Reviewerly/exHarmony huggingface dataset:
Social Media Hate Speech Detection Using Explainable Artificial Intelligence (XAI)

Explainable artificial intelligence (XAI) characteristics have flexible and multifaceted potential in hate speech detection by deep learning models. Interpreting and explaining decisions made by complex artificial intelligence (AI) models to understand the decision-making process of these model were the aims of this research. As a part of this research study, two datasets were taken to demonstrate hate speech detection using XAI. Data preprocessing was performed to clean data of any inconsistencies, clean the text of the tweets, tokenize and lemmatize the text, etc. Categorical variables were also simplified in order to generate a clean dataset for training purposes. Exploratory data analysis was performed on the datasets to uncover various patterns and insights. Various pre-existing models were applied to the Google Jigsaw dataset such as decision trees, k-nearest neighbors, multinomial naïve Bayes, random forest, logistic regression, and long short-term memory (LSTM), among which LSTM achieved an accuracy of 97.6%. Explainable methods such as LIME (local interpretable model—agnostic explanations) were applied to the HateXplain dataset. Variants of BERT (bidirectional encoder representations from transformers) model such as BERT + ANN (artificial neural network) with an accuracy of 93.55% and BERT + MLP (multilayer perceptron) with an accuracy of 93.67% were created to achieve a good performance in terms of explainability using the ERASER (evaluating rationales and simple English reasoning) benchmark.
Secrecy Driven Federated Learning via Cooperative Jamming: An Approach of Latency Minimization

Federated learning (FL) provides a promising framework for enabling distributed machine learning based services without revealing users’ private data. In the scenario of wireless FL, to counter the eavesdropping attack when the parameter-server (PS, which is co-located with a base station, BS) sends the model-data to the wireless devices, we propose a secrecy driven FL via cooperative jamming, in which wireless devices cooperatively provide jamming to the eavesdropper to enhance the PS’s secure throughput based on the measure of physical layer security. We formulate a joint optimization of the PS’s downloading-transmission duration, all wireless devices’ uploading-transmission duration as a non-orthogonal multiple access cluster, each device’s local processing-rate and transmit powers for its uploading NOMA-transmission and jamming to the eavesdropper, with the objective of minimizing the overall latency for each round of FL iteration. Despite the non-convexity of the joint optimization problem, a layered algorithm is proposed to solve it. Taking into account the special feature of the optimal jamming solution, we further propose a benefit-sharing scheme, which is based on the principle of Nash bargaining solution, such that all wireless devices can benefit from reducing the FL latency via cooperative jamming in a fairness manner. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of our proposed secrecy driven FL via cooperative jamming. Experimental results based on the real data-sets and training models demonstrate that our scheme can reduce the latency by more than 35% compared to the case without using the jamming.
Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach

Nowadays, Artificial Intelligence (AI) based modeling is the major consideration to build efficient, automated, and smart systems for our today's needs. Many companies are benefited from these modeling methods for their marketing efforts. Each firm has expected to increase its product development in an innovative way to improve its business growth. Successful firm marketing is to offer the right product to the right person at the right time. To market the product to the customer successfully, it is needed to segment the customer by finding their behavioral patterns. The customer behaviors and their purchasing patterns are used to generate profit for the company. Customer segmentation is the process of grouping customers based on commonalities. Developing an efficient AI-based customer segmentation to improve digital marketing growth is a challenging task. In this paper, an unsupervised deep learning model called a Self-organizing map with an Improved social spider optimization approach has been used for efficient customer segmentation. The customer data are analyzed by a feature engineering process using a swarm intelligence model called Modified social spider optimization to select the behavioral features of the customer. Then, the customers are clustered using Self Organizing neural network (SONN). Based on the clusters, the customers are classified using the Deep neural network (DNN) model. The experimental results prove the performance of the proposed model with high clustering and segmentation capability to improve the business profit in marketing.
ReFormer: The Relational Transformer for Image Captioning

