Reviewerly/exHarmony huggingface dataset:
A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

Abstract In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.
Comprehensive Cyber Arena; The Next Generation Cyber Range

The cyber domain and all the interdependencies between networked systems form an extremely complex ensemble. Incidents in the cyber domain may have an abundance effect on the physical domain. For example, a cyber attack or an intrusion against an electricity system may affect the performance of healthcare system as well. For organisation's cyber resilience, know-how is the key resource. Cyber security training and exercises have an extremely important role for achieving the required level of know-how in the cyber domain. The old military based-proverb You Fight Like You Train is relevant in the cyber domain. Traditionally, the platform for cyber security training and exercises is called cyber range. Because of the accelerating digitalisation and more complex totality of the cyber domain, also the infrastructure for the cyber security training and exercises is required to be more and more complex. In this paper, the concept of cyber arena, next generation cyber range, is discussed.
M-GWNN: Multi-granularity graph wavelet neural networks for semi-supervised node classification

Graph convolutional neural networks (GCNs) based on spectral-domain have achieved impressive performance for semi-supervised node classification task. Recently, graph wavelet neural network (GWNN) has made a significant improvement for this task. However, GWNN is usually shallow based on a one- or two-hop neighborhood structure, making it unable to obtain sufficient global information to make it better. But, if GWNN merely stacks too many convolutional layers, it produces the phenomenon of the wavelet convolutional filters over-smoothing. To stack this challenge, we propose Multi-granularity Graph Wavelet Neural Networks (M-GWNN), a novel spectral GCNs architecture that leverages the proposed Louvain-variant algorithm and the jump connection to improve the ability of node representations for semi-supervised node classification. We first repeatedly apply the proposed Louvain-variant algorithm to aggregate nodes into supernodes to build a hierarchy of successively coarser graph, further refine the coarsened graph symmetrically back to the original by utilizing the jump connection. Moreover, during this process, multiple layers of GWNN are applied to propagate information across entire networks. The proposed M-GWNN efficiently captures node features and graph topological structures of varying granularity to obtain global information. Furthermore, M-GWNN effectively employs the jump connection to connect receptive fields of varying granularity to alleviate the speed of over-smoothing. Experiments on four benchmark datasets demonstrate the effectiveness of the proposed M-GWNN. Particularly, when only a few labeled nodes are provided on the NELL dataset, M-GWNN achieves up to an average 5.7% performance improvement compared with state-of-the-art methods.
Deep AM-FM: Toolkit for Automatic Dialogue Evaluation

There have been many studies on human-machine dialogue systems. To evaluate them accurately and fairly, many resort to human grading of system outputs. Unfortunately, this is time-consuming and expensive. The study of AM-FM (Adequacy Metric - Fluency Metric) suggests an automatic evaluation metric, that achieves good performance in terms of correlation with human judgements. AM-FM framework intends to measure the quality of dialogue generation along two dimensions with the help of gold references: (1) The semantic closeness of generated response to the corresponding gold references; (2) The syntactic quality of the sentence construction. However, the original formulation of both adequacy and fluency metrics face some technical limitations. The latent semantic indexing (LSI) approach to AM modeling is not scalable to large amount of data. The bag-of-words representation of sentences fails to capture the contextual information. As for FM modeling, the n-gram language model implementation is not able to capture long-term dependency. Many deep learning approaches, such as the long short-term memory network (LSTM) or transformer-based architectures, are able to address these issues well by providing better contextual-aware sentence representations than the LSI approach and achieving much lower perplexity on benchmarking datasets as compared to the n-gram language model. In this paper, we propose deep AM-FM, a DNN-based implementation of the framework and demonstrate that it achieves promising improvements in both Pearson and Spearman correlation w.r.t human evaluation on the bench-marking DSTC6 End-to-end Conversation Modeling task as compared to its original implementation and other popular automatic metrics.
Cross domains adversarial learning for Chinese named entity recognition for online medical consultation

