Reviewerly/exHarmony huggingface dataset:
Development of Algorithms for Medical Image Compression

Image compression algorithms are developed mainly for reduction of storage space, easier transmission, and reception. In this chapter, many image compression algorithms have been developed based on various combinations of transforms and encoding techniques. This research work mainly deals with the selection of optimum compression algorithms, suitable for medical images, based on the performance indices like PSNR and compression ratio. In order to find the effectiveness of the developed algorithms, characterization of the CT lung images are performed, before and after compression. The diagnosis of lung cancer is an important application for various medical imaging techniques. In this work, optimal texture features are identified for classification of lung cancer have also been incorporated as a case study. The texture features are extracted from the in CT lung images. BPN is trained to classify the features into normal and cancer.
A Smart Homes through Natural Language Processing using Internet of Things

The IoT is mainly regarded as the key innovation that allows us to utilize online services for integrating so several devices, assistance in having received the guidance, and empower in continuing to act within it in an efficient manner. So several devices are linked to the web that also enable in aiding the participants to utilize the device pretty nearly. This research is concentrated on identifying the critical factors that will help build smart homes by means of The internet of Things and language processing. The two main factors taken into consideration are voice understanding integration with devices and remote arduino microcontroller functionality.Secondary and primary resources are utilized to gather data for the research, and a five - point likert evaluation is applied to quantitatively apply that data. For the purpose of interpreting the route diagram using the structural equations model, the authors use sophisticated data analysis methods that analyse moment structures.
Deep Game Location Acquisition for <i>Pokemon GO</i> on Mobile Devices

Advanced augmented reality (AR) games such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pokemon Go</i> attract millions and millions of players because they integrate various sensors to impress excellent user experiences on mobile devices. In this article, for energy saving on the mobile devices, we can use an accelerometer and a magnetometer to estimate the current user's location approaching interest of points on a game map without active GPS sensors and wireless network. In addition, we create an effective data structure with R-tree and adopt deep learning to efficiently answer the location query of Pokemons on the game location database. Our algorithm outperforms previous approaches in not only smaller running time but also in the better power consumption for the AR games on mobile devices.
A Structure-Aware Method for Cross-domain Text Classification

Cross-domain text classification utilizes the labeled source domain to train a well-performing classifier for the unlabeled target domain. It is an important task in natural language processing. Owing to the outstanding performance for representations learning, deep learning models are introduced into cross-domain classification to learn domain invariant representations. However, existing methods assume that features in text are independent, ignoring the correlation between features. To this end, we propose a structure-aware method for cross-domain text classification, which uses both the feature semantics and the structure among features to learn the invariant representations. Firstly, a knowledge graph is introduced as an additional resource to capture the structure among features, and the text in both domains is mapped into two sub-graphs. Then, the invariant structure representations between two sub-graphs are learned based on Graph Attention Network (GAT) and correlation alignment. Lastly, the invariant structure representations and the invariant feature representations are combined together to learn higher-level invariant representations for cross-domain classification. Extensive experiments demonstrate that our method achieves better classification accuracy compared with state-of-the-art methods.
Centralized Cloud Predictive Control of Multiple Lighting Systems

This paper studies the application of centralized cloud predictive control based on dSPACE system in multiple lighting system control. A centralized cloud predictive control scheme is designed to achieve the consistency and stability of multiple lighting control at the same time. The dSPACE multiple lighting control system is corrected and modeled. The design of cloud prediction controller based on dSPACE system is introduced in detail. The cloud predictive control scheme is analyzed through simulation experiments, and the effective parameters of stability and consistency of closed-loop multiple lighting control system are given. The effectiveness of the proposed scheme on the dynamic behavior and control performance of multiple lighting system is verified. The research results provide a foundation for multiple lighting cooperative control and its application. Finally, the stability and consistency of the controller are verified by the physical experiment platform based on dSPACE.
Interior-Point Filter Line-Search

In this chapter, a combination called IPOPT of the interior-point algorithm with the filter line-search for solving large-scale nonlinear optimization problems, proposed by Wächter and Biegler (2005a, b), is presented. As we know, to allow convergence from poor starting points and to enforce progress to the solution, the interior point methods both in the trust-region and in the line-search frameworks with exact penalty merit function were developed. For example, KNITRO uses the l1 exact penalty function (Byrd, Gilbert, & Nocedal, 2000; Byrd, Hribar, & Nocedal, 1999).
PAN RAM Bootstrapping Regressor - A New RAM-Based Architecture for Regression Problems