Image captioning is shown to be able to achieve a better performance by using scene graphs to represent the relations of objects in the image. The current captioning encoders generally use a Graph Convolutional Net (GCN) to represent the relation information and merge it with the object region features via concatenation or convolution to get the final input for sentence decoding. However, the GCN-based encoders in the existing methods are less effective for captioning due to two reasons. First, using the image captioning as the objective (i.e., Maximum Likelihood Estimation) rather than a relation-centric loss cannot fully explore the potential of the encoder. Second, using a pre-trained model instead of the encoder itself to extract the relationships is not flexible and cannot contribute to the explainability of the model. To improve the quality of image captioning, we propose a novel architecture ReFormer- a RElational transFORMER to generate features with relation information embedded and to explicitly express the pair-wise relationships between objects in the image. ReFormer incorporates the objective of scene graph generation with that of image captioning using one modified Transformer model. This design allows ReFormer to generate not only better image captions with the benefit of extracting strong relational image features, but also scene graphs to explicitly describe the pair-wise relationships. Experiments on publicly available datasets show that our model significantly outperforms state-of-the-art methods on image captioning and scene graph generation.
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection

Robust 3D object detection is critical for safe autonomous driving. Camera and radar sensors are synergistic as they capture complementary information and work well under different environmental conditions. Fusing camera and radar data is challenging, however, as each of the sensors lacks information along a perpendicular axis, that is, depth is unknown to camera and elevation is unknown to radar. We propose the camera-radar matching network CramNet, an efficient approach to fuse the sensor readings from camera and radar in a joint 3D space. To leverage radar range measurements for better camera depth predictions, we propose a novel ray-constrained cross-attention mechanism that resolves the ambiguity in the geometric correspondences between camera features and radar features. Our method supports training with sensor modality dropout, which leads to robust 3D object detection, even when a camera or radar sensor suddenly malfunctions on a vehicle. We demonstrate the effectiveness of our fusion approach through extensive experiments on the RADIATE dataset, one of the few large-scale datasets that provide radar radio frequency imagery. A camera-only variant of our method achieves competitive performance in monocular 3D object detection on the Waymo Open Dataset.
Learning Second Order Local Anomaly for General Face Forgery Detection

In this work, we propose a novel method to improve the generalization ability of CNN-based face forgery detectors. Our method considers the feature anomalies of forged faces caused by the prevalent blending operations in face forgery algorithms. Specifically, we propose a weakly supervised Second Order Local Anomaly (SOLA) learning module to mine anomalies in local regions using deep feature maps. SOLA first decomposes the neighborhood of local features by different directions and distances and then calculates the first and second order local anomaly maps which provide more general forgery traces for the classifier. We also propose a Local Enhancement Module (LEM) to improve the discrimination between local features of real and forged regions, so as to ensure accuracy in calculating anomalies. Besides, an improved Adaptive Spatial Rich Model (ASRM) is introduced to help mine subtle noise features via learnable high pass filters. With neither pixel level annotations nor external synthetic data, our method using a simple ResNet18 backbone achieves competitive performances compared with state-of-the-art works when evaluated on unseen forgeries.
Gradient Boosting for Health IoT Federated Learning

Federated learning preserves the privacy of user data through Machine Learning (ML). It enables the training of an ML model during this process. The Healthcare Internet of Things (HIoT) can be used for intelligent technology, remote detection, remote medical care, and remote monitoring. The databases of many medical institutes include a vast quantity of medical information. Nonetheless, based on its specific nature of health information, susceptibilities to private information, and since it cannot be pooled related to data islands, Federated Learning (FL) offers a solution as a shared collaborative artificial intelligence technology. However, FL addresses a series of security and privacy issues. An adaptive Differential Security Federated Learning Healthcare IoT (DPFL-HIoT) model is proposed in this study. We propose differential privacy federated learning with an adaptive GBTM model algorithm for local updates, which helps adapt the model’s parameters based on the data characteristics and gradients. By training and applying a Gradient Boosted Trees model, the GBTM model identifies medical fraud based on patient information. This model is validated to check performance. Real-world experiments show that our proposed algorithm effectively protects data privacy.
The FAIR Cookbook - the essential resource for and by FAIR doers

