Reviewerly/exHarmony huggingface dataset:
CNN based Identifying Human Activity using Smartphone Sensors

Human Activity Recognition has gained greater emphasize in the last few years due to its widespread applicability and psychological curiosity. This system can be adopted in innumerable applications, like healthcare monitoring systems, surveillance systems, and so on. Smart-phones have built-in multifunctional sensors like as accelerometers and gyroscopes that provide useful sensory data when participants perform daily activities thus helping in HAR activity. Highly efficient features are extracted from this sensor data and techniques like denoising, normalization and segmentation are used to reduce noise and extract valuable feature vectors. Prior research showed that deep learning methods like recurrent neural networks and one-dimensional convolution networks provide excellent results in activity recognition tasks. In this paper, an ensemble model of CNN and SVM is proposed to further improve the accuracy and provide a robust model. Experimental methods are tested on UCI-HAR dataset and compared with other state-of-the-art methods like LSTM, CNN-LSTM, and Conv LSTM.
Adoptation of Blockchain of Things(BCOT): Oppurtunities &amp; Challenges

IoT has been an efficient technology for interconnecting different physical objects with the internet. Several cyber-attacks have resulted in compromise in security. Blockchain distributed ledger provide immutability that can answer IoT security concerns. The paper aims at highlighting the challenges & problems currently associated with IoT implementation in real world and how these problems can be minimized by implementing Blockchain based solutions and smart contracts. Blockchain helps in creation of new highly robust IoT known as Blockchain of Things(BCoT). We will also examine presently employed projects working with integrating Blockchain & IoT together for creating desired solutions. We will also try to understand challenges & roadblocks preventing the further implementation of both technologies merger.
Decentralized Fundraising Application Using Blockchain

It is critical for a donor to understand where and how their money is being used. This will not only make them happy, but it will also encourage them to donate again and propose or publicise donations, resulting in a large donation amount for the needy. This paper proposes a decentralized application which utilizes blockchain and smart contracts which is built with the aim of helping the needy and underprivileged people by providing a platform that is secured, flawless, easy to use and transparent with the funds. Fundraising applications have come into existence to help people and communities alike. But these applications have been far from perfect. People have shown general distrust in a third-party to securely handle their funds before reaching the people donations were meant for. Some reports of misuse of collected funds have worsened the situation. The lack of transparency and involvement of third-party has hindered people from donating at times and have left many needy people without any help. To combat these issues, we have decided to build a fundraising website accessible to everyone whose working will be transparent. Registered users, once verified, can donate to various charity drives and funds and also ask for funds when in need. using Machine learning recommendations of similar fundraisers are made who like to donate to some particular charity drives. To acheive transparency, the blockchain technology is used to store records of donations made by the users. Decentralization of fundraising process also eliminates involvement of any third-party group.
Virtual Mouse Using Hand Gesture Recognition - A Systematic Literature Review

In present scenario, touch screens are found on most mobile devices. However, this technology is currently too expensive to be employed on desktop computers. As an alternative to the touch screen, a virtualized human-computer interface gadget like a mouse can be developed using a camera and some computer vision algorithms. A virtual mouse application based on finger tracking has been conceived utilizing a normal webcam in this study. The goal was to construct a virtual human-computer interface device and an object tracking program to interact with the computer. To achieve this goal, we will be using the openCV2 library along with the Mediapipe Palm Detection Model and Hand Landmark Model.
A Systematic Review on Artificial Intelligence-based Opinion Mining Models

Opinion mining acts as a significant tool in assisting potential customers to make better purchase decisions. In the area of business intelligence, it shapes product innovation and reduces the risks associated with moving to new markets. Thus, the opinion mining from the customer reviews becomes important task for every organization. Therefore, this article focused on detailed survey on artificial intelligence (AI)-based opinion mining models. Further, the conventional methods include deep learning models, feature extraction mechanisms, and machine learning models. However, the state of art approaches is failed to provide the maximum accuracy due to changes in the datasets. The main objective of this article is to design a contrastive opinion summarization objective that would take advantage of context information present in the sentences to create a concise contrastive summary. The resulting summary will present the most important differences in opinion between two products based on their features.
Confidential Inference in Decision Trees: FPGA Design and Implementation

