Reviewerly/exHarmony huggingface dataset:
Approach to an Equivalent Freight-Based Sustainable Joint-Quotation Strategy for Shipping Blockchain Alliance

To improve the sustainability of the shipping industry, a practice of establishing a new type of shipping alliance based on blockchain has been implemented. In this practice, the following question emerges: How will shipping lines achieve sustainable profit improvement? This paper focuses on the freight decision-making problem by constructing a multi-round joint-quotation strategy. This paper also demonstrates the potential impact of a joint quotation strategy on the blockchain-based open freight market from a theoretical perspective. The numerical experiment results show that, compared with the initial state, the joint quotation strategy can help to stabilize shipping demands and reduce the fluctuation in overall demands. In this strategy, the freight level needs to be high to maximize profits. However, part of the demands will be sacrificed as a result. Moreover, the optimal equilibrium solution under the joint quotation strategy is relatively vulnerable to changes in the competitive relationship among the members of an alliance. In addition, the joint quotation may also be resolutely resisted by the shipper due to monopoly risk, resulting in a major risk of a sharp reduction in demand. The findings in this paper offer a decision-making reference for the sustainable development of the shipping industry.
Smart technologies in hospitality: effects on activity, work design and employment. A case study about chatbot usage

This article aims to overcome the empirical limitations of tourism studies concerning customer experience, to focus on the transformations of work activity. From an ergonomics perspective focusing on work activity in two hotels, the article proposes an analysis about smart technologies based on a case study of a chatbot or conversational agent (CA) and proposes an alternative to implement technology in work design. This study uses a systemic approach and incorporates semi-structured interviews, UX curves with different stakeholders (N=7) from touristic organisation: strategic, operational and design workers. Results show that the different stakeholders do not share the same representation of chatbots. Designers and strategy workers consider the technology as a way to improve the customer experience and reduce work constraints by eliminating tasks and creating a new tasks redistribution. Furthermore, the use of chatbot reveals greater flexibility for the operational staff. We also note reconfigurations of work for both staff and designers in order to allow chatbot evolutivity and efficiency over timeIn the light of these statements, this research shows that the deployment of technology cannot be successful without a comprehensive analysis of the work activity context. This study also highlights the role of human intelligence in the creation of evolving and contextually adapted tourism systems. From a general point of view, the introduction of technologies has led to a profound reorganisation of the stage and backstage tasks. They can also be deleterious to work and service relationship when the conditions of their implementation are not thought out in advance. The article suggests to design these work systems through a human-centred approach by contributing to the development of Artificial Intelligence (AI) technology at the service of humans and their work.
Amateur football analytics using computer vision

In recent years, there has been an interest in visual sports analytics, and especially in player and ball detection, action recognition, and camera pose estimation in various sports. The greatest interest is associated with football or soccer. The purpose of this work is the design and implementation of a low-cost methodology that extracts football analytics from simple TV broadcasts, using solely computer vision techniques. In the paper, we discuss the state of the art in this field and propose an integrated pipeline that solves the problem of player localization in the court and extracts useful insights. Technical details concerning the proposed methods that enhance track player–ball movement and camera pose, along with post-analytic results, are presented. The paper concludes with some future expansions of the proposed pipeline.
STPA-Driven Multilevel Runtime Monitoring for In-Time Hazard Detection

Runtime verification or runtime monitoring equips safety-critical cyber-physical systems to augment design assurance measures and ensure operational safety and security. Cyber-physical systems have interaction failures, attack surfaces, and attack vectors resulting in unanticipated hazards and loss scenarios. These interaction failures pose challenges to runtime verification regarding monitoring specifications and monitoring placements for in-time detection of hazards. We develop a well-formed workflow model that connects system theoretic process analysis, commonly referred to as STPA, hazard causation information to lower-level runtime monitoring to detect hazards at the operational phase. Specifically, our model follows the DepDevOps paradigm to provide evidence and insights to runtime monitoring on what to monitor, where to monitor, and the monitoring context. We demonstrate and evaluate the value of multilevel monitors by injecting hazards on an autonomous emergency braking system model.
Mobile-YOLO: A Lightweight and Efficient Implementation of Object Detector Based on YOLOv4

