Reviewerly/exHarmony huggingface dataset:
Am I Being Bullied on Social Media? An Ensemble Approach to Categorize Cyberbullying

People can easily reveal their aggressive remarks on social media platforms using the anonymity it provides. During the COVID-19 pandemic, the usage of social media has been increased several times according to surveys and people are vulnerable to cyber attacks now more than ever. Prevention of cyberbullying needs careful monitoring and identification. Most of the existing works on cyberbullying detection employed traditional machine learning classifiers with handcrafted fea-tures, and deep learning-based models have made their way in this domain very recently. Categorizing cyberbullying based on traits is a complex task and needs contextual consideration. In this work, we have proposed a new approach to detect cyberbullying on social media platforms using a neural ensemble method of transformer-based architectures with attention mechanism. Our proposed architecture is trained on one balanced and one imbalanced dataset and outperforms the given ML and DNN baselines by a significant margin in both cases. We achieved an average F1-score of 95.59% for five classes and 90.65% for six classes on the Fine-Grained Cyberbullying Dataset (FGCD), and 87.28% on Twitter parsed dataset. Our in-depth results provide great insights into the effectiveness of transformer-based models in cyberbullying detection and paves the way for future researches to combat this serious online issue. We have released our models and code. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Thermal Image Generation for Robust Face Recognition

This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate thermal imaging from visible images, using generative adversarial approaches. Using the proposed generator system, we create a database of synthetic thermal faces composed of more than 100k images corresponding to 3227 individuals. When trained and tested using the synthetic database, the Thermal-FaceNet model obtained a 99.98% accuracy. Furthermore, when tested with a real database, the accuracy was more than 98%, validating the proposed thermal images generator system.
Implementing machine learning: chances and challenges

Abstract Finding and implementing a suitable machine learning (ML) solution to a task at hand has several facets. The technical side of ML has widely been discussed in detail, see, e. g., (Heizmann, M., A. Braun, M. Hüttel, C. Klüver, E. Marquardt, M. Overdick and M. Ulrich. 2020. Artificial Intelligence with Neural Networks in Optical Measurement and Inspection Systems. at – Automatisierungstechnik 68(6): 477–487). This contribution focusses on the industrial implementation issues of ML projects, particularly for machine vision (MV) tasks. Especially in small and medium-sized enterprises (SMEs), resources cannot be activated at will in order to use a new technology like ML. We take this into account by, on the one hand, helping to realistically evaluate the opportunities and challenges involved in implementing ML projects for a given task. On the other hand, we consider not only technical aspects, but also organizational, social and customer-related ones. It is discussed which know-how a company itself has to bring into an ML project and which tasks can also be performed by service providers. Here, it becomes clear that ML techniques can be used at different levels of detail. The question of “make or buy” is therefore also an entrepreneurial one when introducing ML into one’s own products and processes, and must be answered with a view to one’s own possibilities and structures.
Location-aware hazardous litter management for smart emergency governance in urban eco-cyber-physical systems

Smart city management is facing a new challenge from littered face masks during COVID-19 pandemic. Addressing the issues of detection and collection of this hazardous waste that is littered in public spaces and outside the controlled environments, usually associated with biomedical waste, is urgent for the safety of the communities around the world. Manual management of this waste is beyond the capabilities of governments worldwide as the geospatial scale of littering is very high and also because this contaminated litter is a health and safety issue for the waste collectors. In this paper, an autonomous biomedical waste management framework that uses edge surveillance and location intelligence for detection of the littered face masks and predictive modelling for emergency response to this problem is proposed. In this research a novel dataset of littered face masks in various conditions and environments is collected. Then, a new deep neural network architecture for rapid detection of discarded face masks on the video surveillance edge nodes is proposed. Furthermore, a location intelligence model for prediction of the areas with higher probability of hazardous litter in the smart city is presented. Experimental results show that the accuracy of the proposed model for detection of littered face masks in various environments is 96%, while the speed of processing is ten times faster than comparable models. The proposed framework can help authorities to plan for timely emergency response to scattering of hazardous material in residential environments.
Approaching language levels and registers in written Chinese with the Menzerath–Altmann Law

