Reviewerly/exHarmony huggingface dataset:
Data Protection Using Multiple Servers for Medical Supply Chain System

Nowadays most of the organizations are experiencing data breaches either from the insiders or externally from outside of the organization. Therefore, at the exact time patient’s personal health data would not be circulated here and there. As data leakage can lead to huge loss of money and create lot of issues in the organization. Even though they are implementing certain security mechanisms but they fail to store them in a secured manner. Present inventions can only be able to protect the data during the transmission stage, but cannot stop the insider attacks. Sometimes they are also chances; here the manager of the patient database can reveal the sensitive information. Therefore, in order to overcome all this issues, we propose a model to get rid of inside attacks with the help of multiple servers to store patient’s data. Our main contribution is to securely distribute the patient data in numerous data servers which employed the Paillier and ElGamal cryptosystems for performing statistical study on the patient data without cooperating the patients’ confidentiality.
A cluster workload forecasting strategy using a higher order statistics based ARMA model for IaaS cloud services

With cloud services becoming more popular among internet users, cloud providers are facing a challenge in allocating resources according to users' demand instantly due to the delay caused by the virtual machines' start up time. This problem can be solved using proactive allocation techniques that predict the workload in advance and make scaling decisions ahead of time. In this paper, we present an adaptive workload prediction method based on higher order statistics (HOS) and autoregressive moving average (ARMA) model. We use HOS to make a Gaussianity checking test of the cloud workload and decide the suitable identification method of the ARMA model to be used for forecasting. We evaluate our proposal with two real traces extracted from cluster workloads. The results show that the proposed method has an average of 34% higher accuracy than the baseline ARMA model and presents a low forecasting overhead (< 2 s).
Comparative Analysis of Various Text Summarization Techniques via Leveraging Transformer Model for the Fake News Detection

Today, the internet has come to be a necessary part of our lifestyle. The role of traditional information channels consisting of newspapers and televisions on how we acquire and consume news has to become much less prominent than within the past. Indeed, the boom of social media structures has performed a critical function in this variation. Oppositely, it empowers the widespread of ”fake news,” i.e., low-quality news with purposefully false data. The broad spread of fake news has the potential for incredibly adverse effects on people and society. The proposed research work aims to design a robust model for an automatic fake news detection system to help journalists and everyday users from misleading content. In this paper, we have studied and performed a deep comparison of leading Transformer-based models for the task of text classification and explored and compared various text summarizing techniques for dealing with more prominent long-length articles before classifying them through existing models. BERTSUM gives the most noticeable results out of all the three methods by enabling us to create a system to label an arbitrarily long article as fake or genuine.
Improving eGovernment Services with Blockchain: Restoring Trust in e-voting Systems

Trust in e-voting systems has declined due to claims of fraud. The trustless blockchain technology offers new hope to restore elector’s trust on e-voting systems. The distributed ledger technology, often referred to as blockchain, provides desirable e-voting characteristics including provenance, immutability, security, and finality. Several technological, organizational, and environmental factors impede the adoption of blockchain technology, such as, its novelty, the perception that it is an unproven technology, its association with the seamier side of cryptocurrency, and uncertainty among prospective stakeholders about how to configure and implement blockchain solutions to serve their unique needs. However, evidence demonstrates that a socio-economic effect or benefit will catalyze the adoption of blockchain technology. This paper presents a modified framework for eGovernment blockchain technology adoption and a use case to help educate the public. The paper recommends field study for the proposed framework and more use cases to help familiarize the public about voting in a blockchain-based platform. Governments leading the way by adopting blockchain-based eGovernment services goes a long way in facilitating blockchain technology adoption by the public, hence, restore trust in blockchain based e-voting systems.
Automatic wheel-type water meter digit reading recognition based on deep learning

The object detectors based on deep learning, such as Yolov3, Yolov3-tiny, and Yolov4-tiny, have achieved real-time object detection in various vision tasks. However, long-tailed distribution of classes, small objects, and incomplete numbers on the dataset have brought challenges to the automatic recognition of wheel-type water meter digit reading using these object detectors. We propose a Mix suppression data augmentation method based on information deletion and resampling. The method improves the long-tailed distribution of the water meter dataset and the performance of small object detection. Moreover, to further improve the performance of detection model, we propose a block spatial attention module to connect with the output of the backbone of object detection model. This module can introduce priori knowledge into the convolutional neural network, reducing the phenomenon of missing objects. The experiments show that our methods successfully improve the performance of neural networks for the water meter digit reading recognition, and they could also be conveniently embedded into other mainstream general object detection models. In addition, we construct a dataset named waterDataset for noncommercial use, which is available at a Github repository: https://github.com/l1158984238/waterDataset.
Transformer Uncertainty Estimation with Hierarchical Stochastic Attention