To address the problems of large number of parameters and high computational complexity in current target detection models, we propose a lightweight target detection model based on YOLOv4 to simple the network structure and reduce parameters, which makes it be suitable for developing on the mobile and embedded devices. In our model, we first use the lightweight network Mobilenetv3 as the backbone feature extraction network, and fuse the depth-separable convolution in the SPP and PANet structure to compress the number of parametres of the model to one-sixth of the original; then the coordinate attention mechanism and adaptive spatial feature fusion mechanism are combined to improve the extraction and fusion capability of the backbone network and Neck module for image features; The SoftPool pooling method and FReLU activation function are also introduced in MobileNetv3 to reduce the information loss of feature maps and improve the ability of the network to capture the spatial information of images. The experimental results on the Pascal VOC dataset show that the improved lightweight target detection model based on YOLOv4 achieves 87.33% accuracy with a lower number of parameters, and reaches the best results in the comparison experiments.
Emotional Reasoning in an Action Language for Emotion-Aware Planning

This paper introduces formal models for emotional reasoning, expressing emotional states and emotional causality, using action reasoning and transition systems. A general framework is defined, comprised of two main components: 1) a model for emotions based on the Appraisal theory of Emotion (AE), and 2) a model for emotional change based on Hedonic Emotion Regulation (HER). A particular transition system is modelled in which states correspond to human emotional states and transitions correspond to restrictive (safe) ways to influence emotions while reducing negative emotional side-effects. The introduced emotional reasoning can be applied to guide a software agent's actions for dealing with emotions while estimating and planning future interactions with humans.
Co-consensus semi-supervised multi-view learning with orthogonal non-negative matrix factorization

Semi-supervised multi-view learning has recently achieved appealing performance with the consensus relation between samples. However, in addition to the relation between samples, the relation between samples and their assemble centroid is also important to the learning. In this paper, we propose a novel model based on orthogonal non-negative matrix factorization, which allows exploring both the consensus relations between samples and between samples and their assemble centroid. Since this model utilizes more consensus information to guide the multi-view learning, it can lead to better performance. Meanwhile, we theoretically derive a proposition about the equivalency between the partial orthogonality and the full orthogonality. Based on this proposition, the orthogonality constraint and the label constraint are simultaneously implemented in the proposed model. Experimental evaluations on five real-world datasets show that our approach outperforms the state-of-the-art methods, where the improvement is 6% average in terms of ARI index.
Explainability-Based Mix-Up Approach for Text Data Augmentation

Text augmentation is a strategy for increasing the diversity of training examples without explicitly collecting new data. Owing to the efficiency and effectiveness of text augmentation, numerous augmentation methodologies have been proposed. Among them, the method based on modification, particularly the mix-up method of swapping words between two or more sentences, is widely used because it can be applied simply and shows good levels of performance. However, the existing mix-up approaches are limited; they do not reflect the importance of the manipulated word. That is, even if a word that has a critical effect on the classification result is manipulated, it is not considered significant in labeling the augmented data. Therefore, in this study, we propose an effective text augmentation technique that explicitly derives the importance of manipulated words and reflects this importance in the labeling of augmented data. The importance of each word, in other words, explainability, is calculated, and this is explicitly reflected in the labeling process of the augmented data. The results of the experiment confirmed that when the importance of the manipulated word was reflected in the labeling, the performance was significantly higher than that of the existing methods.
Individuality Meets Commonality: A Unified Graph Learning Framework for Multi-View Clustering

Multi-view clustering, which aims at boosting the clustering performance by leveraging the individual information and the common information of multi-view data, has gained extensive consideration in recent years. However, most existing multi-view clustering algorithms either focus on extracting the multi-view individuality or emphasize on exploring the multi-view commonality, neither of which can fully utilize the comprehensive information from multiple views. To this end, we propose a novel algorithm named V iew-specific and C onsensus G raph A lignment (VCGA) for multi-view clustering, which simultaneously formulates the multi-view individuality and the multi-view commonality into a unified framework to effectively partition data points. To be specific, the VCGA model constructs the view-specific graphs and the shared graph from original multi-view data and hidden latent representation, respectively. Furthermore, the view-specific graphs of different views and the consensus graph are aligned into an informative target graph, which is employed as a crucial input to the standard spectral clustering method for clustering. Extensive experimental results on six benchmark datasets demonstrate the superiority of our method against other state-of-the-art clustering algorithms.
Electronic Speckle Pattern Interferometry for fatigue crack monitoring

