Reviewerly/exHarmony huggingface dataset:
Automatic Requirements Engineering Model using Goal-Oriented Modelling with Text Pre-Processing Technique

Requirements engineering (RE) is an essential initial stage in the software engineering process. RE activities include elicitation, analysis, specification, and validation. The efficiency of the RE process relies heavily on a systems analyst to perform software specifications. Automation of activities in RE can increase time efficiency. In this study, the Automatic Requirements Engineering Model (AREM) was developed to automate the analysis, specification, and validation processes of the RE. The model was developed by integrating a goal-oriented model and text pre-processing technique. At the elicitation stage, is developed a standard input document that refers to the GORE elements. The requirements analysis was developed by applying the breadth-first search method, forward chaining, and the agent approach. Text pre-processing techniques were used for document extraction and preparation of requirements specifications. The model produces requirements specifications in the form of goal trees, class diagrams, use case diagrams, and sequence diagrams
Offline Handwritten Character and Numeral Recognition

Automatic Character Recognition for the handwritten Indic script has listed up as most the challenging area for research in the field of pattern recognition. Although a great amount of research work has been reported, but all the state-of-art methods are limited with optimal features. This article aims to suggest a well-defined recognition model which harnessed upon handwritten Odia characters and numerals by implementing a novel process of decomposition in terms of 3rd level Fast Discrete Curvelet Transform (FDCT) to get higher dimension feature vector. After that, Kernel-Principal Component Analysis (K-PCA) considered to obtained optimal features from FDCT feature. Finally, the classification is performed by using Probabilistic Neural Network (PNN) on handwritten Odia character and numeral dataset from both NIT Rourkela and IIT Bhubaneswar. The outcome of proposed scheme outperforms better as compared to existing model with optimized Gaussian kernel-based feature set.
Sentiment Analysis in Indian Sub-continent During COVID-19 Second Wave using Twitter Data

In Indian sub-continent COVID-19 second wave started in early March 2021 and its effect was more lethal than the first wave, the confirmed cases and the death rate was higher than in the first wave. Unlike the national lockdown in 2020, this year different states have started imposing lockdown like restrictions spanning April-June 2021. This paper investigates the sentiments of the people using twitter messages during early period of the second wave. Two-weeks data is manually annotated and several machine learning models were built. The best performing models were used to predict sentiments for the next 2–3 weeks and analysis is presented. Predictions of public, commercial libraries were also analysed in the same context.
Design and Simulation of an Autonomous Surface Vehicle for Trash Collection using ROS

While it is true that the earth is covered with 70 percentage of water out of which only 3 percentage of it is drinkable in the form of rivers and lakes. However, by releasing sewage and industrial wastes water has become polluted and is undrinkable. The world water Assessment Program estimates that in developing countries, 70 percentage of industrial wastes are dumped untreated into waters where they pollute the usable water supply and in a country like india where rivers are considered as sacred waters like Ganga, Yamuna and Godavari are contaminated by bathing and throwing flowers, diyas and plastic waste in the name of worship. Every year an estimated 8 million tonnes of garbage is dumped from somewhere in the region of 600,00 places of worship into rivers. Rejuvenation of these holy bodies has been initiated by indian government in the name of Namami Gange. To aid the process of rejuvenation this paper presents a novel Autonomous Surface Vehicle (ASV) model that cleans the water bodies from floating trash, based on Robot Operating System (ROS) for design, simulation and its control system. The use of Unmanned Surface Vehicles as a tool for inspection, monitoring and exploring of water bodies in lakes and ponds has become a research area of growing interest. However, using real models to test and development of software and their deployment is not feasible due to expensive and complicated parts used in surface vehicles to overcome that, software simulations are used which can replicate real world environments in the simulation world and helps in developing control systems using sensor data. The model has been simulated in a water environment using ros for testing of the control system and movement of the model. Simulation results have shown that the proposed work can be an alternative to collect trash floating on the surface of water with low cost and minimum human intervention.
3D Image Inpainting for Rotor Detection using 3D Encoder-Decoder Generative Adversarial Network

In recent years, 3D sensors are widely applied in modern manufacturing automation such as the rotor dynamic balancing. However, the overexposed rotor image will reduce the accuracy of rotor detection, so in this paper we proposed a 3D image jnpainting method for rotor detection using 3D Encoder-Decoder Generative Adversarial Network (3D-EDGAN). The algorithm consists of 3D Encode-Decode Generator based on 3D CNN to generate predicted model and a Discriminator to classify whether the generated model is real or fake. Experimental results showed that our proposed mothed was superior in terms of efficiency, effectiveness, and robustness in online, real-time manufacturing automation system.
A Corner Detection Algorithm Based on Regional Center of Mass in Imaging through Water Surface

