Reviewerly/exHarmony huggingface dataset:
A BLOCKCHAIN TECHNOLOGY AND INTERNET OF THINGS TO SECURE IN HEALTHCARE SYSTEM

The huge headway of new advances like the Web of Things and wearable innovation, the medical services industry is extending rapidly these days. To guarantee far off persistent observing, these devices are generally used. Client/server engineering is utilized in the ongoing execution. Due of the security and protection issues raised by this, medical care frameworks are currently more helpless against different assaults. The utilization of a dispersed engineering is expected to resolve these issues and submit to security rules. Block chain has drawn in a ton of interest as a modern innovation to address the security issues in IoT-based frameworks in light of its conveyed nature and its security ensures. With the new presentation of block chain and IoT, the medical care area is supposed to develop altogether and experience an expansion in monetary potential as well as security, protection, effectiveness, and straightforwardness. The client/server engineering is the underpinning of the ongoing execution. In this paper, various protection and security gives that make medical services frameworks more powerless against different assaults.
Comparative Study of RSA Encryption and Quantum Encryption

Encryption is an important factor during online communication. It is useful to protect users privacy and prevent eavesdroppers listening. RSA encryption and quantum encryption are two mainstream encryption methods applied nowadays. This paper focuses on the evaluation and comparison between these two encryptions. It adopts the basic theory of RSA encryption and quantum encryption and provides an analysis of the benefits and shortcomings of these encryptions. It can be concluded that RSA (a type of mathematical encryption) is more popular than quantum encryption (a kind of physical encryption), but is less secure.
Denoising of discrete-time chaotic signals using echo state networks

Noise reduction is a relevant topic when considering the application of chaotic signals in practical problems, such as communication systems or modeling biomedical signals. In this paper an echo state network (ESN) is employed to denoise a discrete-time chaotic signal corrupted by additive white Gaussian noise. The choice for applying ESNs in this context is motivated by their successful exploitation for separation and prediction of chaotic signals. The results show that the processing gain of ESN is higher than that of the Wiener filter, especially when the power spectral density of the chaotic signals is white.
Realization of OpenCL based CNN Implementation on FPGA using SDAccel Platform

In this paper, we are demonstrating the implementation flow of algorithms using deep Convolutional Neural Networks (CNN). Deploying CNNs solely on CPU-GPU based platforms deal with issues of high power consumption and comparatively slower processing speeds than hardware accelerators like FPGA. FPGA offers high parallel-processing capability, hardware re-configurability, reduced latency and low power consumption. To further improve flexibility in programming the hardware accelerator, we decided to use High Level Synthesis (HLS) tool based on OpenCL framework for development of our CNN model and implementing it on cloud. We are using Xilinx SDAccel development environment targeting Amazon’s AWS EC2 F1 instances. We are using Cloud based services for our model visualization on FPGAs. Cloud provides FPGA as a service with High Performance Computing (HPC) resources and enables us to deploy our optimized code on provided FPGA accelerated instance without need of buying an actual hardware. AWS EC2 F1 instance uses Xilinx Ultrascale+VU9P chip as a Hardware platform.
Realization of Smart and Highly Efficient IoTbased Surveillance System using Facial Recognition on FPGA

In this paper, we have developed a security system using FPGA with the integration of IoT using face recognition technology with the help of the OpenCL library. We will be using HLS for the conversion of our C$+ \quad +$code so that it can be deployed on FPGA after converting it into VHDL. The designed system can recognize and allow the authenticated person to enter the building or office even if the person somehow bypass the authentication then also our system can catch the person using real-time face detection using all the cameras integrated with our IoT system and comparing the faces stored in the database and will alert the security personals, even out system will be able to find one particular person in building conditioned if the person is in camera’s view which will make our system even more effective for surveillance and finding someone.
Towards Lightweight Cross-Domain Sequential Recommendation via External Attention-Enhanced Graph Convolution Network

Cross-domain Sequential Recommendation (CSR) is an emerging yet challenging task that depicts the evolution of behavior patterns for overlapped users by modeling their interactions from multiple domains. Existing studies on CSR mainly focus on using composite or in-depth structures that achieve significant improvement in accuracy but bring a huge burden to the model training. Moreover, to learn the user-specific sequence representations, existing works usually adopt the global relevance weighting strategy (e.g., self-attention mechanism), which has quadratic computational complexity. In this work, we introduce a lightweight external attention-enhanced GCN-based framework to solve the above challenges, namely LEA-GCN. Specifically, by only keeping the neighborhood aggregation component and using the Single- Layer Aggregating Protocol (SLAP), our lightweight GCN encoder performs more efficiently to capture the collaborative filtering signals of the items from both domains. To further alleviate the framework structure and aggregate the user-specific sequential pattern, we devise a novel dual-channel External Attention (EA) component, which calculates the correlation among all items via a lightweight linear structure. Extensive experiments are conducted on two real-world datasets, demonstrating that LEA-GCN requires a smaller volume and less training time without affecting the accuracy compared with several state-of-the-art methods.
Supervised Learning Algorithms: A Comparison

