Reviewerly/exHarmony huggingface dataset:
Branding Rhetoric in Times of a Global Pandemic: A Text-Mining Analysis

As the Covid-19 pandemic unfolded, academics and practitioners alike wondered how and to what extent brands should adapt their advertising and communication efforts to remain resonant and to engage their audiences. Gathering and processing a unique data set composed of about 12,000 tweets of 76 leading brands associated with seven Italian industries, this research examines how rhetorical appeals that brands adopted evolved on Twitter during the pandemic. Theoretically, the research is grounded on an institutional logic perspective and resorts to the Aristotelian categories of logos, ethos, and pathos to make sense of the rhetorical appeals brands adopted. Methodologically, we relied on the most recent text-mining protocols in marketing studies. Our analyses outline that during the lockdown brands resorted to emotive appeals characterized by intense ethical and social overtones. Regarding consumers’ response in their social media engagement, we find they appreciated this strategy the brands followed.
Forecasting energy generation in large photovoltaic plants using radial belief neural network

Forecasting the energy generation from the solar power is considered challenging due to inaccuracies in forecasting, reliability issues and substantial economic losses in power systems. Hence, it is necessary to consider wide features from the solar power generation point of view. In this paper, the study uses large features set to feed the deep learning classifier for optimal prediction of energy generation from the photovoltaic (PV) plants. The features selection and prediction modules automates the process of optimal prediction of energy using Radial Belief Neural Network (RBNN). The Restricted Boltzmann Machines (RBM) is used for rule set generation based on the feature extracted and the rule set generation is powered by action-reward based Reinforcement Learning (RL) method. The experiments are conducted with rich set of input features on large PV plants that ranges between 1, 50, 100 and 1000. The performance of the proposed model is compared with various metrics that includes: Root mean squared error (RMSE), normalized root mean squared error (NRMSE), mean bias error (MBE), Mean absolute error (MAE), Maximum absolute error (MaxAE), mean absolute percentage error (MAPE), Kolmogorov–Smirnov test integral (KSI) and OVER metrics, Skewness and kurtosis and variability estimation metrics. The simulation results show that the RBNN offers improved prediction ability with reduced errors than other deep and machine learning classifiers.
A systematic review of hate speech automatic detection using natural language processing

With the multiplication of social media platforms, which offer anonymity, easy access and online community formation and online debate, the issue of hate speech detection and tracking becomes a growing challenge to society, individual, policy-makers and researchers. Despite efforts for leveraging automatic techniques for automatic detection and monitoring, their performances are still far from satisfactory, which constantly calls for future research on the issue. This paper provides a systematic review of literature in this field, with a focus on natural language processing and deep learning technologies, highlighting the terminology, processing pipeline, core methods employed, with a focal point on deep learning architecture. From a methodological perspective, we adopt PRISMA guideline of systematic review of the last 10 years literature from ACM Digital Library and Google Scholar. In the sequel, existing surveys, limitations, and future research directions are extensively discussed.
A Secure Federated Learning framework using Homomorphic Encryption and Verifiable Computing

In this paper, we present the first Federated Learning (FL) framework which is secure against both confidentiality and integrity threats from the aggregation server, in the case where the resulting model is not disclosed to the latter. We do so by combining Homomorphic Encryption (HE) and Verifiable Computing (VC) techniques in order to perform a Federated Averaging operator directly in the encrypted domain (by means of HE) and produce formal proofs that the operator was correctly applied (by means of VC). Due to the simplicity of the aggregation function, we are able to ground our approach in additive HE techniques which are highly mature in terms of security and decently efficient. We also introduce a number of optimizations which allows to reach practical execution performances on the larger deep learning models end of the spectrum. The paper also provides extensive experimental results on the FEMNIST dataset demonstrating that the approach preserves the quality of the resulting models at the cost of practically meaningful computing and communication overheads, at least in the cross-silo setting for which higher-end machines can be involved on both the client and server sides.
Missing value imputation through shorter interval selection driven by Fuzzy C-Means clustering

