Reviewerly/exHarmony huggingface dataset:
Performative Ornament: Enhancing Humidity and Light Levels for Plants in Multispecies Design

Abstract The paper shifts the design conversation from a human-centered design methodology to a posthuman design, considering human and nonhuman actors. It asks how designers can incorporate a multispecies approach to creating greater intelligence and performance projects. To illustrate this, we describe a project of “ornaments” for plants, culminating from a course in an academic setting. The project methodology starts with “Thing Ethnography” analyzing the movement of a water bottle inside a house and its interaction with different objects. The relationship between water and plant was chosen to be further developed, considering water as a material to increase environmental humidity for the plant and brightness through light reflectance and refraction. 3D printed biomimetic structures as supports for water droplets were designed according to their performance and placed in different arrangements around the plant itself. Humidity levels and illuminance of the structures were measured. Ultimately, this created a new approach for working with plants and mass customization. The paper discusses the resultant evidence-based design and environmental values.
Bearing Remaining Useful Life Prediction Using Federated Learning With Taylor-Expansion Network Pruning

Accurate prediction of bearing remaining useful life (RUL) is essential for machine health management. In existing data-driven prognostic methods, centralized data resources and deep neural networks (DNN) are two requisites. However, conventional data aggregation may result in the privacy disclosure of equipment. Meanwhile, most DNN-based RUL predictors are over-parameterized, making them hard to be deployed on edge devices. To deal with these shortcomings, a new bearing RUL prediction method based on the federated learning and Taylor-expansion network pruning, namely RUL-FLTNP, is proposed in this paper. In this method, a central server and multiple clients work together to train a RUL predictor. First, a multiscale convolutional neural network with a longish full connection in the first layer (LFMCNN) is designed as the RUL predictor. Specifically, LFMCNN contains three units, multiscale feature augmentation module (MFAM), deep feature extraction module (DFEM), and prediction module (PM), where the MFAM is used to extend shallow features from the data stored in each client. Next, the Taylor-expansion pruning criterion is applied to the DFEM to delete unimportant network nodes, after which each client utilizes its local data to recover the pruned model. The server aggregates all rebirth models to a new global model using the Federated Averaging algorithm. Network pruning and rebirth occur alternately in the model training process to produce a compact structure. Experimental results indicate that the proposed method provides a promising solution to prognostic problems in data privacy-preserving scenarios.
Enhancing the Privacy Protection in Locations-based Services through Hybrid LSTM

Location-based services are a call for the provision of useful, tailored information to end users at the time of need. Today, using location-based services (LBS) is a necessary component of our daily life. There are a number of applications of LBS which have major significant in daily lives of a user. Location privacy is a special type of information confidentiality wherein the controlling location information is the core issue. In this paper, a systematic survey is performed over the various methods present so far for privacy protection in LBS. Several possible attacks in LBS are also discussed in this paper. In addition to the systematic survey, a hybrid LSTM model based on deep learning is proposed to increase the effectiveness of the privacy protection techniques in location-based services. The model is expected to provide better performance with improved accuracy and less computational time.
Bernoulli Filter for Extended Target Tracking in the Framework of Possibility Theory

Extended objects give rise to a varying number of noisy measurements from its reflection (scattering or feature) points. Due to imperfect detection, only some of the feature points are detected in each scan of input data, while false alarms can also be present. The optimal sequential Bayesian state estimator in the framework of random set theory is the Bernoulli filter for an extended target (BF-X). In this article, we formulate and derive the analog of the BF-X in the framework of possibility theory, where uncertainty is represented using possibility functions, rather than probability distributions. Possibility functions have the capacity to model partial (imprecise) probabilistic specifications, and thus, the main advantage of the proposed possibilistic BF-X is enhanced robustness in the absence of precise measurements or dynamic models.
Multimedia Security in Audio Signals

The security of Digital media has been varying continuously due to advanced malware attacks. Multimedia security has become one of the major concerns. Earlier works primarily relied on encryption techniques to provide multimedia security, but those works have become obsolete and may be challenged by imposters as technology grows. The proposed work applied watermarking technique in the digital audio signal in which unique data is inserted in one-dimensional data so that it must not affect the significant information of the audio signal. The hybrid-decomposition scheme has been introduced over the audio data to extract features in terms of energy bands. The hiding of the watermark (image data) occurs in a low significant energy band that is stable and contains less information. The watermark extraction method is also introduced in which the hidden watermark data has been extracted back to its original form without leaving any distortion in audio data. The hybrid audio decomposition technique is a scheme in which dual decomposition of host audio signal has been applied containing fast Fourier transform (FFT) and Cordic QR scheme. The disintegration of the audio file will finally result in Q and R matrices under the Cordic QR scheme. Pseudo-random sequence method has been applied to find the correct location from the energy band of the R matrix to embed the watermark data. Before embedding, the watermark first undergoes dual encryption using cyclic coding and Arnold's cat map scheme. The reverse procedure has been applied for the watermark extraction and decryption process. The average PSNR value is 37.8 dB for watermarked audio.
Optimized Hardware Implementation of XXTEA-192 for Resource Constrained Applications