Artificial Intelligence is logical systems where the PCs figures out how to take care of an issue, without unequivocally program them. Machine learning is a subset of AI where machines learn based on the data fed to them. A relative report over various AI managed procedures like Linear Regression, K nearest neighbours, Logistic Regression, Decision Trees, Random Forest, Support Vector Machine and Naive Bayes are made in this paper. The correlation depends on assumptions, influences of co-linearity and exceptions, hyper-parameters, shared examination.
Cloud Portability and Interoperability under the EU Data Act: Dynamism versus Equivalence

This paper examines the provisions of the EU proposal for a Data Act relating to cloud portability and interoperability through a joint economic and legal lens. It suggests that the minutiae of the definition of ‘equivalence’, designed to protect firms consuming cloud computing services, are quite different from portability and interoperability rules for simple products due to product complexity and ongoing innovation. While a certain level of portability and interoperability are natural and essential for customers, the breadth of net cast by ‘equivalence’ as proposed could yield unintended consequences that not only lead to operational challenges in transferring customer assets but even product simplification to a lowest common denominator, reducing availability of the cloud computing service variety that customers seek. In the worst case, an overly expansive definition of ‘equivalence’ could create a race to the bottom that harms dynamic competition and innovation and limits cloud computing service availability in regions covered by the regulation.
Analysis of The Need for The Development of Local-Based English Teaching Materials to Improve Reading Literacy in High School Students

The teaching materials used are English teaching materials which contain local learning concepts that increase the discussion of how to read English correctly in pronouncing words in accordance with grammar. Therefore, English teaching materials are very useful for students to master reading literacy skills. English is also part of the international language contained in the subjects applied in schools. In this case, students are required to have English teaching materials as a guide in mastering English knowledge and are also able to compete nationally and internationally. Thus, the purpose of this study was born, namely to determine the analysis of the need for the development of locally based English teaching materials to improve reading literacy. This research uses a design research development method that takes the form of a type of study validation. The basis of the use of this type of method proves from the theory of learning at the stage of needs analysis. The results of the study show that the usefulness of local learning teaching materials can increase students' reading literacy more independently. Local learning teaching materials can make it easier for teachers to be used as a source of learning media. The conclusion of this study is that local learning teaching materials can be implemented for learning activities and English language learning is more practical and easy to understand .
Emotion Detection using Natural Language Process

The Internet and other types of modern communication technologies have made the process of exchanging information nowadays extraordinarily rapid and fluid. In this day and age, when both mobile phones and computers are prevalent, it is fairly typical for people to use several communication channels, such as mobile phones and computers, in order to communicate with one another. The creation of an Emotion Detection Model that takes into account emotion at the phrase level is one of the tasks that are included in the scope of this project. Concepts from both Natural Language Processing (NLP) and Machine Learning (ML) can be found in content-based classification issues that fall under NLP’s purview. Our methodology makes use of direct emotional keywords that are located throughout the text as a method for determining feelings. These keywords can be located by searching for them. Throughout the process of identification, words and phrases that convey emotional affect were also taken into consideration. This was done with the intention of improving the accuracy of the results. In order to be better at recognizing emotion in textual material, we have thought about a variety of feelings, including happiness, sadness, anger, and other emotions. These are actually phrases that are utilized by human beings in their everyday conversations. These studies are very important because the conclusions that can be derived from them have the ability to better communicate human emotions and to assist in improving communication between persons.
Performance Analysis of Segmentation and Classification of CT scanned Ovarian Tumours using UNet and Deep Convolutional Neural Networks

The difficulty in detecting tumors in earlier stages is the major cause of mortalities of patients, despite the advancements in treatment and research regarding ovarian cancer. Deep Learning algorithms are applied to serve the purpose of a diagnostic tool by applying them on CT scan images of the ovarian region. The images go through a series of pre-processing techniques and further the tumor is segmented using the UNet model. Instances are then classified into two categories – benign and malignant tumors. Classification is performed using Deep Learning models like CNN, ResNet, DenseNet, Inception-ResNet, VGG16 and Xception along with Machine Learning models such as Random Forest, Gradient Boosting, AdaBoosting, XGBoosting. DenseNet 121 emerges as the best model on this dataset even after applying optimization on the Machine Learning models by obtaining an accuracy of 95.7%. The current work demonstrates the comparison of multiple CNN architectures among themselves and with common Machine Learning algorithms, with and without optimization techniques applied.
Quantum Repeater for W states