The presence of missing data is a common and pivotal issue, which generally leads to a serious decrease of data quality and thus indicates the necessity to effectively handle missing data. In this paper, we propose a missing value imputation approach driven by Fuzzy C-Mean clustering to improve the classification accuracy by referring only to the known feature values of some selected instances. In particular, the missing values for each instance are imputed by selecting a shorter interval based on the cluster membership value within the certain threshold limit of each feature, while using a short interval is considered to improve the imputation effectiveness and get more accurate estimation of the values in comparison with using a long interval. Our method is evaluated through comparing with state-of-the-art imputation methods on UCI datasets. The experimental results demonstrate that the proposed approach performs closely to or better than those state-of-the-art imputation methods.
Security and Privacy for Healthcare Blockchains

Healthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration in a decentralized open healthcare network environment. Although the healthcare blockchain technology has attracted broad interests and attention in industry, government and academia, the security and privacy concerns remain the focus of debate when deploying blockchains for information sharing in the healthcare sector from business operation to research collaboration. This article focuses on the security and privacy requirements for medical data sharing using blockchain, and provides a comprehensive analysis of the security and privacy risks and requirements, accompanied by technical solution techniques and strategies. First, we discuss the security and privacy requirements and attributes required for electronic medical data sharing by deploying the healthcare blockchain. Second, we categorize existing efforts into three reference blockchain usage scenarios for electronic medical data sharing, and discuss the technologies for implementing these security and privacy properties in the three categories of usage scenarios for healthcare blockchain, such as anonymous signatures, attribute-based encryption, zero-knowledge proofs, verification techniques for smart contract security. Finally, we discuss other potential blockchain application scenarios in healthcare sector. We conjecture that this survey will help healthcare professionals, decision makers, and healthcare service developers to gain technical and intuitive insights into the security and privacy of healthcare blockchains in terms of concepts, risks, requirements, development and deployment technologies and systems.
Performance Evaluation of Deep Learning Compilers for Edge Inference

Recently, edge computing has received considerable attention as a promising means to provide Deep Learning (DL) based services. However, due to the limited computation capability of the data processing units (such as CPUs, GPUs, and specialized accelerators) in edge devices, using the devices’ limited resources efficiently is a challenge that affects deep learning-based analysis services. This has led to the development of several inference compilers such as TensorRT, TensorFlow Lite, Relay, and TVM, which optimize DL inference models specifically for edge devices. These compilers operate on the standard DL models available for inferencing in various frameworks, e.g., PyTorch, TensorFlow, Caffe, PaddlePaddle, and transform them into a corresponding lightweight model. TensorFlow Lite and TensorRT are considered state-of-the-art inference compilers and encompass most of the compiler optimization techniques that have been proposed for edge computing. This paper presents a detailed performance study of TensorFlow Lite (TFLite) and TensorFlow TensorRT (TF-TRT) using commonly employed DL models for edge devices on varying hardware platforms. The work compares throughput, latency performance, and power consumption. We find that the integrated TF-TRT consistently performs better at the high precision floating point on different DL architectures, especially with GPUs using tensor cores. However, it loses its edge for model compression to TFLite at low precision. TFLite which is primarily designed for mobile applications, performs better with lightweight DL models than the deep neural network-based models. It is the first detailed performance comparison of TF-TRT and TFLite inference compilers to the best of our knowledge.
When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks

Human pose estimation is a fundamental yet challenging task in computer vision, which aims at localizing human anatomical keypoints. However, unlike human vision that is robust to various data corruptions such as blur and pixelation, current pose estimators are easily confused by these corruptions. This work comprehensively studies and addresses this problem by building rigorous robust benchmarks, termed COCO-C, MPII-C, and OCHuman-C, to evaluate the weaknesses of current advanced pose estimators, and a new algorithm termed AdvMix is proposed to improve their robustness in different corruptions. Our work has several unique benefits. (1) AdvMix is model-agnostic and capable in a wide-spectrum of pose estimation models. (2) AdvMix consists of adversarial augmentation and knowledge distillation. Adversarial augmentation contains two neural network modules that are trained jointly and competitively in an adversarial manner, where a generator network mixes different corrupted images to confuse a pose estimator, improving the robustness of the pose estimator by learning from harder samples. To compensate for the noise patterns by adversarial augmentation, knowledge distillation is applied to transfer clean pose structure knowledge to the target pose estimator. (3) Extensive experiments show that AdvMix significantly increases the robustness of pose estimations across a wide range of corruptions, while maintaining accuracy on clean data in various challenging benchmark datasets.
Efficient high nonlinearity S-box generating algorithm based on third-order nonlinear digital filter

