four mainstream JVM languages, namely Java, Scala, Kotlin, and Groovy. We evaluate each bug in terms of several aspects, including their symptom, root cause, bug fix's size, and the characteristics of the bug-revealing test cases. Some representative observations indicate that: (1) more than half of the typing-related bugs manifest as unexpected compile-time errors: the buggy compiler wrongly rejects semantically correct programs, (2) the majority of typing-related bugs lie in the implementations of the underlying type systems and in other core components related to operations on types, (3) parametric polymorphism is the most pervasive feature in the corresponding test cases, (4) one third of typing-related bugs are triggered by non-compilable programs. We believe that our study opens up a new research direction by driving future researchers to build appropriate methods and techniques for a more holistic testing of compilers.
Beyond photon pairs—nonlinear quantum photonics in the high-gain regime: a tutorial

Integrated optical devices will play a central role in the future development of nonlinear quantum photonics. Here we focus on the generation of high-gain nonclassical light within them. Starting from the solid foundation provided by Maxwell's equations, we then move to applications by presenting a unified formulation that allows for a comparison of stimulated and spontaneous experiments in ring resonators and nanophotonic waveguides, and leads directly to the calculation of the quantum states of light generated in high-gain experiments.
Constructivist approach in pedagogical science

The article examines the problems of constructivist understanding of education, the innovations, opportunities and limitations that the constructivist model of the pedagogical process offers. The aim of the work was to analyze the constructivist model of education and those innovations and prospects for understanding the modern educational process that the constructivist approach brings. Constructivism changes the view of teachers and other scientists and practitioners in their view of what is happening in the relations of people in education. Due to its controversial nature, the development of the ideas of constructivism in the context of the development of educational ideologies and technologies leads to the reconceptualization of the traditional model of education, and the constructionist model of education itself can and should become the object of systematic methodological, theoretical, empirical and applied research. Any of the educational approaches available now can be disclosed as an example of a constructivist model of education, the leading features of which are the dialogic nature of education, the focus on understanding the inner and outer world in dialogue with significant other people, the consideration of such a dialogue as a process of building a person's own life world and himself, as a creative "rediscovery" of the basic truths of human existence, as the adoption of socially and personally significant decisions in a situation of educational, professional, life choice.
Genetic Decompilation Concept of the Telecommunication Devices Machine Code

Reverse engineering correct source code from a machine code to find and neutralize vulnerabilities is the most pressing problem for the field of telecommunications equipment. The decompilation techniques applicable for this have potentially reached their evolutionary limit. As a result, new concepts are required that can make a quantum leap in problem solving. Proceeding from this, the paper proposes the concept of genetic decompilation, which is a solution to the problem of multiparameter optimization in the form of iterative approximation of instances of the source code to the "original" one which will compile to the given machine code. This concept is tested by conducting a series of experiments with the developed software prototype using a basic example of machine code. The results of the experiments prove the proof of the concept, thereby suggesting new innovative directions for ensuring information security in this subject area.
The improved grasshopper optimization algorithm and its applications

Grasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization.
Geo-Localization via Ground-to-Satellite Cross-View Image Retrieval

The large variation of viewpoint and irrelevant content around the target always hinder accurate image retrieval and its subsequent tasks. In this paper, we investigate an extremely challenging task: given a ground-view image of a landmark, we aim to achieve cross-view geo-localization by searching out its corresponding satellite-view images. Specifically, the challenge comes from the gap between ground-view and satellite-view, which includes not only large viewpoint changes (some parts of the landmark may be invisible from front view to top view) but also highly irrelevant background (the target landmark tend to be hidden in other surrounding buildings), making it difficult to learn a common representation or a suitable mapping. To address this issue, we take advantage of drone-view information as a bridge between ground-view and satellite-view domains. We propose a Peer Learning and Cross Diffusion (PLCD) framework. PLCD consists of three parts: 1) a peer learning across ground-view and drone-view to find visible parts to benefit ground-drone cross-view representation learning; 2) a patch-based network for satellite-drone cross-view representation learning; 3) a cross diffusion between ground-drone space and satellite-drone space. Extensive experiments conducted on the University-Earth and University-Google datasets show that our method outperforms state-of-the-arts significantly.
Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning

The annotation of large datasets is often the bottleneck in the successful application of artificial intelligence in computational pathology.For this reason recently Multiple Instance Learning (MIL) and Semi Supervised Learning (SSL) approaches are gaining popularity because they require fewer annotations.In this work we couple SSL and MIL to train a deep learning classifier that combines the advantages of both methods and overcomes their limitations.Our method is able to learn from the global WSI diagnosis and a combination of labeled and unlabeled patches.Furthermore, we propose and evaluate an efficient labeling paradigm that guarantees a strong classification performance when combined with our learning framework.We compare our method to SSL and MIL baselines, the state-of-the-art and completely supervised training.With only a small percentage of patch labels our proposed model achieves a competitive performance on SICAPv2 (Cohen's kappa of 0.801 with 450 patch labels), PANDA (Cohen's kappa of 0.794 with 22,023 patch labels) and Camelyon16 (ROC AUC of 0.913 with 433 patch labels).Our code is publicly available at https://github.com/arneschmidt/ssl_and_mil_cancer_classification.
Bayesian Approach in a Learning-Based Hyperspectral Image Denoising Framework