W states are a valuable resource for various quantum information tasks, and several protocols to generate them have been proposed and implemented. We introduce a quantum repeater protocol to efficiently distribute three-qubit W states over arbitrary distances in a 2D triangular quantum network with polylogarithmic overhead, thereby enabling these applications between remote parties. The repeater protocol combines two ingredients that we establish: probabilistic entanglement swapping with three copies of three-qubit W states to a single long-distance three-qubit W state, and an improved entanglement purification protocol. The latter not only shows a better performance, but also an enlarged purification regime as compared to previous approaches. We show that the repeater protocol allows one to deal with errors resulting from imperfect channels or state preparation, and noisy operations, and we analyze error thresholds, achievable fidelities and overheads.
Polyvinylidene Fluoride (PVDF)/Modified Clay Hybrid Membrane for Humic Acid and Methylene Blue Filtration

This research studied the impact of silanized clay modification on performance and antifouling Poly(vinylidene fluoride) (PVDF) membrane toward humic acid and methylene blue filtration. Clay modification was carried out by using 3-aminopropyltriethoxysilane (APS) to produce modified clay (Clay-APS). Hybrid membranes were prepared by phase inversion for humic acid and methylene blue filtration. Hybrid membranes were characterized by measuring surface hydrophilicity, water flux, rejection, and antifouling properties. Clay and Clay-APS modification increased hybrid membrane surface hydrophilicity, as indicated by increasing the β fraction and decreasing the water contact angle. The PVDF/Clay and PVDF/Clay-APS hybrid membranes showed high permeability and selectivity with the highest water flux values of 24.2 L m−2 h−1. The rejections for humic acid and methylene blue were 98.8 and 99.3%, respectively. The highest antifouling property was obtained from the PVDF/Clay-APS hybrid membrane, with a flux recovery ratio was 96.0%. The PVDF/Clay hybrid membrane performance and antifouling properties showed that the membranes have the potential for water treatment.
A GAN-based method for 3D lung tumor reconstruction boosted by a knowledge transfer approach

Three-dimensional (3D) image reconstruction of tumors has been one of the most effective techniques for accurately visualizing tumor structures and treatment with high resolution, which requires a set of two-dimensional medical images such as CT slices. In this paper we propose a novel method based on generative adversarial networks (GANs) for 3D lung tumor reconstruction by CT images. The proposed method consists of three stages: lung segmentation, tumor segmentation and 3D lung tumor reconstruction. Lung and tumor segmentation are performed using snake optimization and Gustafson-Kessel (GK) clustering. In the 3D reconstruction part first, features are extracted using the pre-trained VGG model from the tumors that detected in 2D CT slices. Then, a sequence of extracted features is fed into an LSTM to output compressed features. Finally, the compressed feature is used as input for GAN, where the generator is responsible for high-level reconstructing the 3D image of the lung tumor. The main novelty of this paper is the use of GAN to reconstruct a 3D lung tumor model for the first time, to the best of our knowledge. Also, we used knowledge transfer to extract features from 2D images to speed up the training process. The results obtained from the proposed model on the LUNA dataset showed better results than state of the art. According to HD and ED metrics, the proposed method has the lowest values ​​of 3.02 and 1.06, respectively, as compared to those of other methods. The experimental results show that the proposed method performs better than previous similar methods and it is useful to help practitioners in the treatment process.
Building Kashmiri Sense Annotated Corpus and its Usage in Supervised Word Sense Disambiguation

Objectives:In this research work maiden attempt is made towards developing a sense annotated corpus for Kashmiri Lexical Sample Word Sense Disambiguation (WSD).Sense annotated dataset is required to use Supervised WSD techniques which are the most effective techniques to carry out WSD.As developing a sense-tagged dataset is an arduous task such datasets are not available for all natural languages.Kashmiri being computationally a lowresource language does not have a sense-tagged corpus available for research purposes.Methods: To develop the sense annotated dataset we selected 60 commonly used ambiguous Kashmiri words and annotated the dataset using the manual annotation method.The usefulness of the dataset is also examined by implementing machine learning algorithms (k-NN, Decision Tree (DT) and Support Vector Machine (SVM)) on it.Part of Speech (PoS) and Bag of Words (BoW) features are used to train the classifiers.Findings: The performance of the machine learning algorithms for Kashmiri WSD is evaluated using accuracy metric.Out of the different classifiers used SVM showed the best performance with an average accuracy of 75.74%.Novelty: This research is the first attempt to develop a sense-tagged dataset for Kashmiri language.The developed dataset would be of great importance to the research community and can be used in various Natural Language Processing tasks like WSD, part-of-speech tagging.
Smart Cup for a Smart Pill Dispenser for Verification of Pill Consumption