The wide-spread use of computing devices across the country has increased the risks associated with Radio Frequency Identification (RFID) and Internet of Things (IoT) security. For all IoT environments, conventional cryptographic protocols are no longer appropriate. In order to secure data in environments with limited resources, researchers proposed a variety of lightweight cryptographic algorithms. This paper proposes an XXTEA-192-bit round-based architecture to reduce the area of implemented hardware. The proposed design being used for RFID applications is a low-cost, bounded area. XXTEA involves conventional and logical operations like simple shifting, addition, and XOR operations. Because of this simple operation, design a low-area and highly efficient architecture for XXTEA. On various FPGA device families, including Virtex-7, Virtex-5, Virtex-4, and Spartan-3, performance analysis has been done in terms of effectiveness, throughput, etc. With a throughput of 5239.0272 Mbps on vertex-7, the proposed architecture here outperforms other architectures in terms of high frequency, high efficiency, and small area. This is a more effective result.
Deep Learning based Multilabel Hateful Speech Text Comments Recognition and Classification Model for Resource Scarce Ethiopian Language: The case of Afaan Oromo

In response to the phenomenon of hate speech on popular social media such as Facebook, a number of researchers have investigated and developed different automated techniques in order to detect and moderate hate speech. Reliable hate speech detection tools ranging from traditional machine learning models to deep neural network models have been developed and deployed for resource rich languages such as English, French and Arabic. On the Other hand, the topic of building automated techniques for hate speech detection in Afaan Oromo is still the subject of very limited studies. In this study, we have used pretrained word embedding approaches such as word2vec and fast text, and assessed their effectiveness for hateful speech recognition and classification in Afaan Oromo text comments on Facebook. By using the proposed model, the effectiveness of deep learning algorithms like CNN and LSTM was also assessed. Moreover, the significance of one of the recent topic modeling approaches, i.e., BERTopic, has been investigated to in extracting topics that can help the categorization of Afaan Oromo hate speech. Afaan Oromo Multilabel Hateful Speech Detection and Dataset (AOMLHSDD), which contains 30728 text comments, was created after we employed the BERTopic to extract relevant topics. Accordingly, Then, we have conducted different experiments to test the effectiveness of CNN and LSTM models by using word2vec and fast text. The result of our experiments has shown showed that LSTM has outperformed the CNN approach by achieving the accuracy of 98.32%.
Implementation of Siamese Network for Similarity Computation and Prediction of Handwritten Digits

Consistently machines are getting increasingly modern, from evaluation of addition to handwritten text recognition. Machines have made our lives more precise, sensible, and quick. Goal of this paper is to get familiar with a similarity measure to distinguish the distance of data points in vector space and afterward recognize the labels of unlabeled data through label propagation. Thus, proposed technique has matched another digit image and tracking down similarities of same by utilizing a Siamese network. Likeness between the new images from the current trained framework is being determined and has been shown in output structure. Paper has proposed a prediction rule to distinguish the labels of unlabeled datasets with the assistance of few labeled data points and tracking down likenesses. The strategy utilizes pre training of Convolutional Neural Network (CNN) for vector creation on the MNIST digits dataset. This MNIST dataset has 70000 images of manually written digits accessible for experimental and trial investigation. Main aim of this paper is to find the distance of the vector determined for labeling and matching closeness in the image and making a pair of same images.
Comparison of Fractal Image Compression Techniques

Computed tomography (CT) scans have great medical value since it helps clinicians determine clinical staging and surgical range.In offline storage and remote diagnosis, a significant number of CT scan pictures necessitate a huge quantity of storage space and transmission capacity in the Picture Archiving and Communication System (PACS).As a result, in high-quality MRI images, compression is research-oriented.Present compression techniques for CT-scanned images with an increased compression ratio result in data loss on lesions, which may lead to misdiagnosis; compression approaches for CTscanned images and also MRI images with a low compression ratio do not realize the desired effect.As a result, rapid fractalbased image compression in a variety of areas like DWT, DCT, etc. has been reviewed in this paper and its applications in further domains have been discussed.
Image Caption Generation using Deep Residual Learning

