Reviewerly/exHarmony huggingface dataset:
Improved Accuracy for Exploring Text - Based Emotion Recognition in Social Media Conversation Generalized Linear Model Compared with Random Forest

Aim: The primary goal of this research was to evaluate the efficacy of the New Generalized Linear Model (GLM) and the Random Forest Algorithm in identifying the sentiment of social media posts. Materials and Methods: We estimate numerous times using the Generalized Linear Model with a sample size of 10, and using the Random Forest with a sample size of 10, to predict with an accuracy of 93.01%. Results: In this study, the accuracy of the Generalized Linear Model (GLM) Algorithm was found to be 70%, which is significantly higher than the accuracy of the Random Forest Algorithm (85.18%). With a pre-test probability of 80%, p=0.824 (p0.005) is not statistically significant. Conclusion: In summary, the Generalized Linear Model (GLM) outperformed the Random Forest algorithm when it came to examining text-based emotion recognition in social media interaction.
Lack of Accuracy while Visualizing Personage Characteristics on Online Social Media Using Indian Metrics During Epidemic Using Novel Random Forest Algorithm Comparing Over K-Nearest Neighbor Algorithm

The principal objective of this research is to compare the Novel RFC algorithm with KNN algorithm in order to increase the intensity % of personage characteristics identification in order to determine the influence of coronavirus on Twitter users. The accuracy proportion with a 95% standard error, and a G-power of (0.8) was measured many times using the RFC method with representative sample=10 and the KNN method with representative sample=10. To forecast data for classification and regression models, the RFC employs a variety of Decision Trees. The likelihood that a data point belongs to one group or another is predicted using a KNN model.As compared to KNN accuracy (69%), RFC accuracy (92%) is superior. The findings obtained with p=0.762 (p>0.05) significance value demonstrate that two groups are statistically significant unimportant.RFC technique performs noticeably better than KNN.
An Approach of Image Feature Extraction Using Obtuse-Angled Triangle Segmentation Using Mathematical Analysis for Enhanced Video Stabilization

In the digital world need for high quality image and reduced size for faster processing and gaining more information has been rapidly developing. This paper deal with the image segmentation technique for attaining high quality and more informative image for any crime scenario. The proposed Obtuse-Angled Triangle segmentation (OATSM) method segment the obtained image in a iterative manner until the image gets segmented into equal rectangle triangles, in the iteration manner edges and mid points are detected and segmented to form 4 & 8 Further segmenting the isosceles triangle to right angled triangle we obtain totally 32 right angled triangle. This segmented rectangle will be further processed individually to obtain a high informative quality image to identify the person in crime scenario.
Photonic Reservoir Computing for Wavelength Multiplexed Nonlinear Fiber Distortion Mitigation

We seek to improve nonlinear fiber distortion mitigation for wavelength multiplexed telecommunications in terms of both processing speed and energy efficiency. We propose a photonic reservoir computing hardware implementation maximizing the chip footprint to processing power ratio by employing a single readout for all wavelengths.
Traceability of Pharmaceutical Products Using Blockchain

Recently, traceability has become a top necessity. By tracking the origin, it establishes a genuine relationship between consumers and manufacturers in terms of the standards and quality of the product. The route taken from the producing facility to the final consumer is the traceability. Implementing block chain technology would be an excellent complement to attain this level of traceability and transparency for pharmaceutical items in the healthcare industry. It prevents unauthorised alterations to the information. Blockchain is a method of storing data in units called blocks that contain cryptographic hashes of earlier blocks to form a chain and a timestamp that keeps track of when a transaction was completed. Each block is protected cryptographically, making it difficult or impossible to alter, hack, or cheat by alrtering it.
Design and testing of a Silicon Photonic Tensor Core with integrated lasers

Here we present a reliable architecture to perform Matrix-Vector Multiplication exploiting the integration capability of Silicon Photonics, providing lasers and photodetector on-chip. By using the GEMM compiler, we can process images for edge detection, with an error rate lower than 7%.
Accelerating quantum technologies with programmable integrated photonics

Programmable photonic integrated circuits (PICs) offer practical solutions to the scaling requirements of quantum technologies for quantum communications and distributed quantum computing. Here we present recent advances in piezoelectrically-actuated PICs demonstrating large-scale optical switching and linear-optic operations for the realization of quantum network systems.
Swapping training optimizers and tiny partial datasets to improve performance of lighter neural networks for edge devices