In confidential computing, algorithms operate on encrypted inputs to produce encrypted outputs. Specifically, in confidential inference, Alice has the parameters of the machine-learning model but does not want to reveal them to Bob who has the data. Bob wants to use Alice’s model for inference but does not want to reveal his data. Alice and Bob agree to use homomorphic encryption for running the inference engine in full confidence without revealing either model or data. They find that full homomorphic encryption is very time consuming and very challenging to accelerate on hardware. In this particular case, homomorphic encryption can be made computationally efficient and can even be readily accelerated on hardware. In this paper, we reveal how Alice and Bob run the inference engine in full confidence and show an FPGA implementation of the specialized homomorphic computing algorithm they used. We further evaluate the resources needed to implement the encrypted decision tree and compare them with those of a plain decision tree. Confidential inference tests are run on the encrypted FPGA design using the MNIST dataset.
Attention-Based Relational Graph Convolutional Network for Knowledge Graph Reasoning

In recent years, with the rapid growth of knowledge graphs, knowledge reasoning technology is in great demand for research. The knowledge graph is a heterogeneous network with a graph structure. Graph Convolutional Network (GCN) is an extension of traditional Convolutional Neural Network (CNN) in non-Euclidean space, very suitable for processing complex graph data. In this paper, a attention-based relational graph convolutional network (AR-GCN) is proposed. When aggregating neighbor information, the weight of neighbor nodes is adaptively assigned through the attention mechanism, so that nodes can focus on different neighbor information and enhance the accuracy of feature representation. According to the topological characteristics of different knowledge graphs, two attention mechanisms are proposed. The experimental results show that AR-GCN outperforms R-GCN in entity classification and link prediction tasks, further showing that it has stronger characterization ability.
A Reliable, Secure and Efficient Decentralised Conditional of KYC Verification System: A Blockchain Approach

KYC or Know Your Customer is the procedure to verify the individuality of its consumers & evaluating the possible dangers of illegitimate trade relations. A few problems with the existing KYC manual process are that it is less secure, time-consuming and expensive. With the advent of Blockchain technology, its structures such as consistency, security, and geographical diversity make them an ideal solution to such problems. Although marketing solutions such as KYC-chain.co, K-Y-C. The legal right to enable blockchain-based KYC authentication provides a way for documents to be verified by a trusted network participant. This project uses an ETHereum based Optimised KYC Block-chain system with uniform A-E-S encryption and compression built on the LZ method. The system publicly verifies a distributed encryption, is protected by cryptography, operates by pressing the algorithm and is all well-designed blockchain features. The suggested scheme is a novel explanation based on Distributed Ledger Technology or Blockchain technology that would cut KYC authentication process expenses of organisations & decrease the regular schedule for completion of the procedure whilst becoming easier for clients. The largest difference in the system in traditional methods is the full authentication procedure is performed in just no time for every client, regardless of the number of institutions you desire to be linked to. Furthermore, since DLT is employed, validation findings may be securely distributed to consumers, enhancing transparency. Based on this method, a Proof of Concept (POC) is produced with Ethereum's API, websites as endpoints and the android app as the front office, recognising the viability and efficacy of this technique. Ultimately, this strategy enhances consumer satisfaction, lowers budget overrun & promotes transparency in the customer transport network.
Continued Use of Contact-Tracing Apps in the United States and the United Kingdom: Insights From a Comparative Study Through the Lens of the Health Belief Model

To contain the spread of SARS-CoV-2, contact-tracing (CT) mobile apps were developed and deployed to identify and notify individuals who have exposure to the virus. However, the effectiveness of these apps depends not only on their adoption by the general population but also on their continued use in the long term. Limited research has investigated the facilitators of and barriers to the continued use of CT apps.In this study, we aimed to examine factors influencing the continued use intentions of CT apps based on the health belief model. In addition, we investigated the differences between users and nonusers and between the US and UK populations.We administered a survey in the United States and the United Kingdom. Respondents included individuals who had previously used CT technologies and those without experience. We used the structural equation modeling technique to validate the proposed research model and hypotheses.Analysis of data collected from 362 individuals showed that perceived benefits, self-efficacy, perceived severity, perceived susceptibility, and cues to action positively predicted the continued use intentions of CT apps, while perceived barriers could reduce them. We observed few differences between the US and UK groups; the only exception was the effect of COVID-19 threat susceptibility, which was significant for the UK group but not for the US group. Finally, we found that the only significant difference between users and nonusers was related to perceived barriers, which may not influence nonusers' continued use intentions but significantly reduce experienced users' intentions.Our findings have implications for technological design and policy. These insights can potentially help governments, technology companies, and media outlets to create strategies and policies to promote app adoption for new users and sustain continued use for existing users in the long run.
A Deep Learning Method with Cross Dropout Focal Loss Function for Imbalanced Semantic Segmentation

