Reviewerly/exHarmony huggingface dataset:
Automatic modulation classification with deep learning‐based frequency selection filters

Automatic modulation classification (AMC) is an important and challenging task that aims to discriminate modulation formats of received signals, such as military communications, cognitive radio and spectrum management. With the development of deep learning techniques, research in AMC has gained promising results because of its powerful representation and classification abilities. In this Letter, the authors present a new network architecture that combines a frequency selection module and a convolutional neural network (CNN). This scheme not only processes raw signal data with carriers to increase the in-band signal-to-noise ratio but also converge faster than traditional CNN. Experiments demonstrate the effectiveness and efficiency of the proposed model.
SimTab

SimRank is a classic measure of vertex-pair similarity according to the structure of graphs. Top- k and thresholding SimRank queries are two important types of similarity search with numerous applications in web mining, social network analysis, spam detection, etc. However, extensive studies for SimRank most focus on single-pair and single-source queries and fail to provide any feasible solution for the top- k and thresholding queries, e.g., with theoretical accuracy guarantee or acceptable empirical performance. In this paper, we propose SimTab (&lt;u&gt;Sim&lt;/u&gt;Rank queries with &lt;u&gt;T&lt;/u&gt;ighter confidence bounds and multi-&lt;u&gt;a&lt;/u&gt;rmed &lt;u&gt;b&lt;/u&gt;andits) to answer top- k and thresholding queries in a unified manner. First, we integrate several techniques with random walk sampling to tighten the confidence bound of SimRank estimation, which enhances the query efficiency. Second, we answer top- k and thresholding queries from the perspective of the Multi-Armed Bandits (MAB) problems. The proposed algorithms significantly improve the theoretical efficiency over state of the art, whereas the algorithmic complexity closely matches the hardness of the problem. We further propose a novel sampling strategy specially tailored for node similarity queries, which improves both the theoretical and practical query efficiency of the MAB-based algorithms. Our method is the first with query accuracy guarantee for these two queries, and the sole algorithm to achieve high-quality query results on large graphs. Moreover, all proposed algorithms are index-free, and thus can be naturally applied to dynamic graphs. Extensive experiments on several large graph datasets demonstrate that our algorithms achieve much superior effectiveness with comparable or less query time cost than all index-free and index-based state of the art. Besides, our work proposes the first thorough empirical evaluation of the existing SimRank algorithms over top- k and thresholding queries.
Generalized finite mixture of multivariate regressions with applications to therapeutic biomarker identification

Finite mixtures of regressions have been used to analyze data that come from a heterogeneous population. When more than one response is observed, accommodating a multivariate response can be useful. In this article, we go a step further and introduce a multivariate extension that includes a latent overlapping cluster indicator variable that allows for potential overdispersion. A generalized mixture of multivariate regressions in connection with the proposed model and a new EM algorithm for fitting are provided. In addition, we allow for high‐dimensional predictors via shrinkage estimation. This model proves particularly useful in the analysis of complex data like the search for cancer therapeutic biomarkers. We demonstrate this using the genomics of drug sensitivity in cancer resource.
From "VKontakte" to Coursera: digital services in the educational system of a modern university

This work explains the relevance of digitalization of the educational process in general and the use of digital services in the educational process is determined. The features of the transition of universities to the distance in the context of the coronavirus pandemic are taken into account. The groups of digital services used in the distant environment are identified, taking into account the experience of universities. The features of services by groups are considered, the results of an analysis based on the author's experience of using services in organizing the educational process in a remote format are presented.
Estimation of Global Solar Radiation using Back Propagation Neural Network: A case study Tripoli, Libya

Adequate information on global solar radiation with relevant meteorological parameters at any location is necessary for planning, designing, and prediction of the efficiency and performance of solar energy applications. Measurements of global solar radiation in developing countries are very difficult and not readily available because of the cost of the equipment and their maintenance. Libya as one of the developing countries is facing challenges in global solar radiation measurements and recording. This study employed the application of Back Propagation Artificial Neural Network (BPNN) for the estimation of global solar radiation at Tripoli, Libya. For this reason, meteorological data for the period of January 1995 to December 2010 for important cities of Libya (Tripoli) is collected from Libyan National Meteorological Center Climate and Climate Change. The data consists of the monthly average sunshine hours, rainfall, max. temperature, wind speed, mean evaporation, and relative-humidity. Sensitivity analysis was employed and three different model combinations (M1, M2, and M3) were carried out for the model development. The performance efficiency of the models was evaluated. The obtained results show that BPNN has been proved satisfactory in all the analyses and the analysis of the results indicated that BPNN-M3 has the highest accuracy and reliability.
Simulated physical ageing: A prioritized persona-based model for accessible interiors in senior housing environments

