Reviewerly/exHarmony huggingface dataset:
Healthy Aging: A Deep Meta-Class Sequence Model to Integrate Intelligence in Digital Twin

Objective: The behavior monitoring of older adults in their own home and enabling daily-life activity analysis to healthcare practitioner is a key challenge.Methods and procedures: Our framework replicates the elderly home in digital space which can provide an unobtrusive way to monitor the residentâs daily life activities.The learning challenges posed by different performed activities at home are solved by introducing the deep meta-class sequence model.The notion is to group the set of activities into a single meta-class according to the nature of the activities.It helps the learning process, which is based on long short-term memory (LSTM) to learn feature space abstraction.Each meta-class abstraction is further decomposed to an individual activity performed by the elderly at home.Results: The experiments are carried out over the Center for Advanced Studies in Adaptive Systems dataset and proposed model outperforms as compared to baseline models.Clinical impact: Our findings demonstrate a robust framework to digitally monitor the elderly behavior, which is beneficial for healthcare practitioners to understand the level of support the elderly needed to perform the daily tasks or potential risk of an emergency in their own homes.
Classification of Phishing Website Using Machine Learning Based Proposed Ensemble Model

Phishing is the process of trying to get sensitive data from unauthorized persons, such as usernames, passwords, credit card numbers, and debit card information. Since there is no one method to properly reduce every vulnerability due to the problem of phishing. There are many techniques are frequently utilized to reduce specific attacks. The main aim of this research work is to develop a robust machine learning based model that can detect and classify URL based phishing data. This paper proposed a new ensemble model that is a combination of Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR) using the voting scheme ensemble technique. The proposed ensemble model compared with other individual classifiers like Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Logistic Regression (LR) techniques, and ensemble classifiers like Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost) where our proposed ensemble model achieved remarkable performance of 99.02% accuracy with 10-fold cross validation technique. Finally, machine learning based classification techniques are suitable and robust methods that handle the dynamic nature of phishing techniques and provide an accurate method of classification.
Hybrid Image Captioning Model

Image captioning is implemented using Deep learning and NLP (Natural Language Processing) resulting in producing a description of an image. The proposed model generates a caption for an image using a Convolutional Neural Network (CNN) together with a Recurrent Neural Network (RNN) and area of attention. Previously, the image names were used as keys to map the images with descriptions. In order to achieve high performance, in the proposed model the image caption is based on the relationship between the areas of a picture (attention model), the words used in the caption, and the state of an RNN language model. The approach of progressive loading is employed for the loading of the image dataset. Further, for encoding the image dataset into a feature vector, VGG16 a pre-trained CNN is used. The extracted feature vector is given as input to the RNN model. These image encodings are output to a specific type of RNN model known as Long Short-Term Memory (LSTM) networks. Subsequently, the LSTM works on decoding the feature vector and predicts the sequence of words, resulting in the generation of descriptions or captions. The training performance is measured using one of the model’s quantitative analysis metrics known as BLEU.
An Approach for Anomaly Detection &amp; Prediction in Time-series Telecommunication Data

Anomaly detection is necessary in almost every domain these days. Being able to predict the occurrence of an anomaly ahead of time would also prove to be a good asset for many fields. Especially in Telecommunication, it is like a boon, as using these approaches helps to narrow down the problems. Also, if they know that an anomaly might occur, it will help them be prepared to solve the situation. This paper proposes an approach for the detection and prediction of anomalies in time-series telecom data. In this approach, anomaly detection is done using STL Decomposition, and the residue or deviations that are maximum are classified as anomalies. Then, using the whole dataset, a few data points are forecasted. Next, anomalies are predicted in this newly generated data, using XGBoost on the earlier labelled data. This work also presents a method to identify and alert the average frequency observed in the dataset.
ARIMA time Series Model vs. K-Means Clustering for Cloud Workloads Performance

To develop a thriving ability plan and protect the observable of Internet service providers of cloud environment, the increased heterogeneity brought on by various Cloud workloads, such as Business Data analytics, Big Data, IoT and calls for exact sensors. Although K-Means is an easy and quick clustering technique, it might not fully account for the heterogeneity present in Cloud platform workloads. The Multiple patterns can be found using ARIMA time series Models, which is trained for data prediction on cloud be combined into cohesive, homogenous components that closely resemble the data set’s actual patterns. In order to assess the cluster suggest the two different approaches for heterogeneity in resource utilization of Cloud infrastructure, this study compares ARIMA time series Model and K- Means. Clusters generated using K-Means yield significantly abstracted information, according to Bitbrains’ experiments using Google cluster trace and business-critical workloads. A more accurate clustering with discrete usage boundaries is provided by the ARIMA time series Model. Even though the ARIMA time series Model takes longer to compute than K-Means, it can be employed when a finer-grained characterization and study of the workload is needed.
Evaluation on Utilization and Emaciation of Data Mining Techniques in Information System