Transformers are state-of-the-art in a wide range of NLP tasks and have also been applied to many real-world products. Understanding the reliability and certainty of transformer models is crucial for building trustable machine learning applications, e.g., medical diagnosis. Although many recent transformer extensions have been proposed, the study of the uncertainty estimation of transformer models is under-explored. In this work, we propose a novel way to enable transformers to have the capability of uncertainty estimation and, meanwhile, retain the original predictive performance. This is achieved by learning hierarchical stochastic self-attention that attends to values and a set of learnable centroids, respectively. Then new attention heads are formed with a mixture of sampled centroids using the Gumbel-Softmax trick. We theoretically show that the self-attention approximation by sampling from a Gumbel distribution is upper bounded. We empirically evaluate our model on two text classification tasks with both in-domain (ID) and out-of-domain (OOD) datasets. The experimental results demonstrate that our approach: (1) achieves the best predictive-uncertainty trade-off among compared methods; (2) exhibits very competitive (in most cases, better) predictive performance on ID datasets; (3) is on par with Monte Carlo dropout and ensemble methods in uncertainty estimation on OOD datasets.
Measuring laser beams with a neural network

A deep neural network (NN) is used to simultaneously detect laser beams in images and measure their center coordinates, radii, and angular orientations. A dataset of images containing simulated laser beams and a dataset of images with experimental laser beams-generated using a spatial light modulator-are used to train and evaluate the NN. After training on the simulated dataset the NN achieves beam parameter root mean square errors (RMSEs) of less than 3.4% on the experimental dataset. Subsequent training on the experimental dataset causes the RMSEs to fall below 1.1%. The NN method can be used as a stand-alone measurement of the beam parameters or can compliment other beam profiling methods by providing an accurate region-of-interest.
Discovering Bursting Patterns over Streaming Graphs

A streaming graph is a constantly growing sequence of directed edges, which provides a promising way to detect valuable information in real time. A bursting pattern in a streaming graph represents some interaction behavior which is characterized by a sudden increase in terms of arrival rate followed by a sudden decrease. Mining bursting pattern is essential to early warning of abnormal or notable event. While Bursting pattern discovery enjoys many interesting real-life applications, existing research on frequent pattern mining fails to consider the bursting features in graphs, and hence, may not suffice to provide a satisfactory solution. In this paper, we are the first to address the continuous bursting pattern discovering problem over the streaming graph. We present an auxiliary data structure called BPD for detecting the burst patterns in real time with a limited memory usage. BPD first converts each subgraph into a sequence, and then map it into corresponding tracks based on hash functions to count its frequency in a fixed time window for finding the bursting pattern. Extensive experiments also confirm that our approach generate high-quality results compared to baseline method.
INFORMATION AND COMMUNICATIONS TECHNOLOGY IN THE EDUCATIONAL PROCESS OF PRIMARY SCHOOL: IMPACT ON SCHOOLCHILDREN

We analyze possibilities and limitations of information and communications technology in primary general education to increase effectiveness of cognitive activity among students and motivate them to independent activity. The informatization of primary schools affects the quality of the educational process. Management simplification in the educational process with information and communications technology enables to organize the exchange of pedagogical experience, to expand the didactic possibilities of the lesson. The following positive possibilities of using information and communications technology in the implementation of the educational process in primary school are increased motivation to learn, activation of cognitive activity, effective implementation of the visibility principle, individualization and differentiation of learning, mastery of variable ways of obtaining and processing information. Prolonged and systematic use of information and communications technology may cause a negative impact on the physical, psychological state and health of younger schoolchildren. As a result, it is necessary to comply with restrictions when using information and communications technology in primary classes. The use of information and communications technology in the course of the educational process is a necessary, and not an exclusive (special, exclusive) means of ensuring educational activities in general. The position on the inevitability of the variable use of information and communications technology both at school and beyond is proved.
SynCare: An Innovative Remote Patient Monitoring System Secured by Cryptography and Blockchain