The process of creating a written description of an image that describes the action depicted in it is known as image captioning. It is one of the most challenging study areas, and the only method that can help is the machine learning one. Image captioning is a tool where intellectual approach is required for better performance. The suggested work uses CNN, RNN, and Deep Residual Network to propose an image captioning system. RNN serves as the decoder while CNN serves as the encoder. For the MSCOCO benchmark, the system can accurately infer the state of affairs. Deep Residual Network (Deep ResNet), which efficiently utilises the layers and cuts down on computation time, was used to train the model. Deep ResNet, also known as skip connection, bypasses the convolutional layers that provided a solution to the gradient explosion problem. Comparing the Bilingual Evaluation Understudy (BLEU) score to the previously used model, the system perceived a higher score. By lowering the image's size and improving its visibility return good results.
Classification of Yoga Pose using Pre-trained CNN Models and Machine Learning Classifiers

In the development of recent technologies over the years have resulted in a widespread growth in the world of Machine learning or Deep learning techniques due to their high performance for several fields or domains of healthcare applications varying from the prediction of health issues such as pneumonia to cricket score predictions, the impressive performance of Deep Learning techniques, have lingering doubts regarding the robustness of Deep Learning in healthcare settings. especially in light of recent results thathave shown that Deep Learning are vulnerable and more prone to attacks such as adversarial attacks. In this paper, we present an overview of various yoga positions and their correct gestures while practicing yoga which could be easily achieved even when one is away from the instructor or yoga tutor for minimizing or prevention of the injuries caused due to incorrect yoga poses. Finally we provide insight into the current research challenges and promising directions for future research using various convolutional network frameworks such as VGG16, VGG19 and Inception V3.
Analyzing various Machine Learning Algorithms with SMOTE and ADASYN for Image Classification having Imbalanced Data

Oversampling is a strategy employed in machine learning to handle imbalanced datasets by creating copies of the minority class instances to balance the dataset, thus reducing bias and enhancing the accuracy of the model. The work presents various Machine Learning techniques to classify images from an imbalanced dataset. In this paper various oversampling techniques such as ADASYN and SMOTE are blended with the classification algorithms i.e., SVM and CNN with SVM in order to balance imbalanced datasets. The experimentation is performed on the Google Colab using different Machine Learning techniques: SVM, and (CNN and SVM). The results of the experimentation suggests that the amalgamation of SVM and CNN is better than the SVM and SMOTE is better than ADASYN on the basis of performance matrices such as recall, precision, F1 score.
Software Requirement Ambiguity Avoidance Framework (SRAAF) for Selecting Suitable Requirement Elicitation Techniques for Software Projects

Ambiguity is an intrinsic deficiency of natural language written requirement documents. Requirement Engineers face difficulties while communicating with stakeholders from different backgrounds and knowledge to extract requirements. The selection of a suitable requirement elicitation technique plays a critical role to improve the quality of elicited requirements. Differences among elicitation techniques are very large; it implies that some techniques may perform better than others for a particular situation. Generally, requirement engineers select elicitation techniques based on their interests, knowledge, and experience. The selection of the right technique is critical as all techniques can not fit into different situations. In addition, before starting the new project, there is no software available that can help in selecting suitable elicitation techniques based on the situation. This paper proposed a software-based selection of suitable elicitation techniques used in SRAAF (Software Requirement Ambiguity Avoidance Framework). The tool focuses on the first phase of SRAAF, specifically the situational parameter based selection of effective elicitation techniques. These situational parameters are mapped onto the knowledge base for the selection process. The tool takes situational parameters as input related to three categories: project, requirement engineer, and stakeholders. The tool suggests a technique based on situational parameters under the current settings. If any new evidence related to situational parameters, elicitation technique or contextual value is originated, these values or information can be easily included in the tool.
An Emotion based Sentiment Analysis on Twitter Dataset

Sentiment Analysis (SA), which employs data mining techniques to extract information from text and locate knowledge that can be used to track public opinion on certain commodities and social or political events, is one of the newest and most intriguing topics of research. One of the hottest topics in human-machine interaction right now is emotion recognition. In addition to recognizing Twitter gestures, a current research field uses textual data to determine a person's emotional condition. The primary goal of research is to identify emotions in Twitter tweets. The suggested emotion identification system outputs one of the six emotion classes when tweets are submitted (happy, sad, angry, fear, surprise, disgust). The goal is to assemble a predictive analysis of user behavior as well as the attitudes and opinions of the general public on a range of world events. The suggested hybrid emotion identification system also does well with a fresh set of features.
Remote Patient Monitoring Systems Network Adaptation Scenarios

