Reviewerly/exHarmony huggingface dataset:
The unbearable hurtfulness of sarcasm

In the last decade, the need to detect automatically irony to correctly recognize the sentiment and hate speech involved in online texts increased the investigation on humorous figures of speech in NLP. The slight boundaries among various types of irony lead to think of irony as a linguistic phenomenon that covers sarcasm, satire, humor and parody joined by their trend to create a secondary or opposite meaning to the literal one expressed in the message. Although this commonality, in literature sarcasm is defined as a type of irony more aggressive with the intent to mock or scorn a victim without excluding the possibility to amuse. The aggressive tone and the intent of contempt suggest that sarcasm involves some peculiarities that make it a suitable type of irony to disguise negative messages. To investigate these peculiarities of sarcasm, we examined the dataset of the IronITA shared task. It consists of Italian tweets about controversial social issues, such as immigration, politics and other more general topics. Each tweet is annotated as ironic and non-ironic, and, at a deeper level, as sarcastic and non-sarcastic. Qualitative and quantitative analyses of the dataset showed how sarcasm tends to be expressed with hurtful language revealing the aggressive intention with which the author targets the victim. While irony is characterized by being offensive in hateful context and, in general, moved by negative emotions. For a better understanding of the impact of hurtful and affective language on the detection of irony and sarcasm, we proposed a transformer-based system, called AlBERToIS, combining pre-trained AlBERTo model with linguistic features. This approach obtained the best performances on irony and sarcasm detection on the IronITA dataset.
Dual Mutual Learning for Cross-Modality Person Re-Identification

Cross-modality person re-identification (Re-ID) is more challenging than traditional visible Re-ID due to the huge cross-modality gap from heterogeneous images. To alleviate this problem, existing methods often utilize a dual path learning framework equipped with metric loss to learn discriminative features. Despite effectiveness, the inevitable degeneration of intra-modality discrimination by taking cross-modality discrimination into consideration is unsolvable. Such degeneration substantially hinders the model’s capability of further improving feature representations. To mitigate this degeneration, we propose a Dual Mutual Learning (DML) method for cross-modality Re-ID which conducts mutual learning between the cross-modality and each of two single modalities. We design a triple-branch deep model containing the RGB and IR branches and the cross-modality branch. The cross-modality branch is designed to learn modality-invariant feature subspace for appearance similarity measurement. Both the RGB branch and IR branch provide attention supervision information to the cross-modality branch for attention feature alignment so as to enhance the intra-modality discrimination. Experimental results on two standard benchmarks demonstrate DML is superior to state-of-the-art methods.
Unmanned Aerial Vehicle Flight Data Anomaly Detection and Recovery Prediction Based on Spatio-Temporal Correlation

With the development of unmanned aerial vehicle (UAV) technology, a UAV is gradually applied to a variety of civil fields, such as photography, power line inspection, and environmental monitoring. At the same time, the safety and reliability of a UAV also attract wide attention. Anomaly detection is one of the key technologies to improve the safety of an UAV. The structure of the UAV system is complex, and there are complex spatio-temporal correlations among the high-dimensional flight data with many parameters. However, the existing methods often ignore the spatio-temporal correlation of data and lack parameter selection, which is used to abandon the parameters without a positive impact on anomaly detection results. This article proposes a spatio-temporal correlation based long short-term memory (LSTM) method for anomaly detection and recovery prediction of UAV flight data. First, an artificial neural network correlation analysis is proposed to preliminarily mine the spatio-temporal correlation in flight data and to obtain the correlation parameter sets. Second, the LSTM model is established, and the mapping among different parameters is realized. Finally, anomaly detection and recovery prediction are carried out based on parameter sets mapping model. The effectiveness of the proposed method is verified by generating sample sets with anomaly injection on real UAV flight data.
Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution

This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel graph transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains.
A transaction cost perspective on blockchain governance in global value chains

