Reviewerly/exHarmony huggingface dataset:
A Stackelberg game based two-stage framework to make decisions of freight rate for container shipping lines in the emerging blockchain-based market

To improve the operational efficiency, blockchain technology has been gradually applied into the container shipping industry. As an easier, faster, more efficient platform, blockchain can help to enhance efficiency through digitalizing and easing paperwork. That means, blockchain will make the container shipping market more transparent. Changes in market characteristics will bring a significant problem, how will container shipping lines make decisions in the emerging blockchain-based market? This paper developed a Stackelberg game based two-stage framework to solve the problems. The first stage describes the process of container shipping lines deciding whether to enter the blockchain or not. The second stage describes how container shipping lines decide freight rate in an emerging blockchain-based market. The Cournot game and Stackelberg game are used to solve the second stage model. Spread factor is introduced to reflect the influence of the initial strategy on blockchain strategy. The classical demand function is modified to distinguish the non-blockchain market with more gentle competition from the blockchain market with more intense competition. Numerical experiment is applied to testify this paper. Results show: (1) Entering into the emerging shipping blockchain can be both an opportunity and a challenge for container shipping lines. (2) The ability of filtering function in the low freight rate of shipping blockchain plays a key role in regulating the freight rate and preventing the price war. This paper can provide decision suggestions for shipping lines at the early stage of blockchain introduction.
Detecting research topic trends by author-defined keyword frequency

Detecting research trends helps researchers and decision makers to promptly identify and analyze research topics. However, due to citation and publication delay, previous studies on trend analysis are more likely to identify ex-post trends. In this study, we employ author-defined keywords to represent topics and propose a simple, effective, and ex-ante approach, called author-defined keyword frequency prediction (AKFP), to detect research trends. More specifically, the proposed AKFP relies on the long short-term memory (LSTM) neural network. Four categories of features are proposed as input variables: Temporal feature, Persistence, Community size, and Community development potential. To verify the effectiveness and feasibility of the AKFP, we also proposed a simple but effective method to build a balanced and sufficient data set and conducted extensive comparative experiments, based on data extracted from the ACM Digital Library. Our empirical result confirms the feasibility of word frequency prediction by forecasting precision. Specifically, the short- and medium-term word frequency prediction achieved excellent performance, and the long-term word frequency prediction obtained acceptable prediction accuracy. In addition, we found that these proposed features have a significant but inconsistent impact on the AKFP. Specifically, the temporal feature is always an unignorable factor. The persistence has a strong correlation with the community size, and both are more important in the short- and medium-term prediction. In contrast, the community development potential is particularly significant in the long-term prediction.
Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power

Photovoltaic generation has arisen as a solution for the present energy challenge. However, power obtained through solar technologies has a strong correlation with certain meteorological variables such as solar irradiation, wind speed or ambient temperature. As a consequence, small changes in these variables can produce unexpected deviations in energy production. Although many research articles have been published in the last few years proposing different models for predicting these parameters, the vast majority of them do not consider spatiotemporal parameters. Hence, this paper presents a new solar irradiation forecaster which combines the advantages of machine learning and the optimisation of both spatial and temporal parameters in order to predict solar irradiation 10 min ahead. A validation step demonstrated that the deviation between the actual and forecasted solar irradiation was lower than 4% in 82.95% of the examined days. With regard to the error metrics, the root mean square error was 50.80 W/m2, an improvement of 11.27% compared with the persistence model, which was used as a benchmark. The results indicate that the developed forecaster can be integrated into photovoltaic generators’ to predict their output power, thus promoting their inclusion in the main power network.
Enhanced Boundary Learning for Glass-like Object Segmentation

Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. However, this task is very challenging due to the arbitrary scenes behind glass-like objects. This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning. In particular, we first propose a novel refined differential module that outputs finer boundary cues. We then introduce an edge-aware point-based graph convolution network module to model the global shape along the boundary. We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours. Both modules are lightweight and effective: they can be embedded into various segmentation models. In extensive experiments on three recent glass-like object segmentation datasets, including Trans10k, MSD, and GDD, our approach establishes new state-of-the-art results. We also illustrate the strong generalization properties of our method on three generic segmentation datasets, including Cityscapes, BDD, and COCO Stuff. Code and models will be available for further research.
Defining and detecting toxicity on social media: context and knowledge are key