Abstract The length of language units, such as word length or sentence length, plays a critical role in register classification studies. However, in this line of work, little attention has been paid to the relationship between the lengths of language units at different levels. The Menzerath–Altmann law (MAL) reflects the functional relationship between the lengths of linguistic units at different levels, and its parameters were shown to be register-sensitive. This article focuses on two interrelated questions based on the MAL: (1) whether there are variations in the hierarchical relationships between language units at different levels and (2) whether such variations will influence register classifications. The results based on written Chinese show that (1) the MAL fittings at the ‘sentence &amp;gt; clause &amp;gt; word’ levels outperform that at the ‘clause &amp;gt; word &amp;gt; character’ levels and (2) the classifications based on two registers, i.e., Press (reportage) and Science (academic prose), demonstrate that the fitting parameters at the ‘sentence &amp;gt; clause &amp;gt; word’ levels also outperform those at the ‘clause &amp;gt; word &amp;gt; character’ levels. These indicate that the variations of hierarchical relationships between language units at different levels should be considered in register analysis. Further interpretations were given from perspectives of the information-theoretic principle and language evolution.
Neuro-Symbolic Architecture for Experiential Learning in Discrete and Functional Environments

The paper presents a “horizontal neuro-symbolic integration” approach for artificial general intelligence along with elementary representation-agnostic cognitive architecture and explores its usability under the experiential learning framework for reinforcement learning problem powered by “global feedback”.
Which gridded irradiance data is best for modelling photovoltaic power production in Germany?

Model estimates of expected photovoltaic (PV) power production rely on accurate irradiance data. Reanalysis and satellite products freely provide irradiance data with a high temporal and spatial resolution including locations for which no ground-based measurements are available. We assess differences in such gridded irradiance data and quantify the subsequent bias propagation from individual radiation components to capacity factors in a contemporary PV model. PV power production is simulated based on four reanalysis (ERA5, COSMO-REA6, COSMO-REA6pp, COSMO-REA2) and three satellite products (CAMS, SARAH-2, CERES Syn1Deg). The results are compared against simulations using measurements from 30 weather stations of the German Weather Service. We compute metrics characterizing biases in seasonal and annual means, day-to-day variability and extremes in PV power. Our results highlight a bias of −1.4% (COSMO-REA6) to +8.2% (ERA5) in annual and spatial means of PV power production for Germany. No single data set is best in all metrics, although SARAH-2 and the postprocessed COSMO-REA6 data (COSMO-REA6pp) outperform the other products for many metrics. SARAH-2 yields good results in summer, but overestimates PV output in winter by 16% averaged across all stations. COSMO-REA6pp represents day-to-day variability in the PV power production of a simulated PV fleet best and has a particularly small bias of 0.5% in annual means. This is at least in parts due to compensating biases in local and seasonal means. Our results imply that gridded irradiance data should be used with caution for site assessments and ideally be complemented by local measurements.
A Simulated Environment for Robot Vision Experiments

Training on simulation data has proven invaluable in applying machine learning in robotics. However, when looking at robot vision in particular, simulated images cannot be directly used no matter how realistic the image rendering is, as many physical parameters (temperature, humidity, wear-and-tear in time) vary and affect texture and lighting in ways that cannot be encoded in the simulation. In this article we propose a different approach for extracting value from simulated environments: although neither of the trained models can be used nor are any evaluation scores expected to be the same on simulated and physical data, the conclusions drawn from simulated experiments might be valid. If this is the case, then simulated environments can be used in early-stage experimentation with different network architectures and features. This will expedite the early development phase before moving to (harder to conduct) physical experiments in order to evaluate the most promising approaches. In order to test this idea we created two simulated environments for the Unity engine, acquired simulated visual datasets, and used them to reproduce experiments originally carried out in a physical environment. The comparison of the conclusions drawn in the physical and the simulated experiments is promising regarding the validity of our approach.
Multi-Objective Multi-Factorial Evolutionary Algorithm for Container Placement

