Reviewerly/exHarmony huggingface dataset:
Social Distance Analyzer along with Face Mask Detection using AI and ML

The COVID-19 virus spreads through the midst groups of people who are in close contact for an extended period.The chances of spreading a virus are higher when a person who is infected with the virus sneezes, coughs, or talks near others.It is very important for us to stay a minimum of 6 feet away from other people even if you or they do not have any symptoms.Social distancing is the best technique to be followed to reduce the spread of the virus.People are informed to avoid contact with other people, thereby supervising the spread of the virus.Artificial Intelligence and Deep Learning have shown good outcomes for some daily life problems.Computer vision and deep learning techniques are used to see social distancing between people in public places.It uses the YOLOv3 object recognition paradigm to categorize.The detection algorithm uses a pre-trained algorithm that is associated with an extra trained layer using an overhead human data set.Euclidean distance is used in the detection of bounding box centroid's pairwise distances of people are determined.Accuracy up to 98% is achieved by the detection model.Coronavirus outbreaks can be solved by social distancing as well as putting on a face mask.Wearing a mask as well as the ensuing social distancing would save large numbers of lives.So, Face Mask Detection would be used efficiently for the purpose.
An Amalgamation of Ontology Module in MultiTenant Cloud Architecture

Cloud‖ is an amalgamation of servers, software and applications that can be accessed by individual or business organizations with an aim of accessing services at pay per usage policy.Customers or tenants get access to some portion of cloud to run their tasks and performs computation.As there are many challenges on cloud while accessing, storing and securing data like trade off in applications, higher costs incurred for low supply of resources and many more.It leads to the need of Multitenancy.Multitenancy is an approach of achieving flexibility, higher degree of scalability, clustering of services and reduced data accessing costs.It requires accessing of database by multiple clients at one go and it is obvious that it may lead to security risks in cloud environment.The paper makes readers aware of concept of Multitenancy and its actual need in today's computing era.It is followed by identification of security threats associated with multitenant environment in form of literature review.Lastly, a secured multitenant cloud environment using concept of ontology is being proposed in the following paper.
Deployment of an Enhanced Web-based Really Simple Syndication (RSS) Aggregator (Ultrafeed)

There is too much information for consumption in the world, hence a struggle to keep ourselves up todate with new publications in our various fields of discipline and research interest.Information overload can be very frustrating, this can be prevented by utilizing tools/technologies that provide effective information management, of which Really Simple Syndication (RSS) software is an example.The system is developed with the use of Bootsratps (JavaScript (JS), Hypertext Mark-up Language (HTML), Cascading Style Sheet (CSS) for the system interface, php (a script language for coding the functionalities), and wamp server (apache, mysql, Hypertext Pre-processor (PHP)) as a development environment for apache, mysql and php databases.The proposed system allows users to subscribe to websites from which they can be frequently updated with information including audio and video, blog entries, news headlines, etc.The proposed system is designed to eliminate the user's requirement to manually monitor the website for new contents.Instead, the system continuously monitors the site and notifies users of any changes.The administrator portal is incorporated for the purposes of managing the content available in the system.The system manages all the contents that the user subscribes to and the user receives updates as and when they sign-in.This saves time and helps with quick access to published or available information.
Enhancement of GraphQL Data Fetching Technique

Data fetching techniques are used to communicate between servers and clients on the World Wide Web.It serves as the basis of modern data-driven websites, since these websites have more frequent communication between client and server.In addition, it will have a huge data transfer between them.The predominant data fetching techniques are REST API, GraphQL and Protobuf.REST API is the most popular data fetching technique in the market, but it does not provide a way to get only the fields required by the client.GraphQL addresses this problem by providing dynamic querying.However, both the REST API and GraphQL return the response in JSON format, which has keys and values.These keys in the response can occupy a huge chunk of the response as the size of the response increases.This is a drawback for data-driven applications that run on bandwidth-constrained devices.Protobuf tries to address this problem by returning the response as an array of values, but Protobuf does not allow the client to query only the required fields.Hence, the objective of this paper is to propose a new data fetching technique that combines the advantages of GraphQL and Protobuf to reduce the payload size and the response time.
Real Time Indian Sign Language Detection using LSTM and Keypoint Extraction