Data mining is a critical issue of data extraction and data analysis. Data mining has given answers to numerous fields which include authorities’ data structures additionally. As the era is growing exponentially Zeta byte of data is producing each day. So, it’s far very hard to expecting correct data, we want new strategies and technology to intellectually examine the data. Analyzing such data may be very critical for enhancing the overall performance of information system. In some other way, we can say that data mining strategies are used to extract beneficial data from big data. DM performs an essential position in numerous fields like education, healthcare, enterprise, the clinical arena, etc. The cause is to manipulate a higher knowledge among enterprise targets and consumer needs. This paper evaluates the contributions of numerous authors within side the discipline of data mining. It additionally discusses specific data mining strategies, algorithms within side the discipline of information system.
Software Fault Prediction using Wrapper based Feature Selection Approach employing Genetic Algorithm

Software fault prediction helps in early identification of software faults and as a result it improves the software quality. It uses previous software metrics and fault data as independent features, to detect whether there is a fault in the software or not. Early prediction of software faults saves a lot of money and effort required to correct those faults. But, as the amount of data is very huge, it is essential for feature selection to get the most useful information. In this paper, we proposed a Genetic Algorithm-based feature selection method that identifies the most useful subset of features for classification purposes. We used a combination of Genetic Algorithm with KNN Classifier, Decision Tree Classifier and Naive Bayes Classifier for our experiments. Our results suggest that, by using Genetic Algorithm for feature selection, our prediction accuracy improved in all the three classifiers for all the datasets and also the number of features were reduced.
Metaverse -An overview of daily usage and risks

Metaverse is a new technology that is in rapid development. The new technology is a digital revolution that integrates into every facet of our physical existence. The term “metaverse” was coined to fit this revolutionary vision. Social networks, video conferencing, 3D virtual worlds (such as virtual reality chat), augmented reality applications (such as Pokémon Go), and artificial avatars are just a few examples of computer-mediated virtual environments (such as Upland). Metaverse is composed of two words: Meta: is a Greek prefix that means “after” or “beyond” and Universe. In other words, the Metaverse is a post-reality world, a multiuser environment that integrates actual reality with digital virtuality and describes a possible synthetic ecosystem with physical linkages. This research study provides an overview of the Metaverse’s daily usage, risks, and practical uses. Finally, you will see some provided case studies as suggested improvements that can enhance the Metaverse experience, to make it even more practical.
Computer aided Breast lesions classification system using digitized fine needle aspirate images

According to the WHO, the cases of Breast Cancer are now a global issue as the cases are increasing day by day globally. Hence the early detection of malignant tumor is utmost important to prevent patient’s early demise due to patient’s ignorance. If patient comes to doctor and their application detects the tumor is non cancerous or it is just a benign tumor but actually it is malignant tumor then early detection alone can not play such an important role for cancer prevention. So, It is equally important to use best Machine Learning model, which surely detects malignant as malignant tumor and benign as benign tumor. Such accurate model is also highly required along with early detection of Breast Cancer disease. Our work is dedicated in this direction that our suggested model predicts malignant tumor as malignant and benign as benign tumor with high accuracy than existing model is providing. This paper suggests an architecture which produces high accuracy of the model when applied on Wisconsin Diagnostic Breast Cancer (WDBC) dataset obtained by digitized fine needle aspirate images. Our suggested model is the modified version of SVM. It is providing 63% accuracy when data points are not scaled, 98% when date is correctly scaled and 99% when the data points are correctly scaled with appropriate regularization and K fold validation techniques are applied on Support Vector Machine (SVM) instead of simply using default SVM with default parameters. Evidently, our suggested SVM provides 99% accuracy for detecting tumor as benign or malignant over WDBC dataset obtained by digitized fine needle aspirate images.
Layout-Bridging Text-to-Image Synthesis