Remote patient monitoring involves the collection of patient-generated health data, using sensors/devices and mobile apps, to allow observation of patient’s health status, also outside healthcare environments. The challenge in this field is to facilitate patient-centric data storing, sharing, and retrieving, with high attention to personal, sensitive data privacy and protection. This study presents SynCare, a patient-centered ecosystem developed by LifeCharger, for secure health-related data recording and remote patient monitoring. SynCare has been developed with the aim of making up a strong loop between patients, healthcare professionals and informal caregivers, building up secure channels for data sharing and supporting the patients in the management of their own health and related data. The system includes: 1) a mobile app for the patient, offering different features supporting the therapy and allowing the management of consents to share key data with the healthcare professionals and/or caregivers, 2) a database on Cloud, storing all the encrypted, sensitive health-data, 3) public Ethereum blockchain to validate the data sharing consents, 4) a clinical dashboard developed as a web application whose main purpose is to allow healthcare professional to display and analyze the data collected by the patient through the mobile app. The SynCare ecosystem implements a software developed by LiberActa srl to asynchronously load the anonymous consent data on the Ethereum public blockchain, decoupling the user experience from the blockchain interaction, which can be slow, without compromising the data security.
SparseMAX: Accelerating Quantum Neural Networks on GPU Clusters Using Sparse-Matrix Kernels

The growing popularity of Applied Quantum Mechanics and Artificial Intelligence drives the need for integrating the two fields. Quantum Neural Networks (QNNs) incorporate quantum aspects into classical deep learning networks which are capable of performing universal quantum computations. The dense representation of QNNs presents great challenges in terms of computational cost and noise susceptibility. In this paper, we present SparseMAX, a novel Sparse Quantum Neural Network (SQNN) that is robust to noise and interference for large volumes of network parameters. We also introduce Quantron ( $$\psi $$ ), a generalized version of perceptron, which acts on qubits and performs the necessary quantum operations. Based on these insights, we develop 2 GPU kernels. The first kernel estimates the network architecture through a quantum training algorithm. The second kernel accelerates a sparsified version of the network matrices on a GPU cluster. We validate our kernel performance and training algorithm and present the results in terms of inference time, GPU efficiency and scalability. On an average, SparseMAX utilizes 54.83% of our GPU cluster’s compute resources, while offering a 41.51 $$\times $$ speedup in terms of serial inference timing measurements for network layer range [120, 1920] and neurons per layer range [1024,4096]
Improving Extreme Search with Natural Gradient Descent Using Dirichlet Distribution

Natural gradient descent is an optimization algorithm, which is proposed to replace stochastic gradient descent and its modifications. The most precious ability of this algorithm is to reach the extreme with little number of iterations and required accuracy, which has high value in machine learning and statistics. The goal of this article is to propose a natural gradient descent algorithm with the Dirichlet distribution, which includes step-size adaptation. We will prove experimentally advantage of natural gradient descent over stochastic gradient descent and Adam algorithm. Additionally, the calculating of the Fisher information matrix of Dirichlet distribution will be shown.
Modified LSB Algorithm Using XOR for Audio Steganography

Audio-based steganography has the ability to hide a lot of information. Slight changes in amplitude can store vast amount of information. Audio files are generally bigger than images in terms of size, thus enabling more data transfer. The human ear cannot differentiate between a normal audio file and a ‘stego’ audio file, thus making it almost impossible for a common person to know that a message is embedded in the audio. Thus, audio steganography is a very powerful method to transfer information. The objective of this paper is to obtain a secure and robust way to conceal high rates of secret data by audio steganography using LSB technique. The drawback of the LSB method is the easy extraction and destruction of information. This is overcome by using XOR operation instead of conventional OR operation and by setting a secret encryption key while embedding the audio with the secret text. It is observed that the security of the transmission is highly increased by using this method.
Favourable subpopulation migration strategy for travelling salesman problem

Most of the existing migration strategies are complex in nature which results in high computation time. Since the introduction of parallel genetic algorithms, they are known for obtaining better optimal solutions at the cost of more computation. Migration has played a significant role in maintaining genetic diversity of the population. This paper aims to propose a new migration strategy that improves the performance of the parallel genetic algorithm in solving NP hard problems. In order to assess its behaviour, various travelling salesman problem datasets are considered since it is a perfect example of an NP hard problem. The algorithm is based on an island model approach where one subpopulation is nurtured by the other subpopulations by taking turns during the migration process to progressively reach the optimal solution. In turn, the nurtured subpopulation provides a feedback loop to the subpopulation with poor fitness value based on certain criteria to be met. An extensive study was conducted to understand the optimal parameters values in which the proposed algorithm works best.
The development of a micro-teaching module for online learning