Abstract This study advances theoretical insights into blockchain adoption in the context of global value chains from a transaction cost theory perspective. The research has adopted an exploratory qualitative approach using the Netnography method to scrutinize the case of TradeLens—a thriving blockchain‐enabled ecosystem that Maersk and IBM jointly developed. This study applies textual and audiovisual data from company websites and social media. Our findings indicate blockchain technology's salient and strategic relevance in streamlining business processes, improving efficiency, enhancing visibility, transparency, and traceability for value creation in the global value chains. This investigation supports the notion that blockchain, as a disruptor, will transform global trade with the digital tools to share real‐time information and collaborate security to reduce search and information cost, policing and enforcement cost in global economic transactions and administrative friction in trade. In contrast, the bargaining cost will increase if the information for the transaction is hard to verify where human actor intervention will be required, implying relatively higher designing costs in codifying the agreements in smart contracts.
Adversarial Machine Learning: A Multilayer Review of the State-of-the-Art and Challenges for Wireless and Mobile Systems

Machine Learning (ML) models are susceptible to adversarial samples that appear as normal samples but have some imperceptible noise added to them with the intention of misleading a trained classifier and misclassifying the input. Adversarial Machine Learning (AML) was initially coined following upon researchers pointing out certain blind spots in image classifiers in computer vision field which were exploited by these adversarial samples to deceive the model. Although this has been investigated remarkably in computer vision, the impact of AML in wireless and mobile systems has recently attracted attention. Wireless and mobile networks have intensely benefited from the application of ML classifiers to detect network traffic anomalies and malware detection. However, ML detectors themselves can be exfiltrated/evaded by the samples carefully designed by attackers, raising security concerns for ML-based network applications. Thus, it is crucial to detect such samples to safeguard the network. This survey article presents a systematic mapping and a comprehensive literature review on AML to wireless and mobile systems from physical layer to network and application layers. The article reviews the state-of-the-art AML approaches in the generation and detection of adversarial samples. The samples can be generated by adversarial models such as Generative Adversarial Networks (GANS) and techniques such as Fast Gradient Sign Method (FGSM). The samples can be detected by adversarial models acting as classifiers or ML classifiers reinforced with knowledge on how to detect such samples. For each approach, a high-level overview is provided alongside its impact on solving the problems in wireless and mobile settings. Furthermore, this article provides detailed discussions to highlight the open issues and challenges faced by these approaches, as well as research opportunities which can be of interest to the researchers and developers in Artificial Intelligence (AI)-driven wireless and mobile networking.
A Transfer Learning Evaluation of Deep Neural Networks for Image Classification

Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by reusing learned weights. This technique is ubiquitous because of its great advantages in achieving high performance while saving training time, memory, and effort in network design. In this paper, we investigate how to select the best pre-trained model that meets the target domain requirements for image classification tasks. In our study, we refined the output layers and general network parameters to apply the knowledge of eleven image processing models, pre-trained on ImageNet, to five different target domain datasets. We measured the accuracy, accuracy density, training time, and model size to evaluate the pre-trained models both in training sessions in one episode and with ten episodes.
Synchronized Provable Data Possession Based on Blockchain for Digital Twin

In the digital twin environment, the fusion data onto physical entities in the physical space are mapped to multiple virtual spaces for digital modeling and intelligent simulation in different dimensions. In real intelligent manufacturing scenarios, heterogeneous multi-source fusion data are collected at the same time period. So they are consistent in time state. For the autonomous digital twin system, time states verification and integrity checking are basic security factors. Provable data possession technology can check the integrity of data onto virtual spaces. The blockchain can provide the synchronization interface to make distributed entities to obtain the trusted time state value. Considering the privacy, the blockchain can also provide anonymous services for entities. Therefore, we propose the blockchain-based synchronized provable data possession scheme (named BSPDP) for digital twin. In our scheme, the selection of verifier is flexible. Since virtual spaces may be maliciously framed to pay compensation, we use tag verification to prevent honest virtual spaces from being framed. Under the assumption of RSA, the proposed BSPDP is provably secure. Finally, the performance analysis demonstrates that BSPDP is practical. The experimental results show that BSPDP is effective and attractive for digital twin.
Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human users. Moreover, a new technique that uses three different paths to validate the performance of each candidate configuration is presented. We extend on our previous work by adding two more membership functions to the previous fuzzy model, intending to have a finer-grained adjustment. We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Harmony Search (HS), and the recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that, compared to published results, the proposed fuzzy controllers have better RMSE-measured performance. Nevertheless, experiments also highlight problems with the common practice of evaluating the performance of fuzzy controllers with a single problem case and performance metric, resulting in controllers that tend to be overtrained.
A Voting-Based Blockchain Interoperability Oracle