Abstract Structural light active imaging can obtain more information about the target scene, which is widely used in image registration,3D reconstruction of objects and motion detection. Due to the random fluctuation of water surface and complex underwater environment, the current corner detection algorithm has the problems of false detection and uncertainty. This paper proposes a corner detection algorithm based on the region centroid extraction. Experimental results show that, compared with the traditional detection algorithms, the proposed algorithm can extract the feature point information of the image in real time, which is of great significance to the subsequent image restoration.
Generative Adversarial Network Image Inpainting Based on Dual Discriminators

A generative adversarial network with a double discriminator fusing dilated convolution, and full residual blocks is proposed for existing deep learning image inpainting methods with poor texture after inpainting, unclear pictures, and poor picture semantic consistency. The generator of the model consists of a rough network with skip connections and a refined network, where the skip connections in the rough network fuse the contextual information of the image with higher-level semantic information. To gain a larger perceptual field and deeper semantic information, the rough and refined networks use dilated convolution. At the same time, to avoid the gradients disappearing and convergence being too slow due to the network being too complex, a convolutional layer with four residual structures was used in the refinement network. Experimental results prove that the improved network model in this paper generates more realistic images with greater detailed semantic consistency than the existing network model.
Virtual Reality as Learning Media for Lampung Historical Heritage

Lampung Province has a lot of historical heritage which is a world heritage for future human generations. The Lampung Museum, which was founded in 1975, plays a role in ensuring the continuity of history in the people of Lampung. Dissemination of Lampung history can be improved through education 4.0. Virtual Reality is a technology that allows a user to interact with the environment that exists in the virtual world and can provide information. Virtual Reality can help disseminate the history of Lampung with interactive and humanistic interaction models for historical objects that are presented without being bound by space and time. In addition, the ongoing COVID-19 pandemic has had a major impact on the performance of the Lampung museum in disseminating the history of Lampung. The method used to develop Virtual Reality of Lampung Historical Heritage consists of seven stages, namely Problem Formulation, Determination of Motivation, Idea Development, Design, Prototype Making, Testing, Advanced Development, and Implementation/Release. Eight historical objects were modeled and enriched with information regarding their relationship to Lampung history, namely the Lampung Museum, Thay Hin Bio Vihara, Al-Anwar Mosque, Lampung Siger Tower, Krakatau Monument, Kerti Bhuana Temple, Nuwo Sesat Traditional House, and the Japanese Cave. It is hoped that with the availability of Virtual Reality of Lampung Historical Heritage, the dissemination of Lampung history can be carried out more progressively through education 4.0.
No news is an island: Joint heterogeneous graph network for news classification

News classification is important for people to organize web information. There are many connections between daily news, for example, they may be different reports about the same event or the same person. However, previous works classify news mainly based on single news, they ignore relationships between multiple news. This paper innovatively utilizes relationships between multiple news, such as their relevance in time, place and people, to classify news. To take full advantage of these relationships and integrate various information of multiple news, we propose News Classification Graph (NCG), a heterogeneous graph with different types of nodes and edges. Furthermore, we propose Joint Heterogeneous graph Network (JHN) to properly embed the NCG. It fully utilizes the information of heterogeneous nodes and heterogeneous edges in NCG. Extensive experiments carried on four news datasets demonstrate the effectiveness of our work in solving the news classification problem.
DefQ: Defensive Quantization Against Inference Slow-Down Attack for Edge Computing

The novel multiexit deep neural network (DNN) architectures provide a new optimization solution for efficient model inference in edge systems. Inference of most samples can be completed within the first few layers on an edge device without the need to transmit them to a remote server. This can significantly increase the inference speed and system throughput, which is particularly beneficial to the resource-constrained scenarios. Unfortunately, researchers proposed an inference slow-down attack against this technique, where an external adversary can add imperceptible perturbations on clean samples to invalidate the multiexit mechanism. In this article, we propose a defensive quantization ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DefQ</monospace> ) method as the first defense against the inference slow-down attack. It is designed to be lightweight and can be easily implemented in off-the-shelf camera sensors. Particularly, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DefQ</monospace> introduces a novel quantization operation to preprocess the input images. It is capable of removing the perturbations from the malicious samples and preserving the correct inference exit points and prediction accuracy. Meanwhile, it has little impact on the clean samples. Extensive evaluations show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DefQ</monospace> can effectively defeat the inference slow-down attack and well protect the efficiency of edge systems.
CVAD/An unsupervised image anomaly detector