RAM-based neural networks have been used a few decades before MultiLayer Perceptrons. Despite their implementability in hardware and speed, they have some drawbacks compared to more recent techniques, particularly related to continuous input variables, which kept them out of mainstream research. About a decade ago, the PAN RAM was an attempt to handle continuous inputs with a polynomial approximator neuron in the n-tuple architecture applied to binary decision problems. Constraints on applications and data preparation still remained. This paper presents an evolution of the PAN RAM neuron that can do regression and a single-layer architecture that is dynamically built based on bootstrapping. The proposed system was benchmarked against the Multilayer Perceptron on three regression problems using the Abalone, White Wine, and California Housing datasets from the UCI repository. In the unicaudal paired t-test carried out on a 10-fold cross-validation comparison measuring the Mean Absolute Error (MAE), the proposed system performed better than the Multilayer Perceptron (MLP) on the abalone and white wine datasets. In contrast, for the California dataset there was no significant improvement, all at a 0.05 significance level.
On the SCA Resistance of Crypto IP Cores

With the growth of system complexity, the use of previously designed components, IP cores, has become a usual practice in hardware design. Nowadays, many companies offer a broad range of IP solutions available for use in ASICs as well as FPGA designs. In this paper we concentrate on the use of soft crypto IP cores, i.e. the cores designed to address security issues. The intention is to raise the awareness of the designers about the possible vulnerabilities that may be acquired with such pre-designed IP blocks if no additional security evaluation was performed. We show on the example of an internally-developed cryptographic core, how the security strength of the design depends on the applied semiconductor manufacturing process and the logic gate libraries.
Online Sequential Extreme learning machine-based hybrid model for forecasting of COVID-19 cases in India

COVID-19 has posed high stress on government and people with its disruptive effects on every sector of the nation. Accurate and reliable forecasting models are of great need to handle this unprecedented situation. A hybrid model, which is a combination of, cuckoo search optimization algorithm, variational mode decomposition and online sequential extreme learning machine has been proposed in this work for multistep forecasting of COVID-19 cases. The model showed reasonable accuracy of 1.363%, 1.596% and 1.933% for one, three and five days ahead forecasting. The model gave superior results when compared with partial autocorrelation function (PACF) for selection of number of input parameters. The robustness of the proposed model has been evident in comparison with other similar state of the art techniques discussed in the literature.
Using Complexity Metrics with Hotspot Analysis to Support Software Sustainability

Software sustainability is critical for Computational Science and Engineering (CSE) software. Measuring sustainability is challenging because sustainability consists of many attributes. One factor that impacts software sustainability is the complexity of the source code. This paper introduces an approach for utilizing complexity data, with a focus on hotspots of and changes in complexity, to assist developers in performing code reviews and inform project teams about longer-term changes in sustainability and maintainability from the perspective of cyclomatic complexity. We present an analysis of data associated with four real-world pull requests to demonstrate how the metrics may help guide and inform the code review process and how the data can be used to measure changes in complexity over time.
Group Residual Dense Block for Key-Point Detector with One-Level Feature

In this paper, we propose a novel key-point detector with only one-level feature with the stride of 8, which is 75.0% less than methods with the stride of 4. Due to the reduction of the feature layers, firstly we adopt a new key-point labeling method, which can make full use of the detection points on the feature map. Secondly, we propose a U-shaped feature fusion module with group residual dense blocks, which works together with grouped convolutional and re-parameterization methods to bring significant improvements while reducing parameters. Thirdly, we use a soft non-key-point branch to re-weight the classification score. Using NVIDIA GeForce 3060 GPU and based on the VOC dataset, the proposed model with RepVGG-A0 runs about 51.4% faster than CenterNet with ResNet-18, runs 261.3% faster, and achieves higher accuracy than CenterNet with ResNet-101 under the resolution of $$512\times 512$$ .
Unsupervised Feature Selection via Adaptive Feature Clustering for High-dimensional Data

The purpose of feature selection is to obtain a feature subset from the original data to realize dimension reduction, and at the same time, improve the performance of following machine learning task. Due to the lack of label information, unsupervised feature selection is more challenging. In this paper, the idea of sample clustering is applied to feature space. Clustering the features makes the similarity between features in the same cluster high and that from different clusters low. Subsequently, representative features are selected from each cluster to form the final feature subset. Along this way, an unsupervised feature selection algorithm which considers both the similarity and relevance of different features is proposed. Experiments in comparison with both traditional and novel algorithms on benchmark data sets are performed. The results show the superiority of the proposed method.
Electrical Insulator Surface Condition Analysis Based on Joint Fully Convolutional and Multiscale Spatial Pooling Attention Network

