Reviewerly/exHarmony huggingface dataset:
Multi-layer encryption algorithm for data integrity in cloud computing

The Internet revolution has changed all aspects of our life, accessing and surfing the Internet is becoming as necessity in our life to obtain any kind of information needed, that produces a huge quantity of data available any time, any where. The greater the facilities, less our data privacy. For that maintaining data confidentiality and integrity in the Internet and cloud world is a primary requirement to continue working and using these facilities. In this paper, we propose a security mechanism that makes accessing the data from unauthorized persons more complex. Our mechanism easy to implement and understand but is complex to decrypt because it works in three levels in each level is used a new encryption key generated randomly and in each level is used a different encryption algorithm. Our results show that the encryption/decryption process time is increased linearly with the text size for the authorized persons and is increased exponentially for unauthorized persons.
Abstractive Summarization System

The World Wide Web has evolved into one of the world's most extensive information and knowledge repositories. Despite their ease of access, the great majority of such individual publications are extremely difficult to analyse or evaluate. Text summaries assist users in achieving such information-seeking goals by providing rapid access to the highlights or important features of a document collection. Abstractive summarization attempts to reduce a given text to its core components based on the user's preference for brevity. To summarise, there are two approaches: extraction and abstraction. Statistical techniques are used for extracting most important sentences from a corpus. Abstraction entails reformulating material based on the type of summary. This approach makes use of more adaptive language processing technology. Despite the fact that abstraction yields better summaries, extraction remains the favoured strategy and is widely employed in research. A number of approaches, including cosine, can be used to calculate the measure of resemblance between articles. Sentences' statistical &amp; linguistic features are utilised to determine their importance. An abstractive summary is used to absorb the fundamental concepts of a material and then summarise them into plain English.
KoBERTSEG: Local Context Based Topic Segmentation Using KoBERT

Topic segmentation refers to the work of separating a document consisting of several topics into unit documents, such as paragraphs, with one single topic. Topic segmentation has been considered as one of main preprocessing step prior to performing natural language processing tasks, such as document summary or document classification. This paper proposes a Korean BERT-based news article segmentation method aiming at separating a single news article, in which multiple subjects exist, into news segments, each of which contains a single subject. The proposed model has the advantage of being able to capture a wider range of semantic relationships compared to existing topic segmentation studies by borrowing a structure proposed for document summarization. Experimental results on a Korean news article dataset show that the proposed method outperform the benchmark models for topic segmentation. In addition, we also show that the proposed method can be used for practical news clip summarization task, supporting the possibility of implementing the application service based on Korean topic segmentation model.
A method of multi-target bearings-only association and location with two platforms

It is not easy for a single passive platform to realize target location by just bearings-only measurements, unless it carries a time-consuming laborious maneuvering. By contrast, with two platforms we could simply achieve accurate target location through triangulation approach without the need of platform maneuvering. However, this two-platform approach introduces an inter-platform association problem in multi-target scenarios, that is, one should recognize and exclude the false targets generated from wrongly paired bearings. To solve this, in this paper, a target motion analysis (TMA) based approach is proposed. Through a pre-TMA of all possible targets, the method calculates the confidence coefficients of all possible targets for recognition (real or false), which are then used to associate inter-platform bearings and select real targets. Simulation verification is performed through using Monte Carlo technique. The result demonstrates that the proposed method can achieve accurate inter-platform multi-target data association and multi-target location.
METHODS AND MODELS OF SOFTWARE DEVELOPMENT USING AUTOMATED APPROACH

The present article deals with the main methods and models of automatic software systems development. The main types of implementations of automata abstract models are determined and analyzed. A new option of creating a software system module that is responsible for the behavior of an object, which can change depending on its current state, is proposed. The diagram and main components of this module are presented.
Design of an airborne video processing and digital map video generation system

With the development of avionics systems, ARINC 818 video, PAL video and digital map video have been widely used in airborne avionics systems. For three different videos, the system of an airborne video processing and digital map video is designed. Among them, the airborne video processing uses Among them, the airborne video processing uses programmable logic device FPGA as the core, which realizes the functions of airborne video capture and reception, gating output, format conversion and Digital map generation uses CPU main processor + GPU graphics processor as the core. The core realizes the reception of digital map data and the generation of map videos. The system solution uses low-power, high-performance FPGA and CPU+GPU as the core processor, which simplifies the This design has been successfully used in a certain type of display This design has been successfully used in a certain type of display control management system.
Q-means using variational quantum feature embedding