This article is devoted to the actual topic of studying scenarios for adapting a network infrastructure to a network of remote patient monitoring systems (PCMS). The paper presents an overview and analysis of scenarios for adapting the network infrastructure of monitoring systems. The stages of scenario execution have been studied and simulation models of the RPMS network infrastructure have been developed that implement various adaptation scenarios. A comparative analysis of adaptation scenarios was carried out according to several criteria, such as the speed of request delivery, delivery time, and network load. Simulation models built in the Anylogic environment, a powerful simulator of mass service system, showed the effectiveness of the mesh topology in relation to the ad-hoc topology according to several criteria. The theoretical significance of the work lies in carrying out analytical work on scenarios for adapting the network infrastructure of the RPMS, as well as in studying the network architecture. The practical significance of the work lies in the development of a simulation model that allows you to explore scenarios for adapting the network architecture.
A Deep Learning-Based Approach to Image Captioning in Bengali

One of the most popular research topics in the field of computer vision is automatic image captioning. Image captioning is essentially the process of generating textual description from an image, with the description generated based on the objects and actions in the image. A significant amount of effort has gone into creating image captions in English. However, there have only been a few previous works that have been discovered to generate Bangla image captioning for images, and Papers proposed solution has failed to provide satisfactory results. As a result, in this study, Author proposed a deep learning-based model for Bangla image captioning. This Research used a deep learning-based feature extractor named ResNet50 As a proposed model to extract feature vectors from images and an LSTM network to generate text captions using these features. The Research has deployed two datasets the BanglaLekhaImageCaptions dataset, which contains 9000 images, and the Flickr dataset, which contains 8000 images. After training and testing the model with these datasets, It discovered that the model outperforms all previous Bangla image captioning models.
Real-Time Based ANFIS Control of Two Wheel Robotic (TWR) System

This article presents design and control of a highly nonlinear and multivariable two-wheeled robotic (TWR) system. A model of TWR system has been designed and controlled using an Arduino UNO microcontroller equipped with a MPU-6050 sensor. The real-time data for chassis angle and chassis angular velocity was obtained through Arduino Uno controller and was used for training of an Adaptive neuro fuzzy inference system (ANFIS) controller. ANFIS controller has been designed using only seven gbell shape membership function which solved the problem of fuzzy rule explosion by reducing the number of fuzzy rules from 256 to 49 compared to ANFIS controller proposed in one of the previous studies of author. Moreover, through proposed technique it was successfully possible to stabilize the chassis angle and chassis angular velocity within 2.0 sec and 2.5 sec respectively within acceptable overshoot and steady state error limits.
Designing of a Hybrid DWT-SVD Watermarking Technique of Color Images with Digital Signature

An important technological innovation in recent years for recognizing details of copyright holders and enabling multimedia security is digital image watermarking. Watermarking is the most common way of hiding digital information in a carrier color image having secret information. As a result, the host data are secured by introducing watermark data, which an eavesdropper cannot remove or modify. This technique creates highly protected pictures and ensures content authenticity, integrity authentication, including tamper resistance. Various approaches have been put out in recent years, either focused on the spatial, transform, or both domains. A secure multi-level based watermarking is suggested in this paper. The YCbCR format serves as the basis for the color reproduction. The Discrete wavelet transform (DWT)-Singular value decomposition (SVD) domain incorporates a digital signature as a modification. The adaptive DWT level and scaled SVD values are used to implement the watermarking. The performance evaluation has the best setting for the scaling factor alpha in this paper. The performance of the proposed watermarking is tested under Gaussian noise attack.
AI Methodologies in Upcoming Modern Warfare Systems

Worldwide military influences are energetically at work to exhibit a mutual stand to take care of trials from the ocean bed to outer space, using meeting trouble making machinery, along with different tools enabled by AI. The combination of old war aids with AI expertise is pouring the development of an innovative theory of war based on rapid and precise choices, arrangements, and annihilation of the challenger's capability. In this paper, the authors discuss the state of AI algorithm development today and what the future holds, focusing on military applications in particular. The Tactical networks, in particular, have become essential for carrying out military operations specific behaviours or items of interest are a major difficulty for military networks. Innovative AI techniques deployment for improved performance in targeting and tracking concerning Military applications research is still lacking A robust solution to cybersecurity threats in the military with the help of AI has to be worked on with new research trends in AI. In the middle of conducting cyber military operations are the military focusing on the handling & comparing Rules of Engagement that set up the circumstances & captivities for engagement in each operation. Military Operations AI could help military forces by collecting, categorizing, and analyzing information more swiftly and resourcefully than currently possible. AI which has recently been introduced in military applications has been a game changer and had given military upfront to some countries.