The purpose of this research was to develop a module of micro teaching-based technology for online learning for students at STKIP Muhammadiyah Muara Bungo and to analyze the module's validation and appropriateness for online learning. The development procedures for this research utilized a 4-D model (Define, Design, Develop, and Disseminate), as demonstrated by the validation analysis conducted with learning material experts, learning media experts, and students. The average score for all aspects combined is 82.6. It demonstrates an excellent category. This means that the microteaching module in online learning serves as instructional material for improving teaching ability through the use of technology. The microteaching online learning module is valid and appropriate for use.
Grade Estimation of Mineral Resources: An Application of Cellular Automata

Paty, Soumyadeep Kamilya, SupreetiMineral industry contributes a major part of economical growth to a nation. Therefore, it is important to understand the concentration measurement of minerals in different locations of earth’s crust. The concentration refers to as the grade values of minerals. A block model is a simplified geometrical representation of a mineral deposit. There are several methods to find out the grade values of each block, such as, inverse distance method, copula, krigging etc. Krigging is a popular method to find out the grade values. However, number of mathematical calculations increase with the increasing number of sample points and thus the computational complexity becomes high. Here, we use a mathematical tool, cellular automata (CAs) where each cell is represented as a block. Using cellular automata, we here find out the grade values with much less computations. 2 dimensional CAs are used in this study where the local rule is the ordinary krigging estimator function using spherical variogram model. Also, in this study we deal with multiple horizontal slices of 3 dimensional mineral deposits. It can be seen that the CA based estimation is same as ordinary krigging based estimation, however, in a much simpler way.
Abnormal data detection of guidance angle based on SMP-SVDD for seeker

The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, a new abnormal data detection method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed. In the proposed method, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, in which the particle swarm optimization is used to find the optimal kernels for higher detection accuracy. Besides, by using smoothing mechanism, the constrained quadratic programming problem is translated to be smooth and differentiable. Then, this problem can be solved by the conjugate gradient method, which could reduce the computational complexity. In experimental section, abundant simulation experiments were designed and the experimental results verify that the proposed SMP-SVDD method could achieve higher detection accuracy and low computational cost compared with different detection methods in different guidance stages.
StreamB: A Declarative Language for Automatically Processing Data Streams in Abstract Environments for Agent Platforms

To apply BDI agents to real-world scenarios, the reality-gap, between the low-level data (perceptions) and their high-level representation (beliefs), must be bridged. This is usually achieved by a manual mapping. There are two problems with this solution: (i) if the environment changes, the mapping has to be changed as well (by the developer); (ii) part of the mapping might end up being implemented at the agent level increasing the code complexity and reducing its generality. In this paper, we present a general approach to automate the mapping between low-level data and high-level beliefs through the use of transducers. These transducers gather information from the environment and map them to high-level beliefs according to formal temporal specifications. We present our technique and we show its applicability through a case study involving the remote inspection of a nuclear plant.
On Endomorphisms of the Additive Monoid of Subnets of a Two-layer Neural Network

Previously, for each multilayer neural network of direct signal propagation (hereinafter, simply a neural network), finite commutative groupoids were introduced, which were called additive subnet groupoids. These groupoids are closely related to the subnets of the neural network over which they are built. A grupoid is a monoid if and only if it is built over a two-layer neural network. Earlier, endomorphisms and their properties were studied for these groupoids. Some endomorphisms were constructed, but an exhaustive element-by-element description was not received. It was shown that every finite monoid is isomorphic to some submonoid of the monoid of all endomorphisms of a suitable additive subnet groupoid for some suitable neural network. In this paper, we study endomorphisms of additive groupoids of subnets of twolayer neural networks. The main result of the work is an element-wise description of the monoid of all endomorphisms of additive monoids of subnets built over a two-layer neural network. The item-by-item description is obtained by constructing a general form of endomorphism. The general view of an endomorphism is parameterized by the endomorphisms of suitable booleans with respect to the union operation. Therefore, endomorphisms of these Booleans were studied in this work. In particular, the semirings of endomorphisms of these Booleans with respect to the union were studied. In addition, to describe the general form of the endomorphism of the additive monoid of subnets, homomorphisms of one Boalean into another (with respect to union) were used.