This paper proposes a hybrid quantum-classical algorithm that learns a suitable quantum feature map that separates unlabelled data that is originally non linearly separable in the classical space using a Variational quantum feature map and q-means as a subroutine for unsupervised learning. The objective of the Variational circuit is to maximally separate the clusters in the quantum feature Hilbert space. First part of the circuit embeds the classical data into quantum states. Second part performs unsupervised learning on the quantum states in the quantum feature Hilbert space using the q-means quantum circuit. The output of the quantum circuit are characteristic cluster quantum states that represent a superposition of all quantum states belonging to a particular cluster. The final part of the quantum circuit performs measurements on the characteristic cluster quantum states to output the inter-cluster overlap based on fidelity. The output of the complete quantum circuit is used to compute the value of the cost function that is based on the Hilbert-Schmidt distance between the density matrices of the characteristic cluster quantum states. The gradient of the expectation value is used to optimize the parameters of the variational circuit to learn a better quantum feature map.
On the optimization of hyperparameters in Gaussian process regression

When the data are sparse, optimization of hyperparameters of the kernel in Gaussian process regression by the commonly used maximum likelihood estimation (MLE) criterion often leads to overfitting. We show that choosing hyperparameters based on a criterion of the completeness of the basis in the corresponding linear regression problem is superior to MLE. We show that this is facilitated by the use of High-dimensional model representation whereby a low-order HDMR representation can provide reliable reference functions and large synthetic test data sets needed for basis parameter optimization even with few data.
Understanding Layer-wise Contributions in Deep Neural Networks through  Spectral Analysis

Spectral analysis is a powerful tool, decomposing any function into simpler parts. In machine learning, Mercer's theorem generalizes this idea, providing for any kernel and input distribution a natural basis of functions of increasing frequency. More recently, several works have extended this analysis to deep neural networks through the framework of Neural Tangent Kernel. In this work, we analyze the layer-wise spectral bias of Deep Neural Networks and relate it to the contributions of different layers in the reduction of generalization error for a given target function. We utilize the properties of Hermite polynomials and spherical harmonics to prove that initial layers exhibit a larger bias towards high-frequency functions defined on the unit sphere. We further provide empirical results validating our theory in high dimensional datasets for Deep Neural Networks.
The Obstacle Challenging SMEs Performance in Ogun State, Nigeria

This study presents a conceptual framework for determining the effects of Market Orientation, Entrepreneurial Orientation, and Information Technology on the performance of small and medium-sized firms (SMEs) in Nigeria. The resource-based value theory was used to explain the elements affecting SMEs' performance. Furthermore, this study’s conceptual framework is based on the data derived from a newly constructed questionnaire. 130 copies of the questionnaires were distributed to the CEO / director and managers of SME organisations in Ogun State, Nigeria. Out of the 210 questionnaires, half or 105 were returned and analysed. The Statistical Package for the Social Sciences (SPSS) software was used to analyse the empirical data. It was found that the performance of SMEs organizations is significantly influenced by two factors, market orientation and entrepreneurial orientation, while there is no significant relationship between information technology and SME performance. This study provides valuable input for SMEs organisations by helping them to strategise future operational plan in various industries, including the education sector. The main limitation of this study is that has a limited sample size, and it did not include external performance indicators outside of procurement. As these indicators could provide additional insight into the success of SMEs, future research could consider including them as variables.
The digitalization and its influence on combating corruption

In last years, many governments in all over the world have made a lot of efforts to improve the level of disclosure and transparency in their actions by using the information and communication technology (ICT) which includes: the internet, mobile applications and the social media. And the main target for this transformation is not only to improve the disclosure and transparency, but also to combat corruption. In this paper we are defining the digitalization and corruption and analyzing from the available resources the influence of digitalization on combating and eliminating the corruption.
Beneficial Impact of Internet of Things in Business – A Critical Review