This research paper describes the design of a device that can assist seniors or people with physical or cognitive limitations to take their prescribed medications that are in the form of pills on time while verifying that such pills have been actually consumed. The design consists of a portable smart pill dispenser that will rest on a base, allowing it to dispense pills into a smart cup. The smart pill dispenser uses a stepper motor to rotate to a desired pills based on a specific time slot/day of the week. The smart cup attached to the pill box uses an accelerometer, gyroscope, and an IR proximity sensor to detect if a user is taking the medication by how much the smart cup is lifted and tilted. The smart cup will inform the smart pill dispenser if the pills are properly consumed or not, thus, allowing the device to potentially aid the patients to have a healthier life.
Human trajectory estimation using analog privacy-preserving urban sensing technologies

In recent years, there has been considerable interest globally in “smart cities,” which aim to improve the performance of urban systems and the experiences of citizens. However, growing interest in smart cities has given rise to many underlying challenges. Society is at a critical juncture where the decisions made to integrate technologies into daily life can either help create an equitable future, or will heighten the inequitable distribution of resources, knowledge, and power in society and infringe on privacy. Nowhere is this more notable than in civil infrastructure systems (e.g., transportation, social infrastructure, the grid, buildings), which are the foundation of society, provide basic public services to communities, and play a critical role in the distribution and usage of energy, goods, and mobility resources. Underpinning the management of many of these civil infrastructure systems is the spatio-temporal tracking of humans and the measurement of human-infrastructure interaction. Already, we have witnessed countless engagements where camera-based sensing systems are designed and deployed to track humans in public spaces. While camera-based solutions often promise to anonymize data by processing video footage using automated data processing tools, many communities are resistant to trust camera-based monitoring due to infringements on privacy and overarching notions of “Big Brother” within the community. Consequently, there is a need to track humans spatially and temporally in a way that minimizes reliance on privacy-invasive sensing technologies (e.g., cameras). In this paper, we design and exploit a human-tracking sensing network that leverages low-power, privacy-preserving passive infrared (PIR) sensors, which merit energy efficiency (i.e., no reliance on fixed/wired energy sources) and full privacy protection. To fill the gaps of existing work, we leverage the analog capabilities of PIR sensors to extract and quantify greater information–—direction of travel, distance relative to the sensor, and speed–—with a single PIR sensor, which would otherwise require multiple PIR sensors operating in a binary fashion in series to gain only partial information. Improved understanding and quantification of the information that can be extracted from the PIR sensor’s signal will pave the way for the development of human tracking networks that can explicitly consider tradeoffs between the value of information gained from a sensor and community-driven privacy concerns. To make this work more accessible by diverse research communities, the electrical software and hardware developed are open-sourced and detailed.
Lexical Tone as a Cue in Statistical Word Learning from Bilingual Input

Learners can track word-referent co-occurrences across individually-ambiguous naming events to form correct word-referent mappings, termed statistical word learning (SWL). Prior research largely focuses on learning from a single language input, where a referent co-occurs with a single word (1:1 mapping). Here, we tested adults’ SWL from a simulated bilingual environment, where one referent co-occurred with two words (2:1 mapping) and the two words were either differentiated by a linguistic cue (Mandarin lexical tones, Cued condition) or not (Uncued condition). Results showed that in the Cued condition, Chinese-English bilinguals (N = 38) outperformed Spanish-English bilinguals (N = 56) and English monolinguals (N = 55), while Spanish-English bilinguals and English monolinguals performed similarly. The three groups did not differ in the Uncued condition. Self-reported learning confidence and strategies showed limited conscious awareness of learning. Results demonstrate that familiarity with a linguistic cue boosts overall statistical word learning from bilingual input.
A Static Analysis of Informed Down-Samples

We present an analysis of the loss of population-level test coverage induced by different down-sampling strategies when combined with lexicase selection. We study recorded populations from the first generation of genetic programming runs, as well as entirely synthetic populations. Our findings verify the hypothesis that informed down-sampling better maintains population-level test coverage when compared to random down-sampling. Additionally, we show that both forms of down-sampling cause greater test coverage loss than standard lexicase selection with no down-sampling. However, given more information about the population, we found that informed down-sampling can further reduce its test coverage loss. We also recommend wider adoption of the static population analyses we present in this work.