Most strategies to port ANN on edge devices train large ANN with large datasets on resourceful processors and shrink them (by pruning or using quantization) to fit them on less powerful processors. Those shrunk ANN still usually demand a considerable processor and has lower accuracy. That restricts applications on widely adopted and cheaper 8-bit/16-bit microcontrollers. Also, large datasets make ANN retraining on edge devices unfeasible, shifting it to a cloud environment that makes user experience susceptible to connectivity latencies, restricting some real-time applications. Here, strategies are proposed to train and improve small ANN accuracy by swapping training optimizers and tiny datasets. Around 330,000 ANNs were trained with tiny fractions (<1%) of the MNIST dataset and validated with its complete testing set. Genetic algorithm and tournament heuristics were used to search for the best optimizers combination. Results demonstrated that optimizers combination could improve ANN average accuracy.Moreover, it can achieve similar accuracy with smaller datasets.Among eight optimizers, the top ANNs used most frequently a combination of Adam, Adadelta, and Adamax. This study has potential implications by finding lighter and better ANNs compatible with less powerful edge devices and indicating a research agenda on mixing optimizers for ANN training.
AI Enabled Cloud Service to detect Conversion Fraud in E-commerce

Digital advertising has become very crucial in E-commerce. Artificial display requests or clicks created by malicious codes, bot-nets, and click-firms, pose a significant threat to the advertising ecosystem. Conversion fraud done by attackers directly influences Ad rates, campaign planning, and many other critical components of the Advertising ecosystem as they create fake impressions. Hence conversions need to be monitored and transparent. This research work analyzes different ML Classifiers to detect conversion fraud from log data as an AI Enabled Cloud Service. It was observed that ensemble techniques and Random Forest gives the best results.
MFence: Defending Against Memory Access Interference in a Disaggregated Cloud Memory Platform

A VM-based disaggregated cloud memory platform (DCM) virtualizes the memory device of a remote server connected to a highspeed network as an expansion of local memory. DCM provides large memory to applications to increase throughput. However, DCM is not well-suited to managing fair memory usage between processes when they run concurrently in a VM. This is because DCM has no mechanism to provide independent memory space to each process. As a result, DCM does not guarantee fairness and performance to processes. Partitioning memory for each process is a way to solve this problem. However, in DCM, the host kernel running DCM cannot obtain the memory page information of a process (including memory page address and PID) running in the guest kernel. So it can not segregate memory pages according to the process. Therefore, this paper proposes an efficient method for the host kernel to obtain the memory page information to partition the memory for each process in DCM, called MFence. The MFence was evaluated using two Linux servers connected by a 100 Gbps IB network. Extensive evaluation has confirmed that MFence ideally provides memory partitioning to provide fairness between processes and improve overall performance.
MAFD: A Federated Distillation Approach with Multi-head Attention for Recommendation Tasks

The key challenges that recommendation systems must overcome are data isolation and privacy protection issues. Federated learning can efficiently train global models using decentralized data while preserving privacy. In real-world applications, however, it is difficult to achieve high prediction accuracy due to the heterogeneity of devices, the lack of data, and the limited generalization capacity of models. In this research, we introduce a personalized federated knowledge distillation model for a recommendation system based on a multi-head attention mechanism for recommendation systems. Specifically, we first employ federated distillation to improve the performance of student models and introduce a multi-head attention mechanism to enhance user encoding information. Next, we incorporate Wasserstein distance into the objective function of combined distillation to reduce the distribution gap between teacher and student networks and also use an adaptive learning rate technique to enhance convergence. We show that the proposed approach achieves better effectiveness and robustness through benchmarks.
Convolutional Method for Modeling Video Temporal Context Effectively in Transformer

Video understanding remains a challenging task because video understanding models have many parameters to be trained and should capture detailed spatiotemporal contexts in video effectively. Recent methods have typically employed 3D convolution modules or else self-attention modules. However, we identify that when the self-attention mechanism captures temporal semantics, it often struggles to find out proper temporal context for video understanding. In this paper, we propose a new method for enhancing temporal modeling by incorporating 3D convolution modules into attention-based model, transformer. In particular, we replace the temporal attention of the TimeSformer with a 3D convolution module to improve temporal context learning. In contrast to the TimeSformer, our proposed method can focus on modeling temporal details at the low-level encoders, while gradually getting to focus on temporal contexts more globally at the high-level encoders. Our method surpasses the TimeSformer by 2.2% margin on Something-Something v2, which is required complex temporal modeling for getting high performance.
ACL TA-DA: A Dataset for Text Summarization and Generation