Online platforms have become an increasingly prominent means of communication. Despite the obvious benefits to the expanded distribution of content, the last decade has resulted in disturbing toxic communication, such as cyberbullying and harassment. Nevertheless, detecting online toxicity is challenging due to its multi-dimensional, context sensitive nature. As exposure to online toxicity can have serious social consequences, reliable models and algorithms are required for detecting and analyzing such communication across the vast and growing space of social media. In this paper, we draw on psychological and social theory to define toxicity. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge in a statistical learning algorithm to resolve ambiguity across such dimensions.
Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper’s main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.
Central Banks’ Monetary Policy in the Face of the COVID-19 Economic Crisis: Monetary Stimulus and the Emergence of CBDCs

This article analyzes the monetary policy of major central banks during the economic crisis generated by the COVID-19 pandemic. Rising public debt in many countries is being financed through asset purchases by monetary authorities. Although these stimulus policies predate the pandemic, they have been significantly boosted as many governments face large financing needs. We have been in a low interest rate environment for years and some governments have issued debt securities at negative rates. In addition, the rise of decentralized cryptocurrencies, based on blockchain technology, has created greater competition in the international monetary system and many governments have considered the creation of centralized virtual currencies, known as central bank digital currencies (CBDCs). We will analyze some relevant cases, with an emphasis on the digital euro project. The methodology is based on the analysis of the evolution of monetary variables. Pearson’s correlation will be used to establish some relationships between them. There is a strong similarity in the expansionary monetary policies of central banks. Although the growth of the money supply has not been passed on to the CPI, it has been passed on to the financial markets and the price of assets such as Bitcoin or gold.
Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media

Sentiment Analysis tools allow decision-makers to monitor changes of opinions on social media towards entities, events, products, solutions, and services.These tools provide dashboards for tracking positive, negative, and neutral sentiments for platforms like Twitter where millions of users express their opinions on various topics.However, so far, these tools do not automatically extract reasons for sentiment variations, and that makes it difficult to conclude necessary actions by decision-makers.In this paper, we first compare performance of various Sentiment Analysis classifiers for short texts to select the top performer.Then we present a Filtered-LDA framework that significantly outperformed existing methods of interpreting sentiment variations on Twitter.The framework utilizes cascaded LDA Models with multiple settings of hyperparameters to capture candidate reasons that cause sentiment changes.Then it applies a filter to remove tweets that discuss old topics, followed by a Topic Model with a high Coherence Score to extract Emerging Topics that are interpretable by a human.Finally, a novel Twitter's sentiment reasoning dashboard is introduced to display the most representative tweet for each candidate reason.
Transfer Learning From Speech Synthesis to Voice Conversion With Non-Parallel Training Data

We present a novel voice conversion (VC) framework by learning from a text-to-speech (TTS) synthesis system, that is called TTS-VC transfer learning or TTL-VC for short. We first develop a multi-speaker speech synthesis system with sequence-to-sequence encoder-decoder architecture, where the encoder extracts the linguistic representations of input text, while the decoder, conditioned on target speaker embedding, takes the context vectors and the attention recurrent network cell output to generate target acoustic features. We take advantage of the fact that TTS system maps input text to speaker independent context vectors, thus re-purpose such a mapping to supervise the training of the latent representations of an encoder-decoder voice conversion system. In the voice conversion system, the encoder takes speech instead of text as the input, while the decoder is functionally similar to the TTS decoder. As we condition the decoder on a speaker embedding, the system can be trained on non-parallel data for any-to-any voice conversion. During voice conversion training, we present both text and speech to speech synthesis and voice conversion networks respectively. At run-time, the voice conversion network uses its own encoder-decoder architecture without the need of text input. Experiments show that the proposed TTL-VC system outperforms two competitive voice conversion baselines consistently, namely phonetic posteriorgram and AutoVC methods, in terms of speech quality, naturalness, and speaker similarity.
Online ensemble learning algorithm for imbalanced data stream