Detecting out-of-distribution samples for image applications plays an important role in safeguarding the reliability of machine learning model deployment. In this article, we developed a software tool to support our OOD detector CVAD - a self-supervised Cascade Variational autoencoder-based Anomaly Detector , which can be easily applied to various image applications without any assumptions. The corresponding open-source software is published for better public research and tool usage.
Attention virtual adversarial based semi‐supervised question generation

Abstract Question generation (QG) refers to the automatic generation of questions based on the given passages and answers, and has a wide range of application scenarios in human–computer interaction, education, medical and other fields. However, for Chinese QG, due to the lack of word separation in the writing rules of Chinese text, many methods are not suitable for it, and the generated results have incorrect word order and invalid expressions. In addition, traditional models only use labeled data, but it is difficult and expensive to obtain data labels. In order to solve this problem, this article proposes a semi‐supervised QG model termed virtual stroke‐aware copy network (VSAC Net). It is based on virtual adversarial training and can be used for Chinese QG tasks with few labeled samples. The VSAC Net model combines word embedding virtual counter disturbance and attention virtual counter disturbance, the fitting of the input layer and the attention layer is taken into account, and reduces the overfitting of the model. According to Dureader dataset, a small sample QG dataset is constructed, and the VSAC Net is used for solving. The results show that the proposed model can achieve a better generation effect on small sample datasets.
Vectorized Winograd’s algorithm for Convolution Neural networks

Winograd&#x2019;s algorithm has demonstrated its advantages in accelerating the inference of convolution neural networks. It reduces the number of multiplications in convolution and has achieved success on many accelerators, but there is no practical method to map the Winograd algorithm on a vector processor. With the increasing demand for low-latency inference of convolutional neural networks in data centers, finding effective ways to map the Winograd algorithm for small convolutional kernel networks is essential. In this paper, we propose a mapping method for Winograd&#x2019;s algorithm on the vector processors. We use scalars to calculate the filter data and obtain a multi-dimensional vector through replication and expansion. Images are calculated as vectors, which improves the program&#x2019;s parallelism. Then, we build up a three-stage pipeline, including the data read, scalar calculation, and vector calculation. In order to balance the length of the stages, we treat six tiles as a block and calculate them together. The shape of the block can be changed to adapt to different sizes of convolutions. Using output stationary data flow, we temporarily store the intermediate results in the registers to reduce the computational tasks. Finally, we use a double buffering mechanism to reduce the impact of data transmission. Experiments show that the vectorized Winograd algorithm achieves a speedup of 2.2 times compared with direct convolution and the computational efficiency is 44.69%.
A Novel Hybrid Approach for Improving the Accuracy of Recommender Systems Using Weights

Recommender system is a collection of powerful algorithms and techniques which not only help the potential online buyers by offering them to select the products of their choice but also help business organizations and online sites to earn revenues by increasing in sale. Various techniques such as content based filtering, collaborative filtering along with some mathematical calculations work behind the successful operations of such systems. In this paper, a hybrid approach of recommender system is being introduced which integrate clustering method within content-based filter and classification method within collaborative filter. The proposed recommender system model is applied on MovieLens dataset. This method uses weighted approach to increase the performance of the given system. Weights are applied for efficient implementation hence to developed more generalized model using fuzzy CMeans clustering method. This reduced dimension-based dataset is processed by the probabilistic Bayesian network classifier for predicting the ratings. The proposed model is implemented in MATLAB16, and analysis results are calculated with four different experiments. In the end comparative analysis is done with some existing and implemented recommender systems. The significant reduction in MAE rate is identified. The experimentation results show that the proposed model significantly improved the accuracy rate over existing systems.
Impact of Changing Scope and Customer Involvement to Perceived Benefit

This study explores the connection between changing scope and requirement, project team involvement, and benefit management practices to perceived benefit. Using single case study in a regional software delivery company, it uncovers the practices that influence perceived benefit by evaluating scope management, project team involvement and benefit management. The study used survey and focus group to gather information in the case company and frequency analysis to analyze survey result. The study showed that flexibility to changing scope and requirement impacted perceived benefit positively. Additionally, project team involvement, especially from customer side, impacted changes in scope and requirement. This study shows that practices in accepting change in scope and requirement influence project ability to meet expected benefit.
Digital skills, teacher's leadership styles and academic performance in the digital learning context