Accessibility is a critical interior design consideration that increases performance level and allows older adults to be independent and physically active in their daily activities. This study used a case study to present a new method of combining ageing simulation with personas through importance-performance analysis (IPA) and supporting the basic activities of daily living (BADL). This study developed a prioritized persona-based (PP-B) model to create accessible interiors in senior housing environments for healthy ageing. This model was constructed based on an ontology framework. The data that was gathered through the self-assessment accessibility questionnaire by 60 older adults and it was depicted in the IPA matrix, which later translated into personas. These personas were simulated with an ageing suit by interior architecture students based on the BADL of accessible senior housing environments. This study was an initial attempt to deal with the complex nature of accessible interior design and its attributes for ageing studies, which are often considered as theoretical concepts and standards. The main innovation of this developed PP-B model was to synchronize interior design knowledge on accessibility attributes and users’ BADL performance along their accessibility importance rankings. Findings are beneficial for interior designers to make human-centred interior design decisions.
A Survey on Restoration of Paintings

Images in the true world are subjected to numerous forms of modification during its capture, acquisition, storage, transmission and reproduction. Images are everywhere in our daily life, not only because they are extensively used as a channel for communication but also they are one of the best way to represent the physical world. Digital image is a numeric description, usually binary representation of a two dimensional image. Digital Image Processing is the process which uses computer algorithms to process digital image. One of the most interesting areas in image processing is to enhance the pictorial information for human interpretation. The main aim of image restoration is to reconstruct the original image from a deteriorated image. A lot of researches are being carried out on image restoration. This paper mainly focuses on different techniques used for digital restoration of paintings and various parameters used for evaluation.
Toward Serverless and Efficient Encrypted Deduplication in Mobile Cloud Computing Environments

With the proliferation of new mobile devices, mobile cloud computing technology has emerged to provide rich computing and storage functions for mobile users. The explosive growth of mobile data has led to an increased demand for solutions that conserve storage resources. Data deduplication is a promising technique that eliminates data redundancy for storage. For mobile cloud storage services, enabling the deduplication of encrypted data is of vital importance to reduce costs and preserve data confidentiality. However, recently proposed solutions for encrypted deduplication lack the desired level of security and efficiency. In this paper, we propose a novel scheme for serverless efficient encrypted deduplication (SEED) in mobile cloud computing environments. Without the aid of additional servers, SEED ensures confidentiality, data integrity, and collusion resistance for outsourced data. The absence of dedicated servers increases the effectiveness of SEED for mobile cloud storage services, in which user mobility is essential. In addition, noninteractive file encryption with the support of lazy encryption greatly reduces latency in the file-upload process. The proposed indexing structure (D-tree) supports the deduplication algorithm and thus makes SEED much more efficient and scalable. Security and performance analyses prove the efficiency and effectiveness of SEED for mobile cloud storage services.
An introductory illustration of medical image analysis

The medical imaging field has evolved into an enormous scientific discipline since the last decade of the 19th century. The analysis of medical data obtained by current image modalities such as positron emission tomography, magnetic resonance imaging, computed tomography, and ultrasound comes to the aid of the fruitful diagnosis, appropriate planning, and assessment of therapy for patients’ treatment and much more. Medical image analysis is crucial to grip this huge amount of data and to investigate and present the appropriate information for any particular medical task. In this chapter, different aspects with regard to medical image analysis are exhaustively explored. In particular, issues and challenges in connection with this task are investigated and described. In addition, a brief summary of the contributory chapters is presented to trace the challenges and findings of each.
A Signal Modulation Identification Method Based on Neural Network

Signal modulation identification technology is an important research area in communication systems. With the development of communication technology, the modulation methods of signals are becoming more and more complex. In order to identify the complex modulation methods of signals, the article introduces deep learning technology to the field of modulation identification. A more effective automatic modulation recognition method is presented. This paper builds a CNN_LSTM recognition model, that is, a deep learning network structure connected to a long-term and short-term memory network as a classifier. The model uses the convolution neural network to extract the characteristics of the signal space, and then uses the LSTM network to extract the temporal correlation of the extracted signals. The experimental results show that this method can simultaneously identify 12 signal modulation methods, and the signal-to-noise ratio is more than 3db. The overall recognition accuracy rate is more than 90%, which is significantly improved compared with the traditional method.
An Ensemble Classification Algorithm for Imbalanced Text Data Streams