The Internet of things (IoT) assigns actual substances (or gatherings of such materials) that are encircled with gadgets, taking care of capacity, programming, other than other aptitude that connection and trade realities utilizing different devices and frameworks with the assistance of Internet or other correspondence conditions. The region has advanced because of the converging of complex innovations, containing plentiful processing, item sensors, continuously noticeable inserted frameworks, other than AI. Standard fields of installed frameworks, remote gadget joins, controller frameworks, robotization (including home and development mechanisations), independently and commonly enabling the Internet of Things.IoT innovation is identical to items concerning the possibility of the “savvy homes”, with gadgets (like light fittings, indoor regulators, home security gadgets then observation cameras, other than other domestic devices) that contrary energies at least one shared ecologies, likewise can be arranged by means of contraptions that are interconnected with the organization, as cell phones notwithstanding brilliant speakers. The IoT could be used even in medical services associations too with which the illnesses can be spotted and suitable therapy gave to the patients. By the by, the IoT isn’t liberated from gambles being developed of this exact advancements and items, explicitly with regards to the part of protection and security worries and subsequently the public authority and modern moves have likewise begun, thinking about the improvement of worldwide and nearby benchmarks, proposals and administrative designs. This specific paper gives a more extensive comprehension of the IoT idea concerning how the headway in innovation has changed the different areas in the general public to adjust to connect themselves with the implanted programming. Important benefits given by IoT have been pondered exhaustively. How much the advantages are being valued by different ventures overall are likewise pondered.
A Practice of Combining Game and Exercise using Virtual Reality

Virtual Reality (later all abbreviated as VR) technology has been widely applied in many fields. With its traits like high interactivity and immersivity, many remarkable VR applications for different use have come out and been popular. We design and develop a motion-based VR interactive game that utilizes the advantages of VR to help users get mild physical activities in a fun way within a confined indoor area. In the game, various game objects move towards the players as the background music starts. Players are supposed to move their body in the real world to avoid barriers, hit oncoming game objects and rotate their directions in a virtual world. Besides, a game guide was completed to help players get familiar with in-game interactions. And various effects were achieved to enhance play experiences. The game was successfully developed using Unity and Oculus Rift series devices. The results of play tests show that it can motivate players to exercise in an immersive virtual environment.
Multi-Task Neural Processes

Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes. As they can encode contextual data in the network's function space, they offer a new way to model task relatedness in multi-task learning. To study its potential, we develop multi-task neural processes, a new variant of neural processes for multi-task learning. In particular, we propose to explore transferable knowledge from related tasks in the function space to provide inductive bias for improving each individual task. To do so, we derive the function priors in a hierarchical Bayesian inference framework, which enables each task to incorporate the shared knowledge provided by related tasks into its context of the prediction function. Our multi-task neural processes methodologically expand the scope of vanilla neural processes and provide a new way of exploring task relatedness in function spaces for multi-task learning. The proposed multi-task neural processes are capable of learning multiple tasks with limited labeled data and in the presence of domain shift. We perform extensive experimental evaluations on several benchmarks for the multi-task regression and classification tasks. The results demonstrate the effectiveness of multi-task neural processes in transferring useful knowledge among tasks for multi-task learning and superior performance in multi-task classification and brain image segmentation.
Towards Neural Functional Program Evaluation

This paper explores the capabilities of current transformer-based language models for program evaluation of simple functional programming languages. We introduce a new program generation mechanism that allows control over syntactic sugar for semantically equivalent programs. T5 experiments reveal that neural functional program evaluation performs surprisingly well, achieving high 90% exact program match scores for most in-distribution and out-of-distribution tests. Using pretrained T5 weights has significant advantages over random initialization. We present and evaluate on three datasets to study generalization abilities that are specific to functional programs based on: type, function composition, and reduction steps. Code and data are publicly available at https://github.com/ElementAI/neural-interpreters.
Assignments and Operators

R works with objects. Objects can include vectors, matrices, functions, the results from a function, or other kinds of objects. Objects make working with information easier. This chapter covers assigning names to objects, listing and removing objects, and object operations. Part II (Chapters 4 and 5) covers the possible forms of objects.
A New Methodology to Arrive at Membership Weights for Fuzzy SVM