In many practical applications, due to the inability to collect complete training data sets at one time, the adaptability of the classifier is poor. Online ensemble learning can better solve this problem. However, most of the data streams are imbalanced. Imbalanced data stream will greatly affect the performance of online ensemble learning algorithm. To reduce the impact of imbalanced data stream, this paper proposes a cost sensitive online ensemble learning algorithm for imbalanced data stream. The algorithm uses a variety of equalization methods, mainly including the construction of initial base-classifier, dynamic calculation of misclassification cost, sampling method of samples in data stream and calculation of weight of base-classifier. Those methods can reduce the influence of imbalanced data stream and improve the classification performance under imbalanced data stream. The experimental results show that the performance of the proposed algorithm has the better classification performance for imbalanced data stream. Finally, the algorithm is applied to the network intrusion detection, and the simulation experiment on NSL-KDD data set can reduce the missing alarm rate and the false alarm rate. The experimental results show that the algorithm can improve the detection accuracy, especially the recognition rate of unknown intrusion behavior.
Measurement-induced purification in large- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> hybrid Brownian circuits

Competition between unitary dynamics that scrambles quantum information non-locally and local measurements that probe and collapse the quantum state can result in a measurement-induced entanglement phase transition. Here we study this phenomenon in an analytically tractable all-to-all Brownian hybrid circuit model composed of qubits. The system is initially entangled with an equal sized reference, and the subsequent hybrid system dynamics either partially preserves or totally destroys this entanglement depending on the measurement rate. Our approach can access a variety of entropic observables which are distinguished by the averaging procedure, and for concreteness we focus on a particular purity quantity for which the averaging is particularly simple. We represent the purity as a path integral coupling four replicas with twisted boundary conditions. Saddle-point analysis reveals a second-order phase transition corresponding to replica permutation symmetry breaking below a critical measurement rate. The transition is mean-field-like and we characterize the critical properties near the transition in terms of a simple Ising field theory in 0+1 dimensions. In addition to studying the purity of the entire system, we study subsystem purities and relate these results to manifestations of quantum error correction in the model. We also comment on the experimental feasibility for simulating this averaged purity, and corroborate our results with exact diagonalization for modest system sizes.
Nonhuman humanitarianism: when 'AI for good' can be harmful

(2021). Nonhuman humanitarianism: when 'AI for good' can be harmful. Information, Communication & Society: Vol. 24, Life (online): An Introduction to the #AoIR2020 Special Issue, Guest Editors: Jonathon Hutchinson and Aphra Kerr, pp. 850-868.
Multi-level cross-modal interaction network for RGB-D salient object detection

• We propose a novel MCI-Net for RGB-D salient object detection, which can effectively exploit the correlation between RGB images and depth cues, while also exploring multi-level information to boost the SOD performance. • To exploit the correlations across RGB images and depth cues, a CMRM is proposed to integrate cross-modal features. The CMRM is carried out in various of levels of feature spaces for further multi-level feature fusion. • An MLFM is proposed to fuse the features of each level in a bottom-up manner, and an FIM is proposed to propagate the features of the last convolutional layer back to the previous layers to reduce the information lost during downsampling. • Extensive experimental results on six RGB-D SOD benchmark datasets demonstrate the effectiveness of our MCI-Net over 14 state-of-the-art methods. Depth cues with affluent spatial information have been proven beneficial in boosting salient object detection (SOD), while the depth quality directly affects the subsequent SOD performance. However, it is inevitable to obtain some low-quality depth cues due to thelimitations of its acquisition devices, which can inhibit the SOD performance. Besides, existing methods tend to combine RGB images and depth cues in a direct fusion or a simple fusion module, making them not effectively exploit the complex correlations between the two sources. Moreover, few methods design an appropriate module to fully fuse multi-level features, resulting in cross-level feature interaction insufficient. To address these issues, we propose a novel Multi-level Cross-modal Interaction Network ( MCI-Net ) for RGB-D based SOD. Our MCI-Net includes two key components: 1) a cross-modal feature learning network, which is used to learn the high-level features for the RGB images and depth cues, effectively enabling the correlations between the two sources to be exploited; and 2) a multi-level interactive integration network, which integrates multi-level cross-modal features to boost the SOD performance. Extensive experiments on six benchmark datasets demonstrate the superiority of our MCI-Net over 14 state-of-the-art methods, and validate the effectiveness of different components in our MCI-Net . More important, our MCI-Net significantly improves the SOD performance as well as has a higher FPS.
Wearable Sensors based Exertion Recognition using Statistical Features and Random Forest for Physical Healthcare Monitoring