The crux of text-to-image synthesis stems from the difficulty of preserving the cross-modality semantic consistency between the input text and the synthesized image. Typical methods, which seek to model the text-to-image mapping directly, could only capture keywords in the text that indicates common objects or actions but fail to learn their spatial distribution patterns. An effective way to circumvent this limitation is to generate an image layout as guidance, which is attempted by a few methods. Nevertheless, these methods fail to generate practically effective layouts due to the diversity of input text and object location. In this paper we push for effective modeling in both text-to-layout generation and layout-to-image synthesis. Specifically, we formulate the text-to-layout generation as a sequence-to-sequence modeling task, and build our model upon Transformer to learn the spatial relationships between objects by modeling the sequential dependencies between them. In the stage of layout-to-image synthesis, we focus on learning the textual-visual semantic alignment per object in the layout to precisely incorporate the input text into the layout-to-image synthesizing process. To evaluate the quality of generated layout, we design a new metric specifically, dubbed Layout Quality Score, which considers both the absolute distribution errors of bounding boxes in the layout and the mutual spatial relationships between them. Extensive experiments on three datasets demonstrate the superior performance of our method over state-of-the-art methods on both predicting the layout and synthesizing the image from the given text.
Brain-Inspired Spike Echo State Network Dynamics for Aero-Engine Intelligent Fault Prediction

Aero-engine fault prediction aims to accurately predict the development trend of the future state of aero-engines, so as to diagnose faults in advance. Traditional aero-engine parameter prediction methods mainly use the nonlinear mapping relationship of time series data but generally ignore the adequate spatio-temporal features contained in aero-engine data. To this end, we propose a brain-inspired spike echo state network (Spike-ESN) model for aero-engine intelligent fault prediction, which is used to effectively capture the evolution process of aero-engine time series data in the framework of spatio-temporal dynamics. In the proposed approach, we design a spike input layer based on Poisson distribution inspired by the spike neural encoding mechanism of biological neurons, which can extract the useful temporal characteristics in aero-engine sequence data. Then, the temporal characteristics are input into a spike reservoir through the current calculation method of spike accumulation in neurons, which projects the data into a high-dimensional sparse space. In addition, we use the ridge regression method to read out the internal state of the spike reservoir. Finally, the experimental results of aero-engine states prediction demonstrate the superiority and potential of the proposed method.
MINING: Multi-Granularity Network Alignment Based on Contrastive Learning

Network alignment aims to discover nodes in different networks belonging to the same identity. In recent years, the network alignment problem has aroused significant attentions in both industry and academia. However, the continuous exploding of network data brings two challenges in solving the network alignment problem, i.e., large network scale and scarce labeled data. To bridge this gap, in this paper we propose a novel approach termed as <u>M</u> ulti-granular <u>I</u> ty <u>N</u> etwork al <u>I</u> gnment based on co <u>N</u> trastive learnin <u>G</u> (MINING). Specifically, in MINING, we first design multi-granularity alignment framework to solve the issue of large network scale. Then, we design intra- and inter-network contrastive learning to solve the issue of scarce labeled data. Moreover, we provide theoretical proofs to demonstrate the effectiveness of MINING. Finally, we conduct extensive experiments on the benchmark datasets of Facebook-Twitter, AMiner-LinkedIn and DBpedia <inline-formula><tex-math notation="LaTeX">$_{\text{ZH}}$</tex-math></inline-formula> -DBpedia <inline-formula><tex-math notation="LaTeX">$_{\text{EN}}$</tex-math></inline-formula> , and results show that MINING can averagely achieve 15.93% higher <inline-formula><tex-math notation="LaTeX">$\operatorname{Hits@}k$</tex-math></inline-formula> and 14.82% higher <inline-formula><tex-math notation="LaTeX">$\operatorname{MRR@}k$</tex-math></inline-formula> compared with the state-of-the-art methods.
RISTrack: Learning Response Interference Suppression Correlation Filters for UAV Tracking

With the high computation efficiency and tracking accuracy, discriminative correlation filters (DCF) have been applied to UAV tracking. However, in the scenarios ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., complex background and temporary occlusion), DCF-based trackers usually generate low credibility response under the influence of background distractors, which contains multiple side peaks and declines the tracking performance. Motivated by the response consistency in adjacent frames and background information penalization, we propose learning a response interference suppression (RIS) correlation filter to tackle this problem. Specifically, we introduce a RIS regularization into the DCF-based framework, which aims to keep the target area response consistent in adjacent frames and repress distractors’ response in the background. Besides, we adopt a response auxiliary strategy (RAS) to smooth the target response, which intends to obtain the precise location and avoid target drift. Furthermore, extensive experiments on three UAV benchmarks demonstrate the excellent performance of the proposed method against other 19 state-of-the-art trackers. Moreover, the tracking speed of the proposed method can reach 42 FPS on a single CPU.
Saliency as Pseudo-Pixel Supervision for Weakly and Semi-Supervised Semantic Segmentation