Hyperspectral images are corrupted by a combination of Gaussian-impulse noise.On one hand, the traditional approach of handling the denoising problem using maximum a posteriori criterion is often restricted by the time-consuming iterative optimization process and design of hand-crafted priors to obtain an optimal result.On the other hand, the discriminative learning-based approaches offer fast inference speed over a trained model; but are highly sensitive to the noise level used for training.A discriminative model trained with a loss function which does not accord with the Bayesian degradation process often leads to sub-optimal results.In this paper, we design the training paradigm emphasizing the role of loss functions; similar to as observed in model-based optimization methods.As a result; loss functions derived in Bayesian setting and employed in neural network training boosts the denoising performance.Extensive analysis and experimental results on synthetically corrupted and real hyperspectral dataset suggest the potential applicability of the proposed technique under a wide range of homogeneous and heterogeneous noisy settings.
Corpus-based Open-Domain Event Type Induction

Traditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors.These prerequisites are often hard to be satisfied in real-world applications.This work presents a corpus-based open-domain event type induction method that automatically discovers a set of event types from a given corpus.As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of predicate sense, object head pairs.Specifically, our method (1) selects salient predicates and object heads, (2) disambiguates predicate senses using only a verb sense dictionary, and (3) obtains event types by jointly embedding and clustering predicate sense, object head pairs in a latent spherical space.Our experiments, on three datasets from different domains, show our method can discover salient and high-quality event types, according to both automatic and human evaluations 1 .
The effects of hedonic and utilitarian values on e-loyalty: Understanding the mediating role of e-satisfaction

The purpose of this paper is to examine effects of hedonic and utilitarian aspects of consumer behaviour toward e-loyalty and how e-satisfaction plays a mediating role between them. A survey method was adopted to test the research model. The primary data for the survey was collected during January 2021, using the convenience sampling approach, from business school students. The structural equation modelling method was used to analyse the data with AMOS 22.0 software. The measurement model was estimated through a confirmatory factor analysis before testing the structural model framework and hypotheses (response rate 94.3%, n=264). The results showed that hedonic and utilitarian shopping values have great influence in shaping e-satisfaction. The study also highlights the importance of mediating the role of e-satisfaction between shopping values and e-loyalty. This research highlights why and how ‘satisfaction with website’ matters in the contribution of shopping values to behavioural outcomes by presenting its mediating role. The study offers implications and suggestions to e-retailers by specifying characteristics that should be given more consideration in order to increase online consumer e-satisfaction and e-loyalty. It also contributes to the present body of knowledge on shopping and buying behaviour in the online context, especially for those who have theoretical and managerial interests on the subject in emerging economies like Jordan. The novelty of the paper comes from the fact that it cumulatively captures the factors influencing online consumers’ loyalty in the Jordanian context, apart from validating the mediating role of e-satisfaction.
Verifying the Smart Contracts of the Port Supply Chain System Based on Probabilistic Model Checking

Port supply chains play a very important role in the process of economic globalization. Lack of trust of the mechanism is the main factor in restricting the development of port supply chains. Blockchains have great potential to solve the trust problem among all participants using port supply chains, which can reduce costs and improve efficiency. As the bridge between blockchains and port supply chains, smart contracts reconstruct the business process of blockchain-empowered port supply chains. In this article, we present an architecture of a consortium blockchain-empowered port supply chain system, and propose a system verification framework for the smart contracts of port supply chains with probabilistic behaviors. The smart contracts are modeled as DTMCs (Discrete-Time Markov Chains), which are automatically transformed through the BPMN (Business Process Model and Notation) description of the smart contracts. The requirements are specified by PCTL (Probabilistic Computation Tree Logic). Moreover, we implement the customs clearance process of the Shanghai Yangshan Port based on blockchain Hyperledger Fabric, and reconstruct the clearance process with smart contracts. We use it to demonstrate the effectiveness of this framework, and identify the smart contracts that do not meet the expected needs of users.
Human health risk identification of petrochemical sites based on extreme gradient boosting

Petrochemical industry is a key industry of soil pollution, which presents great effects on human health and the ecological environment. It is of great significance to achieve rapid, economic and efficient health risk identification for petrochemical industry in China. In this work, an efficient method was developed based on extreme gradient boosting (XGBoost) algorithm for human health risk identification, which is different from the traditional health risk assessment with complicated procedures. In this methodology, an index system of 13 indicators was established from the perspective of "sources - pathways - receptors" for risk identification. The 10-fold cross validation was used to assess the generalization performance, and the accuracy, precision and recall were employed to evaluate the performance of the algorithms. Wilcoxon signed-rank test was conducted to analyze the differences between XGBoost and other models for statistical support. The results showed that XGBoost significantly presented a better performance for health risk identification over multilayer perceptron neural network with error backpropagation training (BPNN), support vector machine (SVM), gradient boosting decision tree (GBDT) and light gradient boosting machine (LightGBM), with an accuracy of 0.783. The most important features contributing to the risk identification were determined with the sequence of site location (in the industrial zone or not), site planning and production period. Great attention should be given to the petrochemical sites that are not located in the industrial zone with long production period and sensitive receptors in the health risk identification. This method has important reference significance for relevant departments to carry out soil contamination screening and health risk assessment of petrochemical sites.
DeepCompNet: A Novel Neural Net Model Compression Architecture