Hearing and speech impaired people find it challenging to converse with ordinary.In India, about 63 million (6.3%) of the population suffers from auditory loss.They communicate through sign languages, which are based on visually conveyed sign patterns, which typically include hand movements.Sign language is not universal and not easy to learn.Many Artificial Intelligence (AI) based systems are present for the Sign Language conversion as a typical solution to this problem.But, most of the existing solutions, use American Standard Sign Language (ASL) .As per the research done for this project, Each country has its own sign language variant and in India, Indian Sign Language (ISL) is being followed.The major goal of the proposed system is to create an ISL Hand Gesture Motion Translation Tool that will assist the hearing and speech challenged community in transforming their voice and ideas into text so that they may communicate with others without huddles.The methodology is to identify isolated words and to design a dynamic hand gesture recognition system for ISL.The proposed system is built using Mediapipe which is used for key-point extraction combined with a Long Short-Term Memory (LSTM), a Recurrent Neural Network (RNN) architecture system design with Dense layers.It is capable to identify ISL Alphabets and words in real time environment.
PENYULUHAN LITERASI KEUANGAN SEJAK DINI DI TK GEMA NURANI PEJUANG BEKASI

Education is an important investment for a child's future. Not only moral and academic education is needed, but also education on financial management. Lack of parental awareness about the importance of financial education from an early age, causes a child's lack of money management knowledge. The purpose of this Community Service (PKM) is to increase children's awareness from an early age so that they can get used to saving so they are able to manage finances, distinguish between wants and needs. Financial literacy counseling is carried out using an online method using Zoom Metting to parents of students (PAUD Group) TKIT Gema Nurani. Counseling was carried out in two meetings. The first is with Gema Conscience TKIT teachers and the second is with the parents of TKIT Gema Conscience students. Approach and Cooperation with Related Parties. This financial literacy education was successful. Furthermore, the application of financial literacy from an early age requires joint participation between teachers, parents and the PKM team so that they can increase knowledge of financial literacy from an early age and can provide benefits for kindergarten students.
Local available quantum correlations of non-symmetric X-states

Local available quantum correlations (LAQC), as defined by Mundarain et al., are analyzed for nonsymmetric 2-qubit X states, that is, X states that are not invariant under the exchange of subsystems and therefore have local Bloch vectors whose norms are different. A simple analytic expression for their LAQC quantifier is obtained. As an example, we analyze the local application of the amplitude damping channel for Werner states and general X states. Although this local quantumchannel can create quantum discord in some cases, no such outcome is possible for LAQC, which hints toward their monotonicity under LOCC operations. This work, along with our previous result for the so-called symmetric and anti-symmetric X states, completes the pursuit of exact analytical expressions for the LAQC quantifier for 2-qubit X states.
Evolving Deep Neural Network by Customized Moth-Flame Optimization Algorithm for Underwater Targets Recognition

This chapter proposes using the Moth-Flame Optimization (MFO) algorithm for fine-tuning a Deep Neural Network to recognize different underwater sonar datasets. Same as other models evolved by Meta-heuristics algorithms, premature convergence, trapping in local minima, and failure to converge in a reasonable time are three defects MFO confronts in solving problems with high-dimension search space. Spiral flying is the key component of the MFO as it determines how the moths adjust their positions in relation to flames; thereby, the shape of spiral motions can regulate the transition behavior between the exploration and exploitation phases. Therefore, this chapter investigates the efficiency of seven spiral motions with different curvatures and slopes in the performance of MFO, especially for underwater target classification tasks. To assess the performance of the customized model, in addition to benchmark Sejnowski & Gorman’s dataset, two experimental sonar datasets, i.e., the passive sonar and active datasets, are exploited. The results of MFO and its modifications are compared to four novel nature-inspired algorithms, including Heap-Based Optimizer (HBO), Chimp Optimization Algorithm (ChOA), Ant Lion Optimization (ALO), Stochastic Fractals Search (SFS), as well as the classic Particle Swarm Optimization (PSO). The results confirm that the customized MFO shows better performance than the other state-of-the-art models so that the classification rates are increased by 1.5979, 0.9985, and 2.0879 for Sejnowski & Gorman, passive, and active datasets, respectively. The results also approve that time complexity is not significantly increased by using different spiral motions.
Hiding Secrets in Electrocardiogram Based on Integer Wavelet Transform with Coefficient Adjustment and LSB Substitution

In this paper, we present an efficient data hiding for electrocardiogram (ECG) hosts to solve the issues of existing ECG steganographic methods, which have less hiding capacity and insufficient signal-to-noise ratio (SNR)/ peak SNR (PSNR). Based on the integer wavelet transform (IWT), private (or sensitive) data such as patients&amp;rsquo; diagnosis and personal information can be embedded in an ECG host via the IWT coefficient adjustment and the least significant bit (LSB) technique. Simulations indicated that the SNR (or PSNR), and payload of the proposed method outperform those of existing techniques. In addition, the proposed method is capable of resisting attacks, such as cropping, Gaussian noise-addition inversion, scaling, translation, and truncation attacks from third parties (or adversaries). Since the proposed method has low computational complexity, our method can be employed in portable biometric devices or wearable electronics.
Domain-Specific Image Captioning: A Comprehensive Review