This study aims to monitor the fatigue crack tip through a contactless optical method. A Middle Tension (MT) specimen is initially prepared by submitted it to a cyclic fatigue loading, in order to generate a crack with a specified length. Then, the cracked specimen is statically loaded under a uniaxial tensile condition, and Electronic Speckle Pattern Interferometry (ESPI) is employed to monitor the crack tip. During specimen loading with incremental force values, the behavior of the cracked area is monitored, with displacement and deformation fields acquired for each load increment. The obtained data is used to calculate fracture parameters, such as the Stress Intensity Factor (SIF). For that purpose, an overdeterministic algorithm was developed to calculate SIFs for each measured crack length. The main contribution of the present work is the adoption of a high-resolution optical contactless technique to monitor the fatigue crack tip. The obtained SIF values are compared to the reference solution proposed by ASTM E647 and an acceptable agreement has been verified.
I-Fresh: An IoT-Based System for Predicting the Freshness of Vegetables and Flower

Identifying freshness by color is crucial for the evaluation of vegetables, fruits, or flowers. In this proposed work, an IoT-based framework has been developed which can evaluate the freshness of the object by sensing and analyzing its color. The system can predict the life of the flower or the fruit depending on the evaluated threshold. The fuzzy normal distribution is used to calculate the threshold. Output visualization has been carried out using web-based applications that work on real-time data. A color sensor is used to get the color code of the object. Color of the object depends on ambient temperature and humidity. Temperature and humidity sensor has been incorporated in the model to enhance the accuracy of the obtained result. I-fresh outperforms other state-of-the-art techniques with respect to the accuracy of result, cost-efficiency, and power efficiency.
Combining Base-Learners into Ensembles

Abstract This chapter discusses ensembles of classification or regression models, because they represent an important area of machine learning. They have become popular as they tend to achieve high performance when compared with single models. Besides, they also play an essential role in data-streaming solutions. This chapter starts by introducing ensemble learning and presents an overview of some of its most well-known methods. These include bagging, boosting, stacking, cascade generalization, cascading, delegating, arbitrating and meta-decision trees.
I Enjoy Writing and Playing, Do You?: A Personalized and Emotion Grounded Dialogue Agent Using Generative Adversarial Network

Social chatbots have gained immense popularity, and their appeal lies in their capacity to respond to diverse requests, but also in their ability to develop an emotional connection with users. To develop and promote social chatbots, we need to concentrate on increasing user interaction and consider both the intellectual and emotional quotient in conversational agents. In this work, we propose the task of empathetic, personalized dialogue generation giving the machine the capability to respond emotionally and in accordance with the persona of the user. We design a generative adversarial framework, named EP-GAN (Empathy and Persona aware Generative Adversarial Network) to generate responses that are sensitive to the emotion of the user and corresponds to the persona. The persona information is encoded as memory vectors that, along with the dialogue history, are fed to the decoder for generation. An interactive adversarial learning framework is implemented to verify whether the generated responses elicit the emotion and persona in dialogues. Experimental results show that the EP-GAN framework significantly outperforms the existing baselines for both automatic and manual evaluation. We achieve an improvement of 1 point BLEU-4 and 2 points in the emotion accuracy metric compared to the best performing baseline.
Resources and Benchmarks for Keyword Search in Spoken Audio From Low-Resource Indian Languages

This paper presents the resources and benchmarks developed for keyword search (KWS) in spoken audio from six low-resource Indian languages (from two families), namely Gujarati, Hindi, Marathi, Odia, Tamil, and Telugu.The current work on constructing keywords and building benchmark KWS systems is inspired by the popular IARPA Babel program and the subsequent works on low-resource KWS.The keywords are constructed by taking into account their properties i.e., occurrence, length, and average confusability; and their effects on the evaluation metric -the term-weighted value (TWV).We make use of freely available speech datasets, and reprocess them to create resources for KWS, thereby adding value to the existing speech resources.Four ASR-based KWS systems are built, and their performance is analyzed across the three keyword properties on all the six languages.The prepared keywords and other related resources to replicate our experiments are made available for the public.We believe that the analysis and guidelines provided in this paper will not only help the research community, but also practitioners and engineers to easily create KWS resources for newer languages, datasets, and scenarios.
Layer Pruning for Obtaining Shallower ResNets