Selecting appropriate natural language datasets is imperative to achieving good performance in deep learning natural language tasks. Recent state-of-the-art language models train huge corpora to achieving high language understanding performances. Also, to conduct diverse NLP tasks, fine-tuning pre-trained language models with task specific datasets is necessary. In this paper, we introduce ACL TA-DA (Association of Computational Linguistics Titles Abstracts DAta) consisting of 22k English titles and corresponding abstracts of papers published in ACL. Two NLP tasks, (1) text summarization and (2) text generation, are suitable tasks for our ACL TA-DA dataset. We train and report results from several state-of-the-art text summarization and generation models with our dataset to demonstrate that our dataset can be widely applied.
Exploiting Machine-learning Prediction for Enabling Real-time Pixel-scaling Techniques in Mobile Camera Applications

Modern people are used to recording more and more videos using camera applications for keeping and sharing their life on social media and video-sharing platforms. To capture extensive multimedia materials, reducing the power consumption of recorded videos from camera applications plays an important role for user experience of mobile devices. This paper studies how to process and display power-saving videos recorded by camera applications on mobile devices in a real-time manner. Based on pixel-scaling methods, we design an appropriate feature map and adopt a visual attention model under the real-time limitation to effectively access attention distribution. Then, based on segmentation properties, a parallel design is appropriately applied to exploit available computation power. Next, we propose a frame-ratio predictor using machine-learning methods to efficiently predict frame ratios in a frame. Finally, the results of the comprehensive experiments conducted on a commercial smartphone with four real-world videos to evaluate the performance of the proposed design are very encouraging.
An Efficient Dual Encryption of IoMT data Using Lightweight Security Scheme for Cloud Based IoT Environment

As Internet of Things (IoT) technology develops, medical equipment, wearables, sensors, and users can be linked together to create an ecosystem known as the Internet of Medical Things (IoMT). IoMT enhances the effectiveness, precision, and affordability of the current healthcare system. It is difficult to guarantee data security and privacy of data in the IoMT ecosystem due to the fact that it incorporates several businesses, heterogeneous networks, and a lot of private information. IoT devices' processing and power requirements make it difficult to ensure their security. In order to secure their data and connections, many IoT systems use asymmetric cryptography and Elliptic Curve Cryptography (ECC). Implementing and deploying these security and privacy solutions for the IoT environment, however, continues to be a substantial problem due to the hardware restrictions of IoT objects. In this paper, we have presented a more effective lightweight cryptographic system based on Elliptic Curve and Dual Cryptography (ECDC) to strengthen ECC and secure communication between IoMT devices. It protects against all kinds of security problems, like authentication, privacy, and integrity, and stops common cyberattacks.
BERTEPro : A new Sentence Embedding Framework for the Education and Professional Training domain

FlauBERT and CamemBERT have established a new state-of-the-art performance for French language understanding. Recently, SBERT has transformed the use of BERT, in order to reduce the computational effort of sentence similarity, while maintaining the accuracy of BERT.
Stateful Adaptive Streams with Approximate Computing and Elastic Scaling

The model of approximate computing can be used to increase performance or optimize resource usage in stream and graph processing. It can be used to satisfy performance requirements (e.g., throughput, lag) in stream processing by reducing the effort that applications need to process datasets. There are currently multiple stream processing platforms, and most of them do not natively support approximate results. A recent one, Stateful Functions, is an API that uses Flink to enable developers to easily build stream and graph processing applications. It also retains Flink's features like stateful computations, fault-tolerance, scalability, control events and its graph processing library Gelly. Herein we present Approxate, an extension over this platform to support approximate results. It can also support more efficient stream and graph processing by allocating available resources adaptively, driven by user-defined requirements on throughput, lag, and latency. This extension enables flexibility in computational trade-offs such as trading accuracy for performance. The user can choose which metrics should be guaranteed at the cost of others, and/or the accuracy. Approxate incorporates approximate computing (using load shedding) with adaptive accuracy and resource manegement in state-of-the-art stream processing platforms, which are not targeted in other relevant related work. It does not require significant modifications to application code, and minimizes imbalance in data source representation when dropping events.
Type-based Termination Analysis for Parsing Expression Grammars

Parsing expressions grammars (PEGs) are a recognition-based formalism for language specification, which has been the subject of several research works. A PEG that succeeds or rejects every input string is said to be complete. However, checking if an arbitrary PEG is complete is an undecidable problem. In this work we propose a sound type-based termination analysis for PEGs as a type inference algorithm.
Analysis of active semi-supervised learning