Support Vector Machine (SVM) is a supervised classification technique that uses the regularization parameter and Kernel function in deciding the best hyperplane to classify the data. SVM is sensitive to outliers, and it makes the model weak. To overcome the issue, the Fuzzy Support Vector Machine (FSVM) introduces fuzzy membership weight into its objective function, which focuses on grouping the fuzzy data points accurately. Knowing the importance of the membership weights in FSVM, we have introduced four new expressions to compute the FSVM membership weights in this study. They are determined from the Fuzzy C-means Algorithm's membership values (FCM). The performances of FSVM with three different kernels are assessed in terms of accuracy. The experiments are conducted for various combinations of FSVM parameters, and the best combinations for each kernel are highlighted. Six benchmark datasets are used to demonstrate the performance of FSVM and the proposed models’ performance are compared with the existing models in recent literature.
Full Model Estimation for Non-Parametric Multivariate Finite Mixture  Models

This paper addresses the problem of full model estimation for non-parametric finite mixture models. It presents an approach for selecting the number of components and the subset of discriminative variables (i.e., the subset of variables having different distributions among the mixture components). The proposed approach considers a discretization of each variable into B bins and a penalization of the resulting log-likelihood. Considering that the number of bins tends to infinity as the sample size tends to infinity, we prove that our estimator of the model (number of components and subset of relevant variables for clustering) is consistent under a suitable choice of the penalty term. Interest of our proposal is illustrated on simulated and benchmark data.
Naive Automated Machine Learning

An essential task of Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on a given dataset. This problem has been addressed with sophisticated black-box optimization techniques such as Bayesian Optimization, Grammar-Based Genetic Algorithms, and tree search algorithms. Most of the current approaches are motivated by the assumption that optimizing the components of a pipeline in isolation may yield sub-optimal results. We present Naive AutoML, an approach that does precisely this: It optimizes the different algorithms of a pre-defined pipeline scheme in isolation. The finally returned pipeline is obtained by just taking the best algorithm of each slot. The isolated optimization leads to substantially reduced search spaces, and, surprisingly, this approach yields comparable and sometimes even better performance than current state-of-the-art optimizers.
Uncertainty estimation under model misspecification in neural network  regression

Although neural networks are powerful function approximators, the underlying modelling assumptions ultimately define the likelihood and thus the hypothesis class they are parameterizing. In classification, these assumptions are minimal as the commonly employed softmax is capable of representing any categorical distribution. In regression, however, restrictive assumptions on the type of continuous distribution to be realized are typically placed, like the dominant choice of training via mean-squared error and its underlying Gaussianity assumption. Recently, modelling advances allow to be agnostic to the type of continuous distribution to be modelled, granting regression the flexibility of classification models. While past studies stress the benefit of such flexible regression models in terms of performance, here we study the effect of the model choice on uncertainty estimation. We highlight that under model misspecification, aleatoric uncertainty is not properly captured, and that a Bayesian treatment of a misspecified model leads to unreliable epistemic uncertainty estimates. Overall, our study provides an overview on how modelling choices in regression may influence uncertainty estimation and thus any downstream decision making process.
Device-Independent Oblivious Transfer from the  Bounded-Quantum-Storage-Model and Computational Assumptions

We present a device-independent protocol for oblivious transfer (DIOT) in the bounded-quantum-storage-model, and analyze its security. Our protocol is everlastingly secure and aims to be more practical than previous DIOT protocols, since it does not require non-communication assumptions that are typical from protocols that use Bell inequality violations; instead, the device-independence comes from a recent self-testing protocol which makes use of a post-quantum computational assumption.
Discourse-Aware Prompt Design for Text Generation

Current efficient fine-tuning methods (e.g., adapters, prefix-tuning, etc.) have optimized conditional text generation via training a small set of extra parameters of the neural language model, while freezing the rest for efficiency. While showing strong performance on some generation tasks, they don't generalize across all generation tasks. In this work, we show that prompt based conditional text generation can be improved with simple and efficient methods that simulate modeling the discourse structure of human written text. We introduce two key design choices: First we show that a higher-level discourse structure of human written text can be modelled with \textit{hierarchical blocking} on prefix parameters that enable spanning different parts of the input and output text and yield more coherent output generations. Second, we propose sparse prefix tuning by introducing \textit{attention sparsity} on the prefix parameters at different layers of the network and learn sparse transformations on the softmax-function, respectively. We find that sparse attention enables the prefix-tuning to better control of the input contents (salient facts) yielding more efficient tuning of the prefix-parameters. Experiments on a wide-variety of text generation tasks show that structured design of prefix parameters can achieve comparable results to fine-tuning all parameters while outperforming standard prefix-tuning on all generation tasks even in low-resource settings.