Health condition monitoring of insulators is one of the key factors to ensure a stable power supply. This article proposes an automated insulator surface condition analysis approach based on vision measurement and the convolutional neural network (CNN). Specifically, we consider the condition analysis of insulators as an image multiclassification problem. An insulator image is first fed into a feature extractor, implemented by leveraging CNN, to extract features automatically. Then, a softmax classifier is utilized to map the feature representation into a probability vector to obtain the analysis results. Considering that neither each layer nor each location of features has the same impact on classification performance, we further focus on feature relationships and present a novel attention architectural unit, called the joint fully convolutional and multiscale spatial pooling attention (JFCMSPA) module. This new attention module not only considers the friendly fusion of different pooled information but also introduces the local similarity property of images into visual attention for the first time. These new insights allow it to efficiently learn feature importance. Finally, experimental results demonstrate the effectiveness of the proposed method and the value of its application in the inspection of power equipment.
Diverse Machine Translation with Translation Memory

The challenge of diverse machine translation is to ensure both diversity and quality, in which diversity desires the distinction between multiple hypotheses in the syntactic or word level, while quality requires hypotheses to be consistent with certain references. Existing work on diverse machine translation, most of which boosts translation diversity but compromises trans-lation quality, either resorts to special search strategies to realize word-level diversity or trains several decoders simultaneously to enable possible syntactic multiplicity. In this work, we propose to improve the translation diversity without a severe drop in quality through constructing a novel translation memory based NMT and designing a global diverse beam search strategy. Specifically, the translation memory retrieved from target-size corpus act as experts to interact with standard NMT, which not only generates various hypotheses, but also enhances the quality to a great degree. Moreover, we exploit a novel diverse beam search to further avoid token reuse across different hypotheses and improve local diversity. Experiments on two benchmarks (JRC-Acquis and WMT) demonstrate that our approaches achieve a compelling promotion both of translation quality and diversity compared with other diverse approaches.
Study on Flow Regime Classification of Gas-Liquid Two-Phase Flow with Bidirectional LSTM Networks

Cryogenic fluids such as liquid hydrogen and liquid oxygen used as the rocket propellants easily evaporate and form the gas-liquid two-phase flow. The control of the two-phase flow is difficult due to the large fluctuation of the density. For high-precision control, it is necessary simultaneously to understand flow regimes which represent a gas-liquid distribution pattern. Therefore, in this paper, we developed a classifier that uses Bidirectional LSTM networks, which is part of a family of deep learning methods, with a measured value of a void fraction meter as input and gas-liquid flow rate conditions as output, in order to realize a flow regime classifier in the future. The classifier succeeded in classifying with more than 80% accuracy. In addition, in order to verify what features of the input data the classifier captures, a test data-set of which frequency was artificially changed was classified. As a result, it was confirmed that the classifier would use the frequency component of the input data as one of the basis for classification.
Toward Systematic Considerations of Missingness in Visual Analytics

Data-driven decision making has been a common task in today's big data era, from simple choices such as finding a fast way to drive home, to complex decisions on medical treatment. It is often supported by visual analytics. For various reasons (e.g., system failure, interrupted network, intentional information hiding, or bias), visual analytics for sensemaking of data involves missingness (e.g., data loss and incomplete analysis), which impacts human decisions. For example, missing data can cost a business millions of dollars, and failing to recognize key evidence can put an innocent person in jail. Being aware of missingness is critical to avoid such catastrophes. To fulfill this, as an initial step, we consider missingness in visual analytics from two aspects: data-centric and human-centric. The former emphasizes missingness in three data-related categories: data composition, data relationship, and data usage. The latter focuses on the human-perceived missingness at three levels: observed-level, inferred-level, and ignored-level. Based on them, we discuss possible roles of visualizations for handling missingness, and conclude our discussion with future research opportunities.
Fine Grained Few-Shot Classification with Contrastive Clues

Few-shot learning (FSL) attempts to alleviate poor recognition for less represented classes. The meta-learning stage for knowledge transfer consists of a series of training episodes that mimic the few-shot setting. In this case, randomly sampling a few examples per class for a myriad of episodes, algorithms on a global level tend to use a large number of examples per class. Thus, the existing state-of-the-art may not be extendible to practical actualization wherein a few examples per class is available. Invariably there exists a trade-off between an algorithm's ability to adapt to the intra-class variance and inter-class similarity in fine-grained class recognition. Attending to these impediments we propose a method that leverages matching based similarity measures for finegrained few-shot classification through contrastive verification. Discriminative representation learning (DRL), semantic similarity identification (SSI), and template matching (TM) are the three functional modules of the proposed approach.
Cloud computing Technology in Academic Libraries: A paradigm shift