In many real-world applications, labeled instances are costly and infeasible to obtain large training sets. This way, learning strategies that do the most with fewer labels are calling attention, such as semi-supervised learning (SSL) and active learning (AL). Active learning allows querying instance to be labeled in the uncertain region and semi-supervised learning classify with a small set of labeled data. We combine both strategies to investigate how AL improves SSL performance, considering both classification results and computational cost. We present experimental results comparing five AL strategies on seven benchmark datasets encompassing synthetic data, handwritten digit and image recognition, and brain-computing interaction tasks. The best single AL strategy was the ranked batch mode, but it has the highest computational cost. On the other hand, using a consensus committee approach leads to the highest results and low-processing footprints.
An Exploratory Study About Non-functional Requirements Documentation Practices in Agile Teams

Several companies opt for agile methodologies to enable adjustments and changes to business requirements during the project. However, extensive focus on delivering features might result in a dearth of practices to manage non-functional requirements (NFRs), which often play a vital role in supporting the business. To investigate how agile teams face this issue, we performed a multiple case study with three companies. Our research aims to identify existing practices for NFRs management and verify if specific kinds of NFRs are handled differently. To collect data, we inspected the requirements documentation and conducted semi-structured interviews. As results, we identified five practices concerning NFR documentation. Furthermore, security and usability appeared more frequently than other kinds of NFR in the documentation we inspected. Companies might employ our results to make more informed decisions regarding practices they may use to manage NFRs.
G-HIN2Vec: Distributed heterogeneous graph representations for cardholder transactions

Graph related tasks, such as graph classification and clustering, have been substantially improved with the advent of graph neural networks (GNNs). However, existing graph embedding models focus on homogeneous graphs that ignore the heterogeneity of the graphs. Therefore, using homogeneous graph embedding models on heterogeneous graphs discards the rich semantics of graphs and achieves average performance, especially by utilizing unlabeled information. However, limited work has been done on whole heterogeneous graph embedding as a supervised task. In light of this, we investigate unsupervised distributed representations learning on heterogeneous graphs and propose a novel model named G-HIN2Vec, Graph-Level Heterogeneous Information Network to Vector. Inspired by recent advances of unsupervised learning in natural language processing, G-HIN2Vec utilizes negative sampling technique as an unlabeled approach and learns graph embedding matrix from different pre-defined meta-paths. We conduct a variety of experiments on three main graph downstream applications on different socio-demographic cardholder features, graph regression, graph clustering, and graph classification, such as gender classification, age, and income prediction, which shows superior performance of our proposed GNN model on real-world financial credit card data.
Ontology-Mediated Data Migration: Deriving Migration Rules by Reasoning on Schema Descriptions

Migration of data across information systems is a knowledge intensive task: the definition of mappings between systems requires knowledge of the source and target (relational) schemas and their interpretation of the shared domain. Moreover, direct schema mappings need often to be re-defined for each new migration instance, in order to accommodate the variations caused by the change of systems and representation conventions. A possible solution to such problems is the use of an intermediate ontological model, that can be used as a lingua franca for the description of schemas, by defining mappings from and to the ontology. While this helps in making explicit the semantics of the schemas, the problem remains on how to extract a direct mapping from source to target schema from this intermediate representation.
Discrete Wavelet Coefficient-based Embeddable Branch for Ultrasound Breast Masses Classification

The progress of computer-aid-diagnosis system for ultrasound breast lesions reaches tremendous success in the past few years. However, conventional deep learning-based strategies in recent developments still have challenges particularly in characterizing tumor domain in ultrasound images due to the heterogeneous and complex variations of lesions along with similar intensity exhibited in target object. To address this, this work proposes a discrete wavelet coefficient-based embeddable branch that allows to additionally propagate geometrical features of tumors in an end-to-end trainable fashion. To be elaborate, such branch priorly enforce the wavelet pooling operation to select a certain coefficient to further collect gradient information of target domain. Further, the current work also investigates two different preprocessing strategies in which the internal and external gradients of lesion areas can be emphasized within the transformation. Thus, we examine the effects of the proposed method based on different preprocessing scenarios. To verify the usefulness, GradCam projection, and the cross-validation demonstrate the connection of the proposed branch encourages the importance of target features, thus boosting the overall discrimination between lesion groups. Lastly, the proposed branch can be easily incorporated with existing deep learning-based architectures.