Abstract The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an open, online resource of hands-on recipes for “FAIR doers” in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. Part of the ELIXIR ecosystem, and recommended by funders, the FAIR Cookbook is open to contributions of new recipes.
Does online access promote research in developing countries? Empirical evidence from article-level data

Universities in developing countries have rarely been able to subscribe to academic journals in the past. The “Online Access to Research in the Environment” initiative (OARE) provides institutions in developing countries with free online access to more than 11,500 environmental science journals. We analyze the effect of OARE on (1) scientific output and (2) scientific input as a measure of accessibility in five developing countries. We apply difference-in-difference-in-differences estimation using a balanced panel with 249,000 observations derived from 36,202 journal articles published by authors affiliated with 2,490 research institutions. Our approach allows us to explore effects across scientific fields, i.e. OARE vs. non-OARE fields, within institutions and before and after OARE registration. Variation in online access to scientific literature is exogenous at the level of scientific fields. We provide evidence for a positive marginal effect of online access via OARE on publication output by 29.6% with confidence interval (18.5%, 40.6%) using the most conservative specification. This adds up to 2.07 additional articles due to the OARE program for an average institution publishing 7.0 articles over the observation period. Moreover, we find that OARE membership eases the access to scientific content for researchers in developing countries, leading to an increase in the number of references by 8.4% with confidence interval (5.6%, 11.2%) and the number of OARE references by 14.5% with confidence interval (7.5%, 21.5%). Our results suggest that productive institutions benefit more from OARE and that the least productive institutions barely benefit from registration.
Multi-Modal Description of Public Safety Events Using Surveillance and Social Media

A public safety event is a danger and urgent event that need early detection, quick response, and accuracy recover. The efficient method for responding to a happening public safety event is to collect and describe the related data. Besides the surveillance cameras from the physical space, the social media data can also be used to collect and describe the related data of a public safety event. In this work, the proposed method focuses on the step for describing public safety events. Given a public safety event, videos from the surveillance cameras and social messages from social sensors are collected. The multi-modal information including texts, images, videos, and spatial-temporal data is mined to give a description precisely and concisely. First, the social sensors are associated to surveillance cameras by the spatial and temporal information. In the second stage, the social messages are associated to surveillance cameras by the semantic information. In the third stage, the social messages are associated to surveillance cameras by the visual feature. Besides the text, social sensors may upload images or videos. Finally, the multi-modal description step is given based on the three different associations. The experiments on the real data demonstrate the superiority of the proposed framework. Case studies on the real public safety event show the proposed model has good performance and high effectiveness.
Dependence Models for Searching Text in Document Images

The main goal of existing word spotting approaches for searching document images has been the identification of visually similar word images in the absence of high quality text recognition output. Searching for a piece of arbitrary text is not possible unless the user identifies a sample word image from the document collection or generates the query word image synthetically. To address this problem, a Markov Random Field (MRF) framework is proposed for searching document images and shown to be effective for searching arbitrary text in real time for books printed in English (Latin script), Telugu and Ottoman scripts. The English experiments demonstrate that the dependencies between the visual terms and letter bigrams can be automatically learned using noisy OCR output. It is also shown that OCR text search accuracy can be significantly improved if it is combined with the proposed approach. No commercial OCR engine is available for Telugu or Ottoman script. In these cases the dependencies are trained using manually annotated document images. It is demonstrated that the trained model can be directly used to resolve arbitrary text queries across books despite font type and size differences. The proposed approach outperforms a state-of-the-art BLSTM baseline in these contexts.
Real-Time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy

Vehicle-to-pedestrian communication could significantly improve pedestrian safety at signalized intersections. However, it is unlikely that pedestrians will typically be carrying a low latency communication-enabled device with an activated pedestrian safety application in their hand-held device all the time. Because of this, multiple traffic cameras at a signalized intersection could be used to accurately detect and locate pedestrians using deep learning, and broadcast safety alerts related to pedestrians to warn connected and automated vehicles around signalized intersections. However, the unavailability of high-performance roadside computing infrastructure and the limited network bandwidth between traffic cameras and the computing infrastructure limits the ability of real-time data streaming and processing for pedestrian detection. In this paper, we describe an edge computing-based real-time pedestrian detection strategy that combines a pedestrian detection algorithm using deep learning and an efficient data communication approach to reduce bandwidth requirements while maintaining high pedestrian detection accuracy. We utilize a lossy compression technique on traffic camera data to determine the tradeoff between the reduction of the communication bandwidth requirements and a defined pedestrian detection accuracy. The performance of the pedestrian detection strategy is measured in relation to pedestrian classification accuracy with varying peak signal-to-noise ratios. The analyses reveal that we detect pedestrians by maintaining a defined detection accuracy with a peak signal-to-noise ratio 43 dB while reducing the communication bandwidth from 9.82 Mbits/sec to 0.31 Mbits/sec, a 31× reduction.
Creative goal modeling for innovative requirements

Context When determining the functions and qualities (a.k.a. requirements) for a system, creativity is key to drive innovation and foster business success. However, creative requirements must be practically operationalized, grounded in concrete functions and system interactions. Requirements Engineering (RE) has produced a wealth of methods centered around goal modeling, in order to graphically explore the space of alternative requirements, linking functions to goals and dependencies. In parallel work, creativity theories from the social sciences have been applied to the design of creative requirements workshops, pushing stakeholders to develop innovative systems. Goal models tend to focus on what is known, while creativity workshops are expensive, require a specific skill set to facilitate, and produce mainly paper-based, unstructured outputs. Objective Our aim in this work is to explore beneficial combinations of the two areas of work in order to overcome these and other limitations, facilitating creative requirements elicitation, supported by a simple extension of a well-known and structured requirements modeling technique. Method We take a Design Science approach, iterating over exploratory studies, design, and summative validation studies. Results The result is the Creative Leaf tool and method supporting creative goal modeling for RE. Conclusion We support creative RE by making creativity techniques more accessible, producing structured digital outputs which better match to existing RE methods with associated analysis procedures and transformations.
Fast and Accurate Action Detection in Videos With Motion-Centric Attention Model

A key factor that makes action detection in videos different from general video classification is human-guided clues, especially motion signals. Since not all the pixels in a video are informative for action recognition, the irrelevant and redundant parts can lead to a lot of noise and be burdensome for both feature extraction and classifier training. This encourages the researchers to seek out the design of the attentive model that can dynamically focus computations on the key spatiotemporal volumes. In this paper, we propose a motion-centric attention model for action detection in videos which imitates the human perception of saccade and fixation procedures while detecting actions in a video. Specifically, we first present a strategy to generate motion-centric locations based on the density peak of motion signals, providing reliable candidates around which actions have high possibilities to occur. Then, we introduce an attention model that conducts the saccade and fixation procedures on these candidates to observe local spatiotemporal visual information, preserve internal comprehension, and produce the action proposals on temporal bounds. Afterward, a classifier with several variants is prepared to classify the action proposals and decide which one to fixate and generate the final predictions. We show how to efficiently train our model to produce fast and accurate action detection, by scanning only a small fraction of locations in a video. The extensive experiments on three challenging datasets show promising results with both accuracy and speed.
Importing z-Tree data into R

The software z-Tree is used for thousands of economic experiments worldwide. z-Tree stores experimental data in a way that minimizes the risk of losing data. However, it may be cumbersome to manually read this data into a statistical package. The purpose of the R-package zTree is to make the process of importing data from z-Tree into the statistical package R transparent, reproducible and simple.
Base-Anchored Model for Highly Scalable and Accessible Compression of Multiview Imagery

We present a compression scheme for multiview imagery that facilitates high scalability and accessibility of the compressed content. Our scheme relies upon constructing at a single base view, a disparity model for a group of views, and then utilizing this base-anchored model to infer disparity at all views belonging to the group. We employ a hierarchical disparity-compensated inter-view transform where the corresponding analysis and synthesis filters are applied along the geometric flows defined by the base-anchored disparity model. The output of this inter-view transform along with the disparity information is subjected to spatial wavelet transforms and embedded block-based coding. Rate-distortion results reveal superior performance to the x.265 anchor chosen by the JPEG Pleno standards activity for the coding of multiview imagery captured by high-density camera arrays.