Deep learning methods have been applied to Chinese named entity recognition for the online medical consultation. They require a large number of marked samples. However, no such database is available at present. This paper begins with constructing a larger labelled Chinese texts database for the online medical consultation. Second, a basic framework unit is proposed, which is pre-trained by the transfer learning from both Bidirectional language model and Mask language model trained on the larger unlabelled data. Finally, cross domains adversarial learning (CDAL) for Chinese named entity recognition is proposed to further improve the performance, which not only uses the pre-trained basic framework unit, but also uses the adversarial multi-task learning on both electronic medical record texts and online medical consultation texts. Experimental results validate the effectiveness of CDAL.
PSTIE: Time Information Enhanced Personalized Search

Personalized search aims to improve the search quality by re-ranking the candidate document list based on user's historical behavior. Existing approaches focus on modeling the order information of user's search history by sequential methods such as Recurrent Neural Network (RNN). However, these methods usually ignore the fine-grained time information associated with user actions. In fact, the time intervals between queries can help to capture the evolution of query intent and document interest of users. Besides, the time intervals between past actions and current query can reflect the re-finding tendency more accurately than discrete steps in RNN. In this paper, we propose PSTIE, a fine-grained Time Information Enhanced model to construct more accurate user interest representations for Personalized Search. To capture the short-term interest of users, we design time-aware LSTM architectures for modeling the subtle interest evolution of users in continuous time. We further leverage time in calculating the re-finding possibility of users to capture the long-term user interest. We propose two methods to utilize the time-enhanced user interest into personalized ranking. Experiments on two datasets show that PSTIE can effectively improve the ranking quality over state-of-the-art models.
Siting high solar potential areas using Q-GIS in West Bengal, India

The introduction of renewable energy into the electrical grid has long been attributed as a major step towards the development of a sustainable world. In renewable energy systems, despite there being several sources to extract the energy from, solar energy is considered as a major contributor. For the establishment of solar photovoltaic (PV) plants, considering the amount of investments in terms of capital and skilled labor, it often becomes coherent to investors to ensure maximum profitability out of it. The location of the area where the plant is to be built accounts for a dominating factor affecting its efficiency and hence its actual output even in terms of money. This work portrays a study on the state of West Bengal in India and analyzes the state on a Quantum Geographical Information System (Q-GIS) based model, first by masking out the unsuitable areas for constructing a solar PV plant using Q-GIS, and then further deploying the Analytical Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) tool to obtain a hierarchy of the remaining suitable regions for the construction of the plant. The study concludes Raghunatpur and Belda as the most suitable regions for the establishment of a solar PV plant in the state. The resultant figure obtained using Q-GIS is a directive to investors and policy-makers alike who aim at deploying this model in their future endeavours in the energy domain of the state. This proposed model is a framework, which can be reused in any study of a similar essence.
Secure Collaborative Deep Learning Against GAN Attacks in the Internet of Things

Deep learning makes the Internet-of-Things (IoT) devices more attractive, and in turn, IoT facilitates the resolution of the contradiction between data collection and privacy concerns. IoT devices with small-scale computing power can contribute to model training without sharing data in collaborative learning. However, collaborative learning is susceptible to generative adversarial network (GAN) attack, where an adversary can pretend to be a participant engaging in the model training and learn other participants' data. In this article, we propose a secure collaborative deep learning model which resists GAN attacks. We isolate the participants from the model parameters, and realize the local model training of participants via the interaction mode, ensuring that neither the participants nor the server would have access to each other's data. In particular, we target convolutional neural networks, the most popular network, design specific algorithms for various functionalities in different layers of the network, making it suitable for deep learning environments. To our best knowledge, this is the first work designing specific protocol against GAN attacks in collaborative learning. The results of our experiments on two real data sets show that our protocol can achieve good accuracy, efficiency, and image processing adaptability.
Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning

This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation.
Big Data and the Ethical Implications of Data Privacy in Higher Education Research

Despite the claimed worth and huge interest regarding the increasing volumes of complex data sets and the rewarding promise to improve research, there is, however, a growing concern regarding data privacy that affects both qualitative and quantitative higher education research. Within the contemporary debates on the impact of Big Data on the nature of higher education research and the effective ways to harmonize Big Data practice with privacy restrictions and regulations, this study sets out to qualitatively examine current issues regarding data privacy, anonymity, informed consent and confidentiality in data-centric higher education research, with a focus on the data collector, data subject and data user. We argue that within current regulations, data protection of research subjects concerns more data collection and disclosure and insufficiently describes use, having procedural implications for both the complex nature of higher education (HE) research and the type of research data being collected. We work our argument through an examination of several factors that call for a reconsideration of data privacy and access to private information in HE research. The conclusions indicate that Big Data-centric HE research is increasingly becoming a mainstream research paradigm which needs to address critical data privacy issues before being widely embraced.
PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability

Fashion is an important part of human experience. Events such as interviews, meetings, marriages, etc. are often based on clothing styles. The rise in fashion industry and its effect on social influencing have made outfit compatibility a need. Thus, it necessitates an outfit compatibility model to aid people in clothing recommendation. However, due to the highly subjective nature of compatibility, it is necessary to account for personalization. Our paper devises an attribute-wise interpretable compatibility scheme with personal preference modelling which captures user-item interaction along with general item-item interaction. Our work solves the problem of interpretability in clothing matching by locating the discordant and harmonious attributes between fashion items. Extensive experiment results on IQON3000, a publicly available real-world dataset, verify the effectiveness of the proposed model.
Detecting human—object interaction with multi-level pairwise feature network

Abstract Human–object interaction (HOI) detection is crucial for human-centric image understanding which aims to infer ⟨human, action, object⟩ triplets within an image. Recent studies often exploit visual features and the spatial configuration of a human–object pair in order to learn the action linking the human and object in the pair. We argue that such a paradigm of pairwise feature extraction and action inference can be applied not only at the whole human and object instance level, but also at the part level at which a body part interacts with an object, and at the semantic level by considering the semantic label of an object along with human appearance and human–object spatial configuration, to infer the action. We thus propose a multi-level pairwise feature network (PFNet) for detecting human–object interactions. The network consists of three parallel streams to characterize HOI utilizing pairwise features at the above three levels; the three streams are finally fused to give the action prediction. Extensive experiments show that our proposed PFNet outperforms other state-of-the-art methods on the V-COCO dataset and achieves comparable results to the state-of-the-art on the HICO-DET dataset.
Extending latent semantic analysis to manage its syntactic blindness

Natural Language Processing (NLP) is the sub-field of Artificial Intelligence that represents and analyses human language automatically. NLP has been employed in many applications, such as information retrieval, information processing and automated answer ranking. Semantic analysis focuses on understanding the meaning of text. Among other proposed approaches, Latent Semantic Analysis (LSA) is a widely used corpus-based approach that evaluates similarity of text based on the semantic relations among words. LSA has been applied successfully in diverse language systems for calculating the semantic similarity of texts. LSA ignores the structure of sentences, i.e., it suffers from a syntactic blindness problem. LSA fails to distinguish between sentences that contain semantically similar words but have opposite meanings. Disregarding sentence structure, LSA cannot differentiate between a sentence and a list of keywords. If the list and the sentence contain similar words, comparing them using LSA would lead to a high similarity score. In this paper, we propose xLSA, an extension of LSA that focuses on the syntactic structure of sentences to overcome the syntactic blindness problem of the original LSA approach. xLSA was tested on sentence pairs that contain similar words but have significantly different meaning. Our results showed that xLSA alleviates the syntactic blindness problem, providing more realistic semantic similarity scores.
Foreign financial flows, human capital and economic growth in African developing countries