The main objective of the present study was to know the relationship between teachers digital skills, teachers leadership styles and academic performance of secondary school students; in addition, it was sought to identify the main challenges faced by teachers with a transformational leadership style in the context of virtual classes. The investigation was carried out with a mixed sequential approach; for the quantitative phase there was a sample of 189 high school teachers and for the qualitative phase we worked with 22 teachers. According to the results, A positive relationship was found between teacher's digital skills and student's academic performance. A positive relationship was also found between the teacher's transformational leadership style and the students' academic performance. Additionally, teachers from public educational institutions obtained higher scores in the transformational style than those from private institutions. On the other hand, it was reported that the main challenge identified was in the way of teaching, this changes forced teachers to leave their comfort zone.
Prototyping Electronic Devices on a Dragonfly 3D Printer as a Means of Preserving Original Developments inside the Design Center

The article discusses the currently developing technology of printed electronics. Using the example of the operation of the first in Russia 3D printer DragonFly 2020 LDM, one of the advantages of using printed additive manufacturing inside design centers for the design of electronic devices is shown, as a means of preserving original developments when creating prototypes. The main design and technological characteristics of printed electronics are presented.
Population-Based Hyperparameter Tuning With Multitask Collaboration

Population-based optimization methods are widely used for hyperparameter (HP) tuning for a given specific task. In this work, we propose the population-based hyperparameter tuning with multitask collaboration (PHTMC), which is a general multitask collaborative framework with parallel and sequential phases for population-based HP tuning methods. In the parallel HP tuning phase, a shared population for all tasks is kept and the intertask relatedness is considered to both yield a better generalization ability and avoid data bias to a single task. In the sequential HP tuning phase, a surrogate model is built for each new-added task so that the metainformation from the existing tasks can be extracted and used to help the initialization for the new task. Experimental results show significant improvements in generalization abilities yielded by neural networks trained using the PHTMC and better performances achieved by multitask metalearning. Moreover, a visualization of the solution distribution and the autoencoder's reconstruction of both the PHTMC and a single-task population-based HP tuning method is compared to analyze the property with the multitask collaboration.
Determining the Probability Distribution of Execution Times

In the fields of stochastic scheduling (for cloud workflows) and hard real-time systems, execution times of programs and subroutines are modeled as random variables. Most often, the normal, uniform and exponential families of distributions are used to perform simulation studies. In many other cases, users and practitioners are expected to provide the probability distributions within their workflows, which is not simple. We demonstrate that execution times might have none of the aforementioned distributions, and discuss general features of programs that might lead to normality. In particular, a general central limit theorem can raise a priori evidence that a certain program will display normality, depending on its control flow graph. We analyze a particular program and perform a simulation study to give further insight into this phenomenon. We expect this to ease usage of methods proposed in the literature, and be used as basis for stating that normality can be expected.
A NFT verification method for distributed AI system

Abstract This paper proposes a method invention, namely an efficient NFT data inspection method with minimum granularity and probability comparison. The invention establishes a fast comparison method of AI model and data, that is, the direct comparison of small files priority and the maximum-minimum interval comparison. The invention takes the substantial identity inside the NFT data and the processing method of NFT data coincidence into account, so that the data content outside the token of the NFT publicly shared by the AI distributed system can also be unique on the Internet. Therefore, it can avoid the problem of incremental packaging and repeated packaging, and can successfully balance the efficiency and security of the comparison process. portions given in this document
Reducing the Estimation Error of the Measure of Proximity Between Objects in Pattern Recognition

When recognizing similar or close objects, the recognition reliability becomes very low, since the measure of proximity between objects is close to the value of the resulting error in the measurement of features. The article proposes an algorithm for solving this problem. In this algorithm, the parameters of a standard object are subjected to a large number of measurements in the learning mode. The parameter of the investigated object is measured by such a number that does not affect the speed of the system. During recognition, each measured parameter is compared with all measured values of the standard parameter. Thus, the number of repeated measurements is rather artificially increased. Since the comparison process is performed on the computer programmatically, this does not affect the speed of the system. This algorithm has been simulated on a computer and positive results have been obtained. The processing of the results showed that the proposed algorithm can significantly increase the recognition reliability.