Remote physical activity recognition is gaining popularity as it provides improved healthcare monitoring services without hampering the daily lifestyle of individuals. For smart healthcare, several wearable sensors i.e., inertial measurement unit (IMU), mechanomyography (MMG), electromyography (EMG) and other biosignal devices are used commonly to improve quality of life. In this paper, we have proposed an efficient model including multiple domain features, feature reduction and recognizer engine to provide improved healthcare monitoring. Time-domain and statistical features have been integrated with the system to ensure robustness as a performance measure. These features include zero crossings, abrupt changes, peak to peak, and crest factor. For reducing the dimensionality, feature selection methods i.e. minimal-redundancy-maximal-relevance (MRMR) and novel multi-layer sequential forward selection (MLSFS) have been considered with symbol selection. Improved classification results have been achieved by using bagged random forest technique. This model provides unique prospect to researchers by proposing monitoring mechanism for health disorder i.e., asthenia.
Temporal Augmented Graph Neural Networks for Session-Based Recommendations

Session-based recommendation aims to predict the next item that is most likely to be clicked by an anonymous user, based on his/her clicking sequence within one visit. It becomes an essential function of many recommender systems since it protects privacy. However, as the accumulated session records keep increasing, it becomes challenging to model the user interests since they would drift when the time span is large. Efforts have been devoted to handling dynamic user interests by modeling all historical sessions at one time or conducting offline retraining regularly. These solutions are far from practical requirements in terms of efficiency and capturing timely user interests. To this end, we propose a memory-efficient framework - TASRec. It constructs a graph for each day to model the relations among items. Thus, the same item on different days could have different neighbors, corresponding to the drifting user interests. We design a tailored graph neural network to embed this dynamic graph of items and learn temporal augmented item representations. Based on this, we leverage a sequential neural architecture to predict the next item of a given sequence. Experiments on real-world datasets demonstrate that TASRec outperforms state-of-the-art session-based recommendation methods.
Pathways for climate resilient development: Human well-being within a safe and just space in the 21st century

The concept of climate resilient development pathways (CRDPs) introduced in IPCC’s Fifth Assessment Report remains poorly conceptualised. We have attempted to deepen the conceptualisation of climate resilient development (CRD) or climate compatible development, while charting its pathways through fuzzy cognitive maps (FCMs)-based simulations aided by knowledge based on stakeholders’ insights. We conceptualise CRD as a development embracing mitigation, adaptation and inclusive sustainable development to advance planetary health and well-being for all. The FCMs-based simulations demonstrate that appropriate enabling conditions are critical to the achievement of CRD, the most important of them being (i) the ethics, values, and worldviews shaping CRD’s directions by framing appropriate climate narratives and action; (ii) partnerships and commitment to finance and technology by the governments; (iii) interactions between the actors and arenas of engagement facilitating CRD decisions and actions; and (iv) dimensions of governance at multiple levels involving policy, institutions and practice. Citizens’ defence against climate change as a human right, along with planetary health and well-being, demands synergies while implementing mitigation, adaptation and sustainable development. Short-term decisions and actions related to mitigation, adaptation, and sustainable development could have long-term effects on CRDPs. CRD could entail a societal transformation to eudaimonic living for ensuring universal well-being. The findings of this research could have profound implications for multilateral negotiations.
Emotion recognition model based on facial expressions

Face mining is characterized as the revelation of picture designs in a given congregation of pictures. It is an exertion that generally attracts upon information PC (Personal Computer) vision, picture handling, information mining, AI (Artificial Intelligence), database, and human-made reasoning. Facial acknowledgement breaks down and contemplates the examples from the images of the facial. Facial component extraction is a programmed acknowledgment of human faces by recognizing its highlights, for example, eyebrows, eyes, and lips. In this paper, we are assessing the execution of PCA (Priniciple Component Analysis), GMM (Gaussian Mixture Models), GLCM (Gray Level Co-Occurrence Matrix), and SVM (Support Vector Machines) to perceive seven distinctive outward appearances of two people, for example, angry, sad, happy, disgust, neutral, fear, and surprise in database. Our point is to talk about the best systems that work best for facial acknowledgement. The present investigation demonstrates the plausibility of outward appearance acknowledgement for viable applications like surveillance and human PC communication.