The use of containerization technology in microservice architecture has become widespread, owing to its potential to support fast deployment of web applications and improve the resource utilization in cloud data centers. Evolutionary algorithms (EAs) have been performed promising on the deployment of applications created using microservices. However, with the growing demand for microservice application, the existing EAs fail to solve the large-scale container placement problem due to the high time complexity and poor scalability. A multi-factorial evolutionary algorithm (MFEA) is proposed in this article, which can evolve multiple optimization problems simultaneously for the container placement problem in heterogeneous cluster environments. First, a system model integrated the heterogeneous clusters, microservices, containers, and four optimization objectives is presented. Then, embedded with local search strategy, a multi-objective container placement MFEA (MOCP-MFEA) algorithm is developed to address the container placement problem. MOCP-MFEA is applied to a variety of container placement problems with different application sizes in heterogeneous cluster environments. Experimental results show that compared with various conventional and evolutionary-based approaches, MOCP-MFEA could shorten optimization time significantly and offer a competitive placement solution for the container placement problem and show a good elasticity in heterogeneous cluster environments. Moreover, the deployment scheme of container to physical machines is crucial to lowering resources wastage.
A Novel Delegated Proof of Work Consensus Protocol

Blockchain is a new technology for storing and transmitting information evolving without centralized control, it uses peer-to-peer networks and allows the constitution of replicated and distributed registers, these registers are secured thanks to cryptography by the linking of blocks to each other. As part of solving the challenges that exist to develop this technology, we present this article introducing significant improvements to this efficient mechanism. These improvements consist of proposing a consensus algorithm called Delegated Proof of Work (DPoW). Thanks to this algorithm, we offer a significant reduction in the very large amount of energy currently consumed in the mining process, we reduce the domination of an element with great computing power, we reinforce security against the 51 % attack, reducing its percentage to 67 %, and we also reduce the influence of false identities created within the network (Sybil Attack). On the other hand, we propose a consensus model for permissioned blockchain, this model is based on the encryption of random values and offers total security to reach a consensus.
An Information Multiplexed Encoder-Decoder Network for Image Captioning in Hindi

Image captioning is a multi-modal problem linking computer vision and natural language processing, which combines image analysis and text generation challenges. In the literature, most of the image captioning works have been accomplished in the English language only. This paper proposes a new approach for image captioning in the Hindi language using deep learning-based encoder-decoder architecture. Hindi, widely spoken in India and South Asia, is the fourth most spoken language globally; it is India’s official language. In recent years, significant advancement has been made in image captioning, utilizing encoder-decoder architectures based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Encoder CNN extracts features from input images, whereas decoder RNN performs language modeling. The proposed encoder-decoder architecture utilizes information multiplexing in the encoder CNN to achieve a performance gain in feature extraction. Extensive experimentation is carried out on the benchmark MSCOCO Hindi dataset, and significant improvements in BLEU score are reported compared to the baselines. Manual human evaluation in terms of adequacy and fluency of the generated captions further establishes the proposed method’s efficacy in generating good quality captions.
Applying diegetic cues to an interactive virtual reality experience

Gaze and attention guiding for application in Cinematic Virtual Reality (CVR) has been a research topic with many contributions in recent years. One promising approach coming out of this research, are hints or cues that are placed within the context of the scene that is currently being viewed in VR. These hints are called “diegetic cues”. However, this approach, as many others, does not account for the possibility of interaction that modern VR technology brings with it. These interactions have the potential to move the viewer from a position of “impartial bystander” to one of “active participant” opening new possibilities for telling stories of various nature (e.g., fictional, or historical). This work, is the attempt to apply research from the field of attention guiding using diegetic cues to an interactive and narrative VR experience that was created using the Unreal Engine and newest VR-Technology. It was also attempted to use an alternative approach to narrative theory than the one being used in traditional filmmaking to explore the possibility of interactive storytelling. As a result, strong indications were found that the two diegetic cues that were implemented successfully enhance the experience of VR users in the interactive environment. The reception of the employed narrative approach also was generally positive.
A hybrid optimization method by incorporating adaptive response strategy for Feedforward neural network

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Contextual emotion detection on text using gaussian process and tree based classifiers

It is challenging for machine as well as humans to detect the presence of emotions such as sadness or disgust in a sentence without adequate knowledge about the context. Contextual emotion detection is a challenging problem in natural language processing. As the use of digital agents have increased in text messaging applications, it is essential for these agents to provide sensible responses to its users. The present work demonstrates the effectiveness of Gaussian process detecting contextual emotions present in a sentence. The results obtained are compared with Decision Tree and ensemble models such as Random Forest, AdaBoost and Gradient Boost. Out of the five models built on a small dataset with class imbalance, it has been found that Gaussian Process classifier predicts emotions better than the other classifiers. Gaussian Process classifier performs better by taking predictive variance into account.
Comparative analysis of activation functions in neural networks