For unbalanced text data streams, an ensemble classification algorithm for unbalanced text data streams is proposed in this paper. Firstly, an improved resampling method is used to establish balanced data subsets; secondly, the topic model is used to perform topic modeling on the balanced data subsets to establish document-topic training subsets; finally, an ensemble classifier is constructed using the WE ensemble model. The algorithm sets the difference of the F-value of the neighboring data blocks to a certain threshold as the standard of updating the classifier. When the ensemble classifier is updated, the base classifier is retrained after the error positive instance is added to error set. Experimental results show that the proposed algorithm not only has good classification performance for the positive instances, but also has good classification performance for all instances. Therefore, the algorithm proposed in this paper is an effective classification algorithm for unbalanced text data streams.
Knowledge Graph Construction of Personal Relationships

Knowledge graph has attracted much attention in recent years. It is a high-level natural language processing (NLP) problem, which includes many NLP tasks such as named entity recognition, relation extraction, entity alignment, etc. In this paper, we focus on the entity of persons in the large amount of text data, and then construct the graph of personal relationships. Firstly we investigate how to recognize person names from Chinese text. Secondly, we propose a comprehensive approach including Improved BiGated Recurrent Unit and syntactic analysis to extract the relations between different persons. Then, we align the person entities through entity alignment techniques and some manual proofreading work. Finally, we apply this graph construction process in text records for experimentation. This process performs effectively and efficiently to construct the graph of personal relationships from unstructured Chinese text, and this graph can provide significant relationship insights in texts.
Point-Based Registration for Multi-stained Histology Images

Image registration is a basic task in biological image processing. Different stained histology images contain different clinical information, which could assist pathologists to diagnose a certain disease. It is necessary to improve the accuracy of image registration. In this paper, we present a robust registration method that consists of three steps: 1) extracting match points; 2) a pre-alignment consisting of a rigid transformation and an affine transformation on the coarse level; 3) an accurate non-rigid registration optimized by the extracted points. The existing methods use the features of the image pair to initial alignment. We proposed a new metric for the non-rigid transformation which adding the part of optimizing extracting points into the original metric. We evaluate our method on the dataset from the ANHIR Registration Challenge and use MrTRE (median relative target registration error) to measure the performance on the training data. The test result illustrates that the presented method is accurate and robust.
Topology-aware routing of 3D-printed circuits

Hybrid manufacturing of 3D-printed parts with integrated electronics has made significant progress in recent years. The general idea of depositing conductive material and electronic components during or after the additive manufacturing step was successfully demonstrated for different process types. However, efficient 3D arrangement and wire routing of circuits for seamless integration into the printing process requires the support of appropriate design software. In this paper, we introduce an approach to integrate the wire routing into the slicing software for Fused Filament Fabrication (FFF). This allows us to consider process parameters for each specific print, e.g. extrusion width, layer thickness or shell thickness. The volumetric object model is first converted into a graph data structure which is then used to apply a routing algorithm. The resulting G-code with embedded wires can be directly executed by the printer. The G-code also includes instructions for a pick and place system to automatically produce the entire object, including electronic components, without operator intervention.
Scalable Kernel Ordinal Regression via Doubly Stochastic Gradients

Ordinal regression (OR) is one of the most important machine learning tasks. The kernel method is a major technique to achieve nonlinear OR. However, traditional kernel OR solvers are inefficient due to increased complexity introduced by multiple ordinal thresholds as well as the cost of kernel computation. Doubly stochastic gradient (DSG) is a very efficient and scalable kernel learning algorithm that combines random feature approximation with stochastic functional optimization. However, the theory and algorithm of DSG can only support optimization tasks within the unique reproducing kernel Hilbert space (RKHS), which is not suitable for OR problems where the multiple ordinal thresholds usually lead to multiple RKHSs. To address this problem, we construct a kernel whose RKHS can contain the decision function with multiple thresholds. Based on this new kernel, we further propose a novel DSG-like algorithm, DSGOR. In each iteration of DSGOR, we update the decision functional as well as the function bias with appropriately set learning rates for each. Our theoretic analysis shows that DSGOR can achieve O(1/t) convergence rate, which is as good as DSG, even though dealing with a much harder problem. Extensive experimental results demonstrate that our algorithm is much more efficient than traditional kernel OR solvers, especially on large-scale problems.
Segments-Based 3D ConvNet for Action Recognition