Cloud computing has presented to us another point of view to take a gander at the current asset – sharing issue, cloud computing can be applied to advanced library assets to further develop data sharing abilities, further develop asset use. This paper portrays how cloud computing assists with accomplishing more effective utilization of IT equipment and programming ventures and give a way to speed up the appropriation of advancement. The study gives an overview of cloud computing and how it might be combined with library administrations in the online environment.
Knowledge-Aware Topological Networks for Recommendation

Knowledge graphs (KGs) play a critical role in recommender systems, aiming to provide diverse, accurate, and explainable recommendations to users. Enhanced with KGs, recommender systems are able to leverage valuable auxiliary information, which is beneficial to predict new user-item interactions. Specifically, the connectivity between relations and entities in a KG can reveal the structural and semantic information, as well as help to provide inferences for user choices. However, the information of the holistic topological structure in KGs has not been fully taken into account in most existing studies. To this end, we propose the Knowledge-aware Topological Recurrent Network (KTRN), an end-to-end network for recommendation with recurrent neural network and knowledge graph embedding. To simultaneously discover sequential dependencies and semantic information in a KG, we consider both relevant paths and triplets. Moreover, we focus on the importance of relation-entity pairs in learning representations, rather than treating relations and entities as independent units. We conduct experiments on three public datasets about movie, book, and music recommendation scenarios, and extensive experimental results show that our method outperforms benchmark approaches.
Research on Digital Music Teaching System Based on “Introduction and Fusion”

This paper puts forward the curriculum design and extension teaching concept of “introduction and fusion”. On the basis of the traditional teacher-apprentice model of music performance courses, ahypercycle model of “before class” and “after class” auxiliary teaching is established, and classroom teaching is established in the environment of knowing teaching from teaching and learning from learning. The digital teaching system constructed in this paper has the characteristics of diversified and personalized self-organization. Through the self-organization process of “before class”, “in class” and “after class”, in order to reduce the uncertainty of the teaching system and enhance the autonomy of students Provides teaching modes and methods with practical wisdom.
Research on Database Disaster Recovery and Architecture Design of Power Marketing System

In recent years, with the accelerated pace of digital transformation of electric power enterprises, we have put forward new tasks and goals for the construction of marketing informatization, in order to conform to the new situation and new demand of traditional electric power marketing business upgrading. Based on the cloud platform architecture, this paper studies and designs database disaster recovery for the upgrading and iterative construction of power marketing system. It avoids unexpected data loss and system downtime when disasters occur.
Quo Vadis Computer Science? The topics of the influential papers during the period 2014-2021

In this study we analyze the titles of all influential publications in Computer Science, during the period of 2014-2021, indexed by Scopus, using an innovative graph-partitioning based topic modelling approach. We consider as influential publications the top 1% cited papers per year. The findings of this specific study are interesting per se, showing the dominance of recent advances in deep learning and neural networks, but our main contribution is the methodology we followed which can be easily applied to any other field and with different types of paper information such as abstracts, keywords, etc. Our approach involves the creation of a bipartite graph of paper ids and their title keywords and the projection on the keywords to find and visually present the identified topics.
Less is More: Bitcoin Volatility Forecast Using Feature Selection and Deep Learning Models

Utilizing a large set of variables that include transaction information, public attention, blockchain information, macroeconomic variables and technical indicators, we compare different deep learning models with baseline methods, such as statistical and machine learning models, on Bitcoin volatility forecast. We find that feature selection approach strongly affects model performance. The results show that a simple Long Short-Term Memory (LSTM) model outperforms other models when using individual feature selection method.
Profiling Cyber Crimes from News Portals Using Web Scraping

In the past few years and especially during the pandemic period everything is going online. Everyone is connected through the Internet. With this rapid surge, cyber crimes are also skyrocketing. The government has made efforts to cope with situation but that is not enough. India faced a loss of more than Rs. 1.25 trillion ($16 billion USD) due to cyber crimes. Online news are a reliable source of information which are always up to date and freely available. In this paper, we conduct a survey of web scraping related previous works. Web scraping is a method to gather information from websites. With this knowledge we have proposed a system of web scraping where we gather cyber crime related news articles. From this data we can classify the crimes into their respective categories according to their regions and time period. This can help law enforcement and also create awareness amongst common people.