&lt;p&gt;Image captioning is a sentence summarizing the semantic content of an image. Describing the essential information of an image in the form of a statement typically deals with computer vision and natural language processing. The prior research frequently addressed this subject simply using standard machine learning approaches that model such systems by extracting the hand-engineered characteristics from the input data. With the resurgence of deep-learning approaches, the image captioning area has experienced state-of-the-art outcomes in numerous application domains, including visual aid, traffic assistance, medical support, and remote sensing. This research thoroughly reviews the application domain-based image captioning frameworks by addressing both classical and modern deep learning-based architectures. We further propose a taxonomy to characterize the same by studying their distinct technological foundations and performances across diverse datasets. We also describe evaluation metrics used to examine the captioning image models across several domains. Finally, we highlight open research problems concerning such models from a futuristic standpoint.&lt;/p&gt;
A Novel Approach for Classification in Resource-Constrained Environments

Internet of Things’ (IoT) deployments are increasingly dependent upon learning algorithms to analyse collected data, draw conclusions, and take decisions. The norm is to deploy such learning algorithms on the cloud and have IoT nodes interact with the cloud. While this is effective, it is rather wasteful in terms of energy expended and temporal latency. In this article, the endeavour is to develop a technique that facilitates classification, an important learning algorithm, within the extremely resource constrained environments of IoT nodes. The approach comprises selecting a small number of representative data points, called prototypes, from a large dataset and deploying these prototypes over IoT nodes. The prototypes are selected in a manner that they appropriately represent the complete dataset and are able to correctly classify new, incoming data. The novelty lies in the manner of prototype selection for a cluster that not only considers the location of datapoints of its own cluster but also that of datapoints in neighboring clusters. The efficacy of the approach is validated using standard datasets and compared with state-of-the-art classification techniques used in constrained environments. A real world deployment of the technique is done over an Arduino Uno-based IoT node and shown to be effective.
Discriminating seismic events using 1D and 2D CNNs: applications to volcanic and tectonic datasets

Abstract Detecting seismic events, discriminating between different event types, and picking P- and S-wave arrival times are fundamental but laborious tasks in seismology. In response to the ever-increasing volume of seismic observational data, machine learning (ML) methods have been applied to try to resolve these issues. Although it is straightforward to input standard (time-domain) seismic waveforms into ML models, many studies have used time–frequency-domain representations because the frequency components may be effective for discriminating events. However, detailed comparisons of the performances of these two methods are lacking. In this study, we compared the performances of 1D and 2D convolutional neural networks (CNNs) in discriminating events in datasets from two different tectonic settings: tectonic tremor and ordinary earthquakes observed at the Nankai trough, and eruption signals and other volcanic earthquakes at Sakurajima volcano. We found that the 1D and 2D CNNs performed similarly in these applications. Half of the misclassified events were misassigned the same labels in both CNNs, implying that the CNNs learned similar features inherent to the input signals and thus misclassified them similarly. Because the first convolutional layer of a 1D CNN applies a set of finite impulse response (FIR) filters to the input seismograms, these filters are thought to extract signals effective for discriminating events in the first step. Therefore, because our application was the discrimination of signals dominated by low- and high-frequency components, we tested which frequency components were effective for signal discriminations based on the filter responses alone. We found that the FIR filters comprised high-pass and low-pass filters with cutoff frequencies around 7–9 Hz, frequencies at which the magnitude relations of the input signal classes change. This difference in the power of high- and low-frequency components proved essential for correct signal classifications in our dataset.
Quantum vs classical genetic algorithms: A numerical comparison shows faster convergence

Genetic algorithms are heuristic optimization techniques inspired by Darwinian evolution. Quantum computation is a new computational paradigm which exploits quantum resources to speed up information processing tasks. Therefore, it is sensible to explore the potential enhancement in the performance of genetic algorithms by introducing quantum degrees of freedom. Along this line, a modular quantum genetic algorithm has recently been proposed, with individuals encoded in independent registers comprising exchangeable quantum subroutines [arXiv:2203.15039], which leads to different variants. Here, we address the numerical benchmarking of these algorithms against classical genetic algorithms, a comparison missing from previous literature. To overcome the severe limitations of simulating quantum algorithms, our approach focuses on measuring the effect of quantum resources on the performance. In order to isolate the effect of the quantum resources in the performance, the classical variants have been selected to resemble the fundamental characteristics of the quantum genetic algorithms. Under these conditions, we encode an optimization problem in a two-qubit Hamiltonian and face the problem of finding its ground state. A numerical analysis based on a sample of 200 random cases shows that some quantum variants outperform all classical ones in convergence speed towards a near-to-optimal result. Additionally, we have considered a diagonal Hamiltonian and the Hamiltonian of the hydrogen molecule to complete the analysis with two relevant use-cases. If this advantage holds for larger systems, quantum genetic algorithms would provide a new tool to address optimization problems with quantum computers.
Scalable Spatio-Temporal Smoothing via Hierarchical Sparse Cholesky Decomposition