Deep learning methods have proven their potential in semantic segmentation. However, they depend on the data quality and training process. Often, the data corresponding to the objects to be segmented are of different sizes and this creates difficulties for the segmentation method. Objects are segmented and associated with categories during the training process. Data imbalance is a challenging problem, which often results in unsatisfactory segmentation performance. This paper proposes a solution to this task based on a novel cross dropout focal loss (CDFL) function, which represents well the change between the cross-entropy and other state-of-the-art loss functions providing a balance between the precision and accuracy of segmentation. The performance of the considered fully convolutional network (FCN) with different loss functions is considered and carefully evaluated. The proposed loss function improves efficiently the semantic segmentation performance over other well-known loss functions. It is demonstrated on Cityscapes and PASCAL VOC 2010 publicly available datasets. The implementation is over relatively large data sets. The achieved mean accuracy of the proposed CDFL network on Cityscapes dataset is 76.41% and on PASCAL VOC 2010 dataset is 79.63% which is with approximately 2.5% improvement compared with the same network implemented with the cross-entropy loss function.
Analyzing Spatial Variations of Cloud Attenuation by a Network of All-Sky Imagers

All-sky imagers (ASIs) can be used to model clouds and detect spatial variations of cloud attenuation. Such cloud modeling can support ASI-based nowcasting, upscaling of photovoltaic production and numeric weather predictions. A novel procedure is developed which uses a network of ASIs to model clouds and determine cloud attenuation more accurately over every location in the observed area, at a resolution of 50 m × 50 m. The approach combines images from neighboring ASIs which monitor the cloud scene from different perspectives. Areas covered by optically thick/intermediate/thin clouds are detected in the images of twelve ASIs and are transformed into maps of attenuation index. In areas monitored by multiple ASIs, an accuracy-weighted average combines the maps of attenuation index. An ASI observation’s local weight is calculated from its expected accuracy. Based on radiometer measurements, a probabilistic procedure derives a map of cloud attenuation from the combined map of attenuation index. Using two additional radiometers located 3.8 km west and south of the first radiometer, the ASI network’s estimations of direct normal (DNI) and global horizontal irradiance (GHI) are validated and benchmarked against estimations from an ASI pair and homogeneous persistence which uses a radiometer alone. The validation works without forecasted data, this way excluding sources of error which would be present in forecasting. The ASI network reduces errors notably (RMSD for DNI 136 W/m2, GHI 98 W/m2) compared to the ASI pair (RMSD for DNI 173 W/m2, GHI 119 W/m2 and radiometer alone (RMSD for DNI 213 W/m2), GHI 140 W/m2). A notable reduction is found in all studied conditions, classified by irradiance variability. Thus, the ASI network detects spatial variations of cloud attenuation considerably more accurately than the state-of-the-art approaches in all atmospheric conditions.
Detection of SQL injection attack based on improved TFIDF algorithm

With the arrival of the Internet age, SQL injection is widely distributed as a web application attack method. It's with low operating threshold and not easy to be discovered, so it seriously threatens network security, causing a large number of websites to have security risks. When the number of sensitive words and non-sensitive words in the SQL sentence data set is close, the SQL injection attack detection method based on the TFIDF algorithm has the problem of high false alarm rate. This paper proposes a detection method based on the improved TFIDF algorithm. It analyzes and compares a large number of attack statements and normal SQL statements, summarizes the characteristics of SQL statements and vectorizes the text, and adds quantification related to keywords in the formula. parameter. The purpose of this article is to use the improved TFIDF algorithm in the data preprocessing stage, use three different classifiers in the classification stage, combine SVM, KNN and DT three different machine learning algorithms, and finally compare experiments. The results prove that the improved TFIDF algorithm combined with SVM has a higher accuracy rate and a lower false alarm rate.
Communication advantage of quantum compositions of channels from non-Markovianity

The combined use of quantum channels can grant communication advantages in the form of enhancements to communication capacities. One such channel composition is the quantum switch, which implements a system with indefinite causal order by coherent control of the orderings of two or more quantum channels, resulting in enhanced communication capacities. Here, using the monogamy relation, we studied the flow of entanglement monotone in these quantum compositions of channels in the environmental representation. We implemented the two-party quantum switch in this framework, and demonstrated that non-Markovianity is the source of revival of the entanglement monotone in this setup. The possibility and amount of revival was shown to depend on the entangling capability of the channels, and the perfect activation of coherent information with entanglement-breaking channels was also replicated with the perfect revival of entanglement monotone. Additionally, we showed that a more general non-Markovian circuit can still grant enhancements to coherent information and Holevo quantity without the presence of indefinite causal order.
Detecting and Understanding Sentiment Trends and Emotion Patterns of Twitter Users—A Study on the Demise of a Bollywood Celebrity