Today's blockchain landscape is severely fragmented as more and more heterogeneous blockchain platforms have been developed in recent years. These blockchain platforms are not able to interact with each other or with the outside world since only little emphasis is placed on the interoperability between them. Already proposed solutions for blockchain interoperability such as naive relay or oracle solutions are usually not broadly applicable since they are either too expensive to operate or very resource-intensive. For that reason, we propose a blockchain interoperability oracle that follows a voting-based approach based on threshold signatures. The oracle nodes generate a distributed private key to execute an off-chain aggregation mechanism to collectively respond to requests. Compared to state-of-the-art relay schemes, our approach does not incur any ongoing costs and since the on-chain component only needs to verify a single signature, we can achieve remarkable cost savings compared to conventional oracle solutions.
Data-efficient image captioning of fine art paintings via virtual-real semantic alignment training

The image captioning of fine art paintings aims at generating a sentence to describe the painting content. Compared with photographic images, there are few annotated data of clear and precise content descriptions for paintings. Besides, painting images usually have abstract expressions, making it hard to extract their representative features. In this paper, we propose a virtual-real semantic alignment training process to address these challenges in painting captioning. To provide sufficient training data, we generate a virtual painting captioning dataset by applying style transfer to a large-scale photographic image captioning dataset and maintaining their annotations. To tackle the difficulty of abstract expressions, we employ a semantic alignment loss between photographic image features and virtual painting features to guide the training of the painting feature extractor. We evaluate our method in two data-hungry scenarios where only a few or no annotated painting data for training. According to the evaluation results on a public painting captioning dataset and our annotated painting captioning dataset, our model achieves significant improvements and higher data efficiency than the baselines in the two data-hungry scenarios on all datasets.
Consensus graph and spectral representation for one-step multi-view kernel based clustering

Recently, multi-view clustering has received much attention in the fields of machine learning and pattern recognition. Spectral clustering for single and multiple views has been the common solution. Despite its good clustering performance, it has a major limitation: it requires an extra step of clustering. This extra step, which could be the famous k-means clustering, depends heavily on initialization, which may affect the quality of the clustering result. To overcome this problem, a new method called Multi-view Clustering via Consensus Graph Learning and Nonnegative Embedding (MVCGE) is presented in this paper. In the proposed approach, the consensus affinity matrix (graph matrix), consensus representation and cluster index matrix (nonnegative embedding) are learned simultaneously in a unified framework. Our proposed method takes as input the different kernel matrices corresponding to the different views. The proposed learning model integrates two interesting constraints: (i) the cluster indices should be as smooth as possible over the consensus graph and (ii) the cluster indices are set to be as close as possible to the graph convolution of the consensus representation. In this approach, no post-processing such as k-means or spectral rotation is required. Our approach is tested with real and synthetic datasets. The experiments performed show that the proposed method performs well compared to many state-of-the-art approaches.
DeepLineDP: Towards a Deep Learning Approach for Line-Level Defect Prediction

Defect prediction is proposed to assist practitioners effectively prioritize limited Software Quality Assurance (SQA) resources on the most risky files that are likely to have post-release software defects. However, there exist two main limitations in prior studies: (1) the granularity levels of defect predictions are still coarse-grained and (2) the surrounding tokens and surrounding lines have not yet been fully utilized. In this paper, we perform a survey study to better understand how practitioners perform code inspection in modern code review process, and their perception on a line-level defect prediction. According to the responses from 36 practitioners, we found that 50% of them spent at least 10 minutes to more than one hour to review a single file, while 64% of them still perceived that code inspection activity is challenging to extremely challenging. In addition, 64% of the respondents perceived that a line-level defect prediction tool would potentially be helpful in identifying defective lines. Motivated by the practitioners’ perspective, we present DeepLineDP, a deep learning approach to automatically learn the semantic properties of the surrounding tokens and lines in order to identify defective files and defective lines. Through a case study of 32 releases of 9 software projects, we find that the risk score of code tokens varies greatly depending on their location. Our DeepLineDP is 17%-37% more accurate than other file-level defect prediction approaches; is 47%-250% more cost-effective than other line-level defect prediction approaches; and achieves a reasonable performance when transferred to other software projects. These findings confirm that the surrounding tokens and surrounding lines should be considered to identify the fine-grained locations of defective files (i.e., defective lines).
A Blockchain and Metaheuristic-Enabled Distributed Architecture for Smart Agricultural Analysis and Ledger Preservation Solution: A Collaborative Approach