Existing studies on semantic segmentation using image-level weak supervision have several limitations, including sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects. To overcome these challenges, we propose a novel framework, an improved version of Explicit Pseudo-pixel Supervision (EPS++), which learns from pixel-level feedback by combining two types of weak supervision. Specifically, the image-level label provides the object identity via the localization map, and the saliency map from an off-the-shelf saliency detection model offers rich object boundaries. We devise a joint training strategy to fully utilize the complementary relationship between disparate information. Notably, we suggest an Inconsistent Region Drop (IRD) strategy, which effectively handles errors in saliency maps using fewer hyper-parameters than EPS. Our method can obtain accurate object boundaries and discard co-occurring pixels, significantly improving the quality of pseudo-masks. Experimental results show that EPS++ effectively resolves the key challenges of semantic segmentation using weak supervision, resulting in new state-of-the-art performances on three benchmark datasets in a weakly supervised semantic segmentation setting. Furthermore, we show that the proposed method can be extended to solve the semi-supervised semantic segmentation problem using image-level weak supervision. Surprisingly, the proposed model also achieves new state-of-the-art performances on two popular benchmark datasets.
Seesaw Counting Filter: A Dynamic Filtering Framework for Vulnerable Negative Keys

Bloom filter is an efficient data structure for filtering negative keys (keys not in a given set) with substantially small space. However, in real-world applications, there widely exist vulnerable negative keys, which will bring high costs if not being properly filtered, especially when positive keys are added/deleted dynamically. Such problem gets more severe when keys within one set are dynamically added or deleted. Recently, there are works focusing on handling such (vulnerable) negative keys by incorporating learning techniques. These learning-based filters fail to work as the learning techniques can hardly handle incremental insertions or deletions. To address the problem, we propose <u>S</u> ee <u>S</u> aw <u>C</u> ounting <u>F</u> ilter (SSCF), which is innovated with encapsulating the vulnerable negative keys into a unified counter array named seesaw counter array, and dynamically modulating (or varying) the applied hash functions to guard the encapsulated keys from being misidentified. Moreover, we design ada-SSCF to handle the scenarios where the vulnerable negative keys cannot be obtained in advance. We extensively evaluate our SSCF, which shows that SSCF outperforms the cutting-edge filters by <inline-formula><tex-math notation="LaTeX">$3\times$</tex-math></inline-formula> on averages regarding accuracy while ensuring a low operation latency. All source codes are in (SSCF-authors).
Evolving Deep Delay Echo State Network for Effluent NH<sub>4</sub>-N Prediction in Wastewater Treatment Plants

In wastewater treatment plants (WWTPs), the prediction of effluent ammonia nitrogen (NH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -N) concentration is vital, which is a major cause of lake eutrophication. To solve this problem, the evolving deep delay echo state network (EDDESN) is proposed. Firstly, the EDDESN is decomposed into several serially connected sub-reservoirs, which are inserted delay units to learn the temporal relationships within sequence data. Secondly, the input and reservoir internal weights are generated by singular value decomposition-based matrix design strategy, which can reduce searching dimensions and guarantee the echo state property (ESP). Moreover, the architecture hyperparameters and weights of EDDESN are simultaneously optimized by competitive swarm optimizer (CSO) based two-stages optimization approach. Finally, the experimental results on practical NH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -N dataset and simulated Mackey-Glass time series demonstrate the superiority of EDDESN as compared with other time series prediction approaches.
Enhancing 5G QoS Management for XR Traffic Through XR Loopback Mechanism

5G networks are designed to support a variety of services with highly demanding Quality-of-Service (QoS) requirements. This opened the door for novel extended reality (XR) media applications to emerge with 5G. However, recent 5G field tests and system-level simulation studies show that further XR enhancements are required to support a massive adoption of XR services in 5G networks. Such enhancements are expected to come into play with 5G-Advanced. In this line, we propose and study an XR loopback mechanism that adapts the XR traffic to the instantaneous 5G network conditions by exploiting an XR application feedback. We propose various XR loopback algorithms, strategies, and parameters’ configurations and study their impact on the 5G end-to-end performance. We conduct extensive simulation campaigns by building realistic end-to-end 5G network scenarios with 3GPP mixed XR traffic setups. Results show that the proposed XR loopback mechanism can boost XR performance in 5G networks by adapting to 5G network conditions, while keeping the XR QoS requirements under control. We provide various insights and practical directions on XR loopback design that allow us to take full advantage of the 5G network capabilities and progress toward 5G-Advanced network design.