Abstract This study investigates the relationship between foreign financial flows (i.e., FDI inflows, ODA, and remittances), human capital, and economic growth in African developing countries. Along with the two‐step system GMM estimator, fixed effect panel quantile regression is applied as a robustness check for the sample of 38 African countries over the period 2002–2017. First, FDI inflows emerge with different impacts on economic growth, depending on the level: negative and positive effects at low and high levels, respectively. Interestingly, these effects are moderated by the human capital of hosting countries. Second, ODA and remittances are found as negative factors for economic growth; notably, higher human capital accumulation of receiving countries reduces these negative impacts. Overall, the results imply that foreign financial flows are correlated differently with human capital in influencing economic growth among African countries. Nevertheless, the significant implications are that more attention must be paid to human capital in these countries, and more feasible policies are urgently needed for better absorption of external capital flows.
ComNet: Combinational Neural Network for Object Detection in UAV-Borne Thermal Images

We propose a deep learning-based method for object detection in UAV-borne thermal images that have the capability of observing scenes in both day and night. Compared with visible images, thermal images have lower requirements for illumination conditions, but they typically have blurred edges and low contrast. Using a boundary-aware salient object detection network, we extract the saliency maps of the thermal images to improve the distinguishability. Thermal images are augmented with the corresponding saliency maps through channel replacement and pixel-level weighted fusion methods. Considering the limited computing power of UAV platforms, a lightweight combinational neural network ComNet is used as the core object detection method. The YOLOv3 model trained on the original images is used as a benchmark and compared with the proposed method. In the experiments, we analyze the detection performances of the ComNet models with different image fusion schemes. The experimental results show that the average precisions (APs) for pedestrian and vehicle detection have been improved by 2%~5% compared with the benchmark without saliency map fusion and MobileNetv2. The detection speed is increased by over 50%, while the model size is reduced by 58%. The results demonstrate that the proposed method provides a compromise model, which has application potential in UAV-borne detection tasks.
Eye movements to absent objects during mental imagery and visual memory in immersive virtual reality

Abstract The role of eye movements in mental imagery and visual memory is typically investigated by presenting stimuli or scenes on a two-dimensional (2D) computer screen. When questioned about objects that had previously been presented on-screen, people gaze back to the location of the stimuli, even though those regions are blank during retrieval. It remains unclear whether this behavior is limited to a highly controlled experimental setting using 2D screens or whether it also occurs in a more naturalistic setting. The present study aims to overcome this shortcoming. Three-dimensional (3D) objects were presented along a circular path in an immersive virtual room. During retrieval, participants were given two tasks: to visualize the objects, which they had encoded before, and to evaluate a statement about visual details of the object. We observed longer fixation duration in the area, on which the object was previously displayed, when compared to other possible target locations. However, in 89% of the time, participants fixated none of the predefined areas. On the one hand, this shows that looking at nothing may be overestimated in 2D screen-based paradigm, on the other hand, the looking at nothing effect was still present in the 3D immersive virtual reality setting, and thus it extends external validity of previous findings. Eye movements during retrieval reinstate spatial information of previously inspected stimuli.
Exploring Antecedents and Consequences of Toxicity in Online Discussions

Toxicity in online discussions has been an intriguing phenomenon and an important problem. In this paper, we seek to better understand toxicity dynamics in online discussions via a case study on Reddit that explores the antecedents and consequences of toxicity in text. We inspected two dimensions of toxicity: language toxicity, i.e. how toxic the text itself is; and toxicity elicitation, i.e. how much toxicity it elicits in its response. Through regression analyses on Reddit comments, we found that both author propensity and toxicity in discussion context were strong positive antecedents of language toxicity; meanwhile, language toxicity significantly increased the volume and user evaluation of the discussion in some sub-communities, while toxicity elicitation showed mixed effects. We then discuss how our results help understand and regulate toxicity in online discussions by interpreting the complicated triggers and outcomes of toxicity.