Distributed forecasting of agriculture commodity prices has an attractive research perspective that delivers active breakthrough analysis of the rapid fluctuations in pricing forecasts for participating stakeholders without being manually dispatched lists. The increased use of an efficient forecasting mechanism for the agriculture information management of generated records and processing creates emerging challenges and limitations. These include new government mandates and regulations, the price of land for expansion, forecasting the growing demand for commodities, fluctuations in the global financial market, food security, and bio-based fuels. Building and deploying distributed dynamic scheduling, management, and monitoring systems of agricultural activities for commodity price forecasting and supply chains require a significant secure and efficient approach. Thus, this paper discusses a collaborative approach where two different folds are demonstrated to cover distinct aspects with different objectives. A metaheuristic-enabled genetic algorithm is designed to receive day-to-day agricultural production details and process and analyze forecast pricing from the records by scheduling, managing, and monitoring them in real-time. The blockchain hyperledger sawtooth distributed modular technology provides a secure communication channel between stakeholders, a private network, protects the forecasting ledger, adds and updates commodity prices, and preserves agricultural information and node transactions in the immutable ledger (IPFS). To accomplish this, we design, develop, and deploy two distinct smart contracts to register the system’s actual stakeholders and allow for the addition of node transactions and exchanges. The second smart contract updates the forecasting commodity pricing ledger and distributes it to participating stakeholders while preserving detailed addresses in storage. The simulation results of the proposed collaborative approach deliver an efficient E-agriculture commodity price forecast with an accuracy of 95.3%. It also maintains ledger transparency, integrity, provenance, availability, and secure operational control and access of agricultural activities.
Federated Deep Learning for Heterogeneous Edge Computing

Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration of advanced sensors with higher computing capabilities and widespread internet availability. These edge devices generate a vast amount of data that can be utilized for better inference. However, due to privacy concerns, communication overhead, processing delay, and security issues, traditional machine learning (ML) algorithms face challenges that work in a centralized fashion where all the available data is accumulated beforehand. Federated learning (FL) is a new distributed on-device learning method that generates a global model through the collaboration of edge devices without compromising data privacy. In this paper, we propose a federated transfer learning (FTL) model considering clients’ heterogeneity in terms of their available computing resources and model architecture. We simulate the training performance of heterogeneous clients and observe that clients with sufficient resources require significantly lower computational time. In turn, the resource-constrained clients take notably higher computational time to accomplish a given task. Inspired by that, we design an FL model that constructs multiple global models based on the available resources of the clients and carries out a separate training process for each of the global models. We demonstrate the effectiveness of our proposed strategy by evaluating our FL model on CFAR100 dataset. Our findings show that training time differs significantly among the heterogeneous clients and assigning multiple global models can notably improve convergence time.
Genetic optimization of quantum annealing

The study of optimal control of quantum annealing by modulating the pace of evolution and by introducing a counterdiabatic potential has gained significant attention in recent times. In this work, we present a numerical approach based on genetic algorithms to improve the performance of quantum annealing, which evades the Landau-Zener transitions to navigate to the ground state of the final Hamiltonian with high probability. We optimize the annealing schedules starting from polynomial ansatz by treating their coefficients as chromosomes of the genetic algorithm. We also explore shortcuts to adiabaticity by computing a practically feasible $k$-local optimal driving operator, showing that even for $k=1$ we achieve substantial improvement of the fidelity over the standard annealing solution. With these genetically optimized annealing schedules and/or optimal driving operators, we are able to perform quantum annealing in relatively short time-scales and with larger fidelity compared to traditional approaches.