• A third-order nonlinear digital filter with two’s complement arithmetic under discrete sine inputs is designed, which can exhibit strong chaotic behavior. • A novel chaos based S-box generation algorithm is proposed, in which two Lemmas are proposed and proven to construct bijective S-boxes with high nonlinearity. • A novel block cipher algorithm based on the chaotic S-boxes is presented, which is competitive with some of the most advanced algorithms. In this paper, we first design a third-order nonlinear digital filter (3rd-NDF) with twos complement arithmetic, its trajectories under discrete sine inputs are theoretically analyzed. Analysis results show the trajectories can be partitioned into three categories according to the periodicity of symbolic sequences and chaotic behavior can exhibit when the symbolic sequences are aperiodic. According to its Lyapunov exponents and statistical properties, we find not only the chaotic behaviors of the filter are better than many excellent chaotic systems, but also it has good pseudo-randomness. Then, an S-box generation algorithm based on the filter is presented, in which two Lemmas are presented and proven to construct bijective S-boxes with high nonlinearity. To best of our knowledge, similar methods have never been used in the existing studies. In addition, the designed S-boxes show good cryptographic performances in terms of strict avalanche criteria, differential uniformity, bits independence criterion and linear approximation probability. Finally, we present a novel block cipher algorithm based on the chaotic S-boxes. Analysis results show it is competitive with some of the most advanced algorithms.
Self-supervised Video Hashing via Bidirectional Transformers

Most existing unsupervised video hashing methods are built on unidirectional models with less reliable training objectives, which underuse the correlations among frames and the similarity structure between videos. To enable efficient scalable video retrieval, we propose a self-supervised video Hashing method based on Bidirectional Transformers (BTH). Based on the encoder-decoder structure of transformers, we design a visual cloze task to fully exploit the bidirectional correlations between frames. To unveil the similarity structure between unlabeled video data, we further develop a similarity reconstruction task by establishing reliable and effective similarity connections in the video space. Furthermore, we develop a cluster assignment task to exploit the structural statistics of the whole dataset such that more discriminative binary codes can be learned. Extensive experiments implemented on three public benchmark datasets, FCVID, ActivityNet and YFCC, demonstrate the superiority of our proposed approach.
Coarse-to-Fine Semantic Alignment for Cross-Modal Moment Localization

Video moment localization, as an important branch of video content analysis, has attracted extensive attention in recent years. However, it is still in its infancy due to the following challenges: cross-modal semantic alignment and localization efficiency. To address these impediments, we present a cross-modal semantic alignment network. To be specific, we first design a video encoder to generate moment candidates, learn their representations, as well as model their semantic relevance. Meanwhile, we design a query encoder for diverse query intention understanding. Thereafter, we introduce a multi-granularity interaction module to deeply explore the semantic correlation between multi-modalities. Thereby, we can effectively complete target moment localization via sufficient cross-modal semantic understanding. Moreover, we introduce a semantic pruning strategy to reduce cross-modal retrieval overhead, improving localization efficiency. Experimental results on two benchmark datasets have justified the superiority of our model over several state-of-the-art competitors.
BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation

Scene graphs are nodes and edges consisting of objects and object-object relationships, respectively. Scene graph generation (SGG) aims to identify the objects and their relationships. We propose a bidirectional GRU (BiGRU) transformer network (BGT-Net) for the scene graph generation for images. This model implements novel object-object communication to enhance the object information using a BiGRU layer. Thus, the information of all objects in the image is available for the other objects, which can be leveraged later in the object prediction step. This object information is used in a transformer encoder to predict the object class as well as to create object-specific edge information via the use of another transformer encoder. To handle the dataset bias induced by the long-tailed relationship distribution, softening with a log-softmax function and adding a bias adaptation term to regulate the bias for every relation prediction individually showed to be an effective approach. We conducted an elaborate study on experiments and ablations using open-source datasets, i.e., Visual Genome, Open-Images, and Visual Relationship Detection datasets, demonstrating the effectiveness of the proposed model over state of the art.
SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images

The detection of nuclei and cells in histology images is of great value in both clinical practice and pathological studies. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional object detection methods cannot obtain satisfactory performance in many cases. A detection task consists of two sub-tasks, classification and localization. Under the condition of dense object detection, classification is a key to boost the detection performance. Considering this, we propose similarity based region proposal networks (SRPN) for nuclei and cells detection in histology images. In particular, a customised convolution layer termed as embedding layer is designed for network building. The embedding layer is added into the region proposal networks, enabling the networks to learn discriminative features based on similarity learning. Features obtained by similarity learning can significantly boost the classification performance compared to conventional methods. SRPN can be easily integrated into standard convolutional neural networks architectures such as the Faster R-CNN and RetinaNet. We test the proposed approach on tasks of multi-organ nuclei detection and signet ring cells detection in histological images. Experimental results show that networks applying similarity learning achieved superior performance on both tasks when compared to their counterparts. In particular, the proposed SRPN achieve state-of-the-art performance on the MoNuSeg benchmark for nuclei segmentation and detection while compared to previous methods, and on the signet ring cell detection benchmark when compared with baselines. The sourcecode is publicly available at: https://github.com/sigma10010/nuclei_cells_det.
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study

We introduce a procedural content generation (PCG) framework at the intersections of experience-driven PCG and PCG via reinforcement learning, named ED(PCG)RL, EDRL in short. EDRL is able to teach RL designers to generate endless playable levels in an online manner while respecting particular experiences for the player as designed in the form of reward functions. The framework is tested initially in the Super Mario Bros game. In particular, the RL designers of Super Mario Bros generate and concatenate level segments while considering the diversity among the segments. The correctness of the generation is ensured by a neural net-assisted evolutionary level repairer and the playability of the whole level is determined through AI-based testing. Our agents in this EDRL implementation learn to maximise a quantification of Koster's principle of fun by moderating the degree of diversity across level segments. Moreover, we test their ability to design fun levels that are diverse over time and playable. Our proposed framework is capable of generating endless, playable Super Mario Bros levels with varying degrees of fun, deviation from earlier segments, and playability. EDRL can be generalised to any game that is built as a segment-based sequential process and features a built-in compressed representation of its game content.
LOHO: Latent Optimization of Hairstyles via Orthogonalization

Hairstyle transfer is challenging due to hair structure differences in the source and target hair. Therefore, we propose Latent Optimization of Hairstyles via Orthogonalization (LOHO), an optimization-based approach using GAN inversion to infill missing hair structure details in latent space during hairstyle transfer. Our approach decomposes hair into three attributes: perceptual structure, appearance, and style, and includes tailored losses to model each of these attributes independently. Furthermore, we propose two-stage optimization and gradient orthogonalization to enable disentangled latent space optimization of our hair attributes. Using LOHO for latent space manipulation, users can synthesize novel photorealistic images by manipulating hair attributes either individually or jointly, transferring the desired attributes from reference hairstyles. LOHO achieves a superior FID compared with the current state-of-the-art (SOTA) for hairstyle transfer. Additionally, LOHO preserves the subject’s identity comparably well according to PSNR and SSIM when compared to SOTA image embedding pipelines. Code is available at https://github.com/dukebw/LOHO.
Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.
Dynamic Contextualized Word Embeddings

Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts.Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and extralinguistic context.Based on a pretrained language model (PLM), dynamic contextualized word embeddings model time and social space jointly, which makes them attractive for a range of NLP tasks involving semantic variability.We highlight potential application scenarios by means of qualitative and quantitative analyses on four English datasets.