The emergence of powerful deep learning architectures has resulted in breakthrough innovations in several fields such as healthcare, precision farming, banking, education, and much more. Despite the advantages, there are limitations in deploying deep learning models in resource-constrained devices due to their huge memory size. This research work reports an innovative hybrid compression pipeline for compressing neural networks exploiting the untapped potential of z-score in weight pruning, followed by quantization using DBSCAN clustering and Huffman encoding. The proposed model has been experimented with state-of-the-art LeNet Deep Neural Network architectures using the standard MNIST and CIFAR datasets. Experimental results prove the compression performance of DeepCompNet by 26x without compromising the accuracy. The synergistic blend of the compression algorithms in the proposed model will ensure effortless deployment of neural networks leveraging DL applications in memory-constrained devices.
Open-Set Adversarial Defense with Clean-Adversarial Mutual Learning

Open-set recognition and adversarial defense study two key aspects of deep learning that are vital for real-world deployment. The objective of open-set recognition is to identify samples from open-set classes during testing, while adversarial defense aims to robustify the network against images perturbed by imperceptible adversarial noise. This paper demonstrates that open-set recognition systems are vulnerable to adversarial samples. Furthermore, this paper shows that adversarial defense mechanisms trained on known classes are unable to generalize well to open-set samples. Motivated by these observations, we emphasize the necessity of an Open-Set Adversarial Defense (OSAD) mechanism. This paper proposes an Open-Set Defense Network with Clean-Adversarial Mutual Learning (OSDN-CAML) as a solution to the OSAD problem. The proposed network designs an encoder with dual-attentive feature-denoising layers coupled with a classifier to learn a noise-free latent feature representation, which adaptively removes adversarial noise guided by channel and spatial-wise attentive filters. Several techniques are exploited to learn a noise-free and informative latent feature space with the aim of improving the performance of adversarial defense and open-set recognition. First, we incorporate a decoder to ensure that clean images can be well reconstructed from the obtained latent features. Then, self-supervision is used to ensure that the latent features are informative enough to carry out an auxiliary task. Finally, to exploit more complementary knowledge from clean image classification to facilitate feature denoising and search for a more generalized local minimum for open-set recognition, we further propose clean-adversarial mutual learning, where a peer network (classifying clean images) is further introduced to mutually learn with the classifier (classifying adversarial images). We propose a testing protocol to evaluate OSAD performance and show the effectiveness of the proposed method on white-box attacks, black-box attacks, as well as the rectangular occlusion attack in multiple object classification datasets.
How blockchain technology can be a sustainable infrastructure for the agrifood supply chain in developing countries

Purpose This study aims to formulate a conceptual sustainable framework for developing a trusted, reliable, scalable, transparent, traceable and sustainable agri-food supply chain in a developing country so that it minimizes wastage and increases the efficiency levels of agri-produce and its usage. Design/methodology/approach This study uses a rigorous review of extant literature, case studies and the interview method for theory building, using blockchain technology (BT) as a subject. Further, the study builds a framework to relate blockchain solutions to the challenges faced by the agri-supply chain. Notably, the use of BT in the agri-food supply chain is a relatively new area of study. Findings Limitations of using BT 3.0 in a diverse supply chain like the food sector, especially in a developing country such as India, may be overcome by adopting BT 4.0 and could change the country’s face by controlling inefficiencies and ensuring transparency, helping in good governance, improving the humanitarian supply chain and integrating the bottom of the pyramid to the main economy. Based on the findings, this study proposes BT 4.0 for the agri-supply chain in India to deal with the current issues of demand-supply gap, wastages of agri-produce, unequal distribution of profit among agriproduct producers and logistics suppliers and ensuring sustainability. Research limitations/implications Results in this study have been derived from a specific demographic condition in India. Future research with other demographic conditions may be replicated. BT is a new technology product, and its effectiveness is yet to be established. Practical implications The outcome of this study provides the application of BT 4.0 in the area of the agri-food supply chain. The BT 4.0 framework has been developed on studying a few cases that either implemented BT or were in the testing phase. The benefits of the agri-food supply chain vis-à-vis its overall social well-being may be achieved on the successful implementation of the framework, despite existing complexities in the food supply chain. Further research on this subject may help the other dimensions of the complexity of adopting BT 4.0. Both practitioners and policymakers from developing countries can, therefore, use the findings of this study to analyze BT 4.0 and address the concerns of the agri-food supply chain. Originality/value This research paper has proposed a conceptual framework of BT 4.0, which is a completely new technology. It is fairly transparent, and therefore sustainable supply chain practices may be achieved easily.