Network pruning has become an effective scheme to cut down the network complexity and speed up the inference. Current work mainly focuses on filter pruning, which deletes filters in a structured way. Although the calculation amount is reduced after pruning, the acceleration effect is not obvious. In this letter, we present a layer pruning method to obtain shallower ResNets (LPSR) and significantly reduce the inference latency. Taking advantage of the characteristics of the residual module, we disconnect the unimportant residual mapping and reserve only the identity mapping, thereby equivalently removing the corresponding residual module and achieving the purpose of layer pruning. We adopt Taylor expansion to estimate the impact of disconnecting BN layers in the residual mapping. These impacts are then normalized and used to identify unimportant residual blocks to prune. Comprehensive experimental results demonstrate that LPSR not only reduces the number of parameters and calculations, but also significantly speed up the inference. LPSR improves the accuracy of ResNet-56 on CIFAR-10 from 93.21&#x0025; to 93.40&#x0025;, while reducing 44.71&#x0025; parameters, 52.68&#x0025; calculations and 44.89&#x0025; inference latency.
AUPOD: End-to-End Automatic Poster Design by Self-Supervision

The automatic design has become a popular topic in the application field of computer vision technologies. Previous methods for automatic design are mostly saliency-based, relying on an off-the-shelf model for saliency map detection and hand-crafted aesthetic rules for ranking on multiple proposals. We argue that the multi-stage generation and the excessive reliance on saliency map hindered the progress of pursuing better automatic design solutions. In this work, we explore the possibility of a saliency-free solution in a representative scenario, automatic poster design. We propose a novel end-to-end framework to solve the automatic poster design problem, which is divided into the layout prediction and attributes identification sub-tasks. We design a neural network based on multi-modality feature extraction to learn the two sub-tasks jointly. We train the deep neural network in our framework with automatically extracted supervision from semi-structured posters, bypassing a large amount of required manual labor. Both qualitative and quantitative results show the impressive performance of our end-to-end approach after discarding the explicit saliency detection module. Our system learned on self-supervision performs well on the automatic design by learning aesthetic constraints implicitly in the neural networks.
KaIDA: a modular tool for assisting image annotation in deep learning

Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehensive tool for assistance which is time- consuming, burdensome, and not intuitive. Using the here presented modular Karlsruhe Image Data Annotation (KaIDA) tool, for the first time assisted annotation in various image processing tasks is possible to support users during this process. It aims to simplify annotation, increase user efficiency, enhance annotation quality, and provide additional useful annotation-related functionalities. KaIDA is available open-source at https://git.scc.kit.edu/sc1357/kaida.
An infrared image detection of power equipment based on super‐resolution reconstruction and YOLOv4

Infrared imaging technology is widely used in the fault diagnosis of power equipment. The power equipment images obtained by infrared thermal cameras generally have the characteristics of low resolution, low contrast, similar geometric features, and complex image backgrounds. The traditional infrared image detection methods have problems of low detection accuracy, low real-time performance, and missed and fault detection, all of which will lead to great difficulties in the automatic identification process of infrared images of power equipment. This paper adopted an algorithm based on You Only Look Once (YOLO) v4 to realize the detection of electrical equipment under infrared images. First of all, the authors utilized the Fast Super-Resolution Convolutional Neural Network (FSRCNN) algorithm to perform super-resolution reconstruction on the infrared images of power equipment to achieve image enhancement. Moreover, the lightweight network Mobilenetv3 was selected as the backbone network of YOLOv4 to improve the running speed by reducing network parameters. Finally, the performance of the YOLOv4 algorithm was compared to verify the effectiveness of the algorithm proposed in this paper. The experimental results showed that the proposed method had strong generalization ability and reliable detection of infrared targets of various types of power equipment, with the mean average precision (mAP) reaching 91.3%.
Code Layering for the Detection of Network Covert Channels in Agentless Systems

The growing interest in agentless and serverless environments for the implementation of virtual/container network functions makes monitoring and inspection of network services challenging tasks. A major requirement concerns the agility of deploying security agents at runtime, especially to effectively address emerging and advanced attack patterns. This work investigates a framework leveraging the extended Berkeley Packet Filter to create ad-hoc security layers in virtualized architectures without the need of embedding additional agents. To prove the effectiveness of the approach, we focus on the detection of network covert channels, i.e., hidden/parasitic network conversations difficult to spot with legacy mechanisms. Experimental results demonstrate that different types of covert channels can be revealed with a good accuracy while using limited resources compared to existing cybersecurity tools (i.e., Zeek and libpcap).