We propose an approximation to the forward-filter-backward-sampler (FFBS) algorithm for large-scale spatio-temporal smoothing. FFBS is commonly used in Bayesian statistics when working with linear Gaussian state-space models, but it requires inverting covariance matrices which have the size of the latent state vector. The computational burden associated with this operation effectively prohibits its applications in high-dimensional settings. We propose a scalable spatio-temporal FFBS approach based on the hierarchical Vecchia approximation of Gaussian processes, which has been previously successfully used in spatial statistics. On simulated and real data, our approach outperformed a low-rank FFBS approximation.
A Multi-Stage Framework for the 2022 Multi-Structure Segmentation for Renal Cancer Treatment

Three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images is of great clinical significance. Automatic segmentation of kidney, renal tumor, renal vein and renal artery benefits a lot on surgery-based renal cancer treatment. In this paper, we propose a new nnhra-unet network, and use a multi-stage framework which is based on it to segment the multi-structure of kidney and participate in the KiPA2022 challenge.
Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution

Self-supervised cross-modal super-resolution (SR) can overcome the difficulty of acquiring paired training data, but is challenging because only low-resolution (LR) source and high-resolution (HR) guide images from different modalities are available. Existing methods utilize pseudo or weak supervision in LR space and thus deliver results that are blurry or not faithful to the source modality. To address this issue, we present a mutual modulation SR (MMSR) model, which tackles the task by a mutual modulation strategy, including a source-to-guide modulation and a guide-to-source modulation. In these modulations, we develop cross-domain adaptive filters to fully exploit cross-modal spatial dependency and help induce the source to emulate the resolution of the guide and induce the guide to mimic the modality characteristics of the source. Moreover, we adopt a cycle consistency constraint to train MMSR in a fully self-supervised manner. Experiments on various tasks demonstrate the state-of-the-art performance of our MMSR.
Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness

Accurate 3D object detection with LiDAR is critical for autonomous driving. Existing research is all based on the flat-world assumption. However, the actual road can be complex with steep sections, which breaks the premise. Current methods suffer from performance degradation in this case due to difficulty correctly detecting objects on sloped terrain. In this work, we propose Det6D, the first full-degree-of-freedom 3D object detector without spatial and postural limitations, to improve terrain robustness. We choose the point-based framework by founding their capability of detecting objects in the entire spatial range. To predict full-degree poses, including pitch and roll, we design a ground-aware orientation branch that leverages the local ground constraints. Given the difficulty of long-tail non-flat scene data collection and 6D pose annotation, we present Slope-Aug, a data augmentation method for synthesizing non-flat terrain from existing datasets recorded in flat scenes. Experiments on various datasets demonstrate the effectiveness and robustness of our method in different terrains. We further conducted an extended experiment to explore how the network predicts the two extra poses. The proposed modules are plug-and-play for existing point-based frameworks. The code is available at https://github.com/HITSZ-NRSL/De6D.
A Survey on EOSIO Systems Security: Vulnerability, Attack, and Mitigation

EOSIO, as one of the most representative blockchain 3.0 platforms, involves lots of new features, e.g., delegated proof of stake consensus algorithm and updatable smart contracts, enabling a much higher transaction per second and the prosperous decentralized applications (DApps) ecosystem. According to the statistics, it has reached nearly 18 billion USD, taking the third place of the whole cryptocurrency market, following Bitcoin and Ethereum. Loopholes, however, are hiding in the shadows. EOSBet, a famous gambling DApp, was attacked twice within a month and lost more than 1 million USD. No existing work has surveyed the EOSIO from a security researcher perspective. To fill this gap, in this paper, we collected all occurred attack events against EOSIO, and systematically studied their root causes, i.e., vulnerabilities lurked in all relying components for EOSIO, as well as the corresponding attacks and mitigations. We also summarized some best practices for DApp developers, EOSIO official team, and security researchers for future directions.