Although the impact of activations on the accuracy of neural networks has been covered in the literature, there is little discussion about the relationship between the activations and the geometry of neural network model. In this paper, we examine the effects of various activation functions on the geometry of the model within the feature space. In particular, we investigate the relationship between the activations in the hidden and output layers, the geometry of the trained neural network model, and the model performance. We present visualizations of the trained neural network models to help researchers better understand and intuit the effects of activation functions on the models.
Blockchain for Aviation Industry: Applications and Used Cases

The aviation industry has remained at the forefront when it comes to technology adaptation and it seems the case for adaptation of blockchain technology. Blockchain technology has appeared as a landmark revolution in data encryption and storage. The distributed ledger system allows the hashes to be available with multiple nodes any time and tampering or modification creates a new hash hence makes it easy to confirm the authenticity of the data files and track the node where the changes happened. Blockchain technology promises a great future for digital data security for digital transactions be it informational or monetary. Industries that deal with information exchange between multiple stakeholders like aviation industry have found multiple applications of blockchain technology, the adaptations in the industry are advancing but still, there is a long way to go. In this chapter, we discuss various transaction situations in the aviation industry where the blockchain technology can be of use or have already been put to practice.
Zero-Trust Security Implementation Using SDP over VPN

VPNs are widely used among organizations for securing their internal networks. This research demonstrates implementing the VPN environment and possible attack vectors of HTTP traffic over VPN connections. We also acknowledged the importance of software-defined perimeter or SDP by implementing it at a very basic level and mitigating the attacks that could be performed in a VPN environment. This research also focuses on the vulnerable area found in between the VPN client to the web server, where implementation of the MITM attack and cryptojacking using CoinIMP API service was successful. The architecture of software-defined perimeter based on zero-trust security model was studied. The client, controller, and gateway operations which are the three main important modules inside the SDP architecture were implemented using python script into three different virtual machines, respectively. Also tried the same attack on SDP environment and proved that software-defined perimeter is resilient to such attacks. SDP is capable of hiding the servers containing sensitive information from unauthorized users. SDP helps to overcome the MITM attacks and cryptojacking. Theoretical and practical implications of the results are discussed.
Advanced Encryption Standard for embedded applications: An FPGA-based implementation using VHDL

Computing systems are ubiquitous in today’s world and hence security has become a seminal factor that should be given more priority along with other parameters. In this context, many cryptographic systems have been emerged of which Advanced Encryption Standard (AES) has been furnished as one of the most prevalent security algorithms now-a-days with a view to fighting against data security as well as ensuring end-to-end secure communication. Moreover, Field-programmable gate array (FPGA) is a reprogrammable device which can be an intriguing option for hardware implementations. This paper implements AES encryption and decryption on FPGA using VHDL (VHSIC Hardware Description Language) in ModelSim software. The design process employs iterative looping having block and key size of 128 bits and takes help from an S-box lookup table implementation. Later on, all the simulation results along with the calculation of latency and throughput have been presented. The S-box computation has been done in such a way so that it reduces the latency and gives low complexity architecture of the hardware implementation.
A Simple Image Encryption Based on Binary Image Affine Transformation and Zigzag Process

In this paper, we propose a new and simple method for image encryption. It uses an external secret key of 128 bits long and an internal secret key. The novelties of the proposed encryption process are the methods used to extract an internal key to apply the zigzag process, affine transformation, and substitution-diffusion process. Initially, an original gray-scale image is converted into binary images. An internal secret key is extracted from binary images. The two keys are combined to compute the substitution-diffusion keys. The zigzag process is firstly applied on each binary image. Using an external key, every zigzag binary image is reflected or rotated and a new gray-scale image is reconstructed. The new image is divided into many nonoverlapping subblocks, and each subblock uses its own key to take out a substitution-diffusion process. We tested our algorithms on many biomedical and nonmedical images. It is seen from evaluation metrics that the proposed image encryption scheme provides good statistical and diffusion properties and can resist many kinds of attacks. It is an efficient and secure scheme for real-time encryption and transmission of biomedical images in telemedicine.