Detecting societal sentiment trends and emotion patterns is of great interest. Due to the time-varying nature of these patterns and trends this detection can be a challenging task. In this paper, the emotion patterns and trends are detected among social media users in a certain case and it is noted that the detection of the trends and patterns is especially difficult in this medium because of the use of informal language. In particular, the role of social networks in the expression of emotions relating to the death of a well-known and loved Bollywood actor Sushant Singh Rajput (SSR) by their fans is explored. The data for the analysis of the emotional state and the sentiment levels of the fans has been acquired from Twitter posts. Different existing sentiment analysis algorithms were compared for the study and chosen for identifying the sentiment trend over a specific timeline of events. The same Twitter posts were also analyzed for emotional content by extracting linguistic features using the psycholinguistic package, Linguistic Inquiry and the Word Count package (LIWC), relating to emotions. Additionally, viral hashtags extracted from the Twitter posts have been segmented and analyzed in order to identify new viral hashtags expressed by the posts over time. The associations between the old and new viral hashtags and between sentiment trends and emotional shifts among the fan base of SSR have been determined and presented graphically.
Discrepancies among pre-trained deep neural networks: a new threat to model zoo reliability

Training deep neural networks (DNNs) takes significant time and resources. A practice for expedited deployment is to use pre-trained deep neural networks (PTNNs), often from model zoos--collections of PTNNs; yet, the reliability of model zoos remains unexamined. In the absence of an industry standard for the implementation and performance of PTNNs, engineers cannot confidently incorporate them into production systems. As a first step, discovering potential discrepancies between PTNNs across model zoos would reveal a threat to model zoo reliability. Prior works indicated existing variances in deep learning systems in terms of accuracy. However, broader measures of reliability for PTNNs from model zoos are unexplored. This work measures notable discrepancies between accuracy, latency, and architecture of 36 PTNNs across four model zoos. Among the top 10 discrepancies, we find differences of 1.23%-2.62% in accuracy and 9%-131% in latency. We also find mismatches in architecture for well-known DNN architectures (e.g., ResNet and AlexNet). Our findings call for future works on empirical validation, automated tools for measurement, and best practices for implementation.
Detection of Recolored Image by Texture Features in Chrominance Components

Image recoloring is an emerging editing technique that can change the color style of an image by modifying pixel values without altering the original image content. With the rapid proliferation of social network and image editing techniques, recolored images (RIs) have raised new security issues in society. Existing detection methods have good performance in detecting RIs for certain categories of recoloring techniques. However, the performance on the handcrafted recoloring scenario is still poor due to the influence of human prior knowledge. To deal with this problem, we explore a solution from the perspective of chrominance texture artifacts to improve the generalization ability. The results of the analysis show that natural images (NIs) and RIs have textural disparities in different color components, especially in the chrominance components (i.e., Cb, Cr, and H). Based on such new prior knowledge of statistical discriminability, we propose a feature set to capture texture features in chrominance components for identifying RIs. Extensive experimental results show that the proposed method can accurately identify RIs with certain categories of recoloring techniques, and outperforms existing methods in the scenario of handcrafted recoloring.
Securing grayscale image using improved Arnold transform and ElGamal encryption

The security of sensitive data is critical, and it opens up a wide area of research to find efficient and effective methods to prevent unauthorized access. Our study provides a secure framework for sending visual information over an untrusted channel, such as a social networking site. The proposed framework is a combination of scrambling and encryption techniques. Initially, a hybrid block-wise and pixel-wise scrambling approach is administered to the grayscale image, followed by the Arnold transform, which causes all pixel points to move within the image. Finally, to improve the efficiency of the diffusion process, the asymmetric encryption ElGamal algorithm has been mastered. Peak-signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), number of pixels change rate (NPCR), and unified average changing intensity (UACI) are the metrics utilized to evaluate the efficiency of the proposed scheme. The efficiency of the suggested scheme validates the low-average PSNR value of <9 dB and the SSIM average value of <0.01 for the encrypted images. The NPCR and UACI values achieved in our study are above the threshold values of 99.6% and 33.33%, respectively, exhibiting the strength of the proposed framework.