Abstract Learning to capture both long-range and short-range temporal information is crucial for action recognition task. Previous works utilize 3D ConvNets to capture short-range temporal dynamics in replacement of optical-flow which needs time-consuming extraction. However, dramatically incresed parameters limit the capacity for modeling long-term interactions. In this paper, we propose Segments-based 3D ConvNet (S3D) to integrate both long-term and short-term temporal dynamics. Firstly, we utilize 3D ResNet without temporal downsampling to capture short-range video contents. Secondly, we integrate a sparse sampling strategy to model long-range temporal structure. Finally, experiments on UCF-101 and HMDB-51 datasets show the effectiveness of our S3D compared with corresponding 3D ConvNet.
BSOGCN: Brain Storm Optimization Graph Convolutional Networks Based Heterogeneous Information Networks Embedding

Recently, Graph Convolutional Networks (GCNs) have shown great potential in the field of graph embedding. They map the nodes of the graph into the low dimensional vectors by aggregating the neighbor nodes' features information. However, most existing GCNs only focus on the homogeneous information networks instead of the heterogeneous information networks (HINs) with multiple types of nodes which are more common in the real world. Because the different types of neighbor nodes could have different impacts on the target nodes, it is difficult to manually design a proper neighbor nodes' features information aggregating weights. To address this problem, we propose a novel HINs embedding algorithm based on the brain storm optimization (BSO) algorithm and the graph convolutional network (GCN), called BSOGCN, which utilizes BSO to optimize the neighbor nodes' features information aggregating weights. It can be applied to the various HINs under various scenarios without any prior knowledge. The proposed method has been evaluated on both inductive and transductive node multi-class classification tasks on three real-world HINs datasets. The experimental results demonstrate that BSOGCN is competitive against other state-of-the-art methods.
Vilokana - Lightweight COVID19 Document Analysis

An increase in the scientific literature related to COVID19 makes searching for scientific information a challenging task. In this paper, we present the implementation of a semantic search engine targeted at COVID19 research articles. The algorithm uses a modified Term Frequency-Inverse Document Frequency (TFIDF) features and cosine similarity with ontology maps for semantic search. The implementation includes sentiment analysis, keyword extraction, keyword-based search, phrase extraction, textual belief indication, and text summary. The system is lightweight and can be deployed as a standalone system on mobile devices. The analysis reported in the work is based on data from 50k research articles related to COVID19 research. The tool also assists the researchers to upload their own text or documents for performing analysis. The comparisons with state of the art algorithms such as BERT and Word2Vec indicate improved performance.
Multi-formation track initiation method based on Density clustering

Abstract Aiming at the problem that the existing track initiation algorithm has a poor effect when starting multiple formation tracks under strong clutter, based on the Hough transform method and its derivative algorithm, a multi-formation Hough transform route based on density clustering is proposed. Trace start algorithm. The algorithm first combines the motion information of the target and the timing parameters of the detection points to filter the detection data set to exclude as many points as possible from non-targets. Then it uses the Hough transform to process the obtained detection point set to obtain a preliminary threshold with a lower threshold Formation track; Finally, according to the characteristics of formation goals, the overall formation track is obtained by density clustering method, which solves the problems of track crossing and chaos. Simulation results show that, compared with the standard Hough transform method and its derivative algorithm, the algorithm can start the track of formation targets under strong clutter, and has good performance.
Exploiting Stopping Time to Evaluate Accumulated Relevance

Evaluation measures are more or less explicitly based on user models which abstract how users interact with a ranked result list and how they accumulate utility from it. However, traditional measures typically come with a hard-coded user model which can be, at best, parametrized. Moreover, they take a deterministic approach which leads to assign a precise score to a system run.
Verification of Power-based Side-channel Leakage through Simulation

Side-channel resistant design is becoming a common requirement in IC design for connected systems and Internet of Things. In contemporary design practice, power-based side-channel leakage is validated on a fabricated chip by an independent security testing team. This practice is expensive. There is a need for a design methodology and an associated design flow to perform validation of power-based side-channel leakage during the design flow and before chip fabrication. We describe the technical requirements for this approach, and we summarize the state of the art. We then describe the side-channel leakage verification in a System-on-Chip, and we compare simulations with measurements from a fabricated chip of the same design.
NP-hardness and evolutionary algorithm over new formulation for a Target Set Selection problem

This work considers the Target Set Selection problem, which can be used to model the propagation and consumption of information, data, ideas and products through networks, with applications in marketing, medicine, sociology and bioinformatics. We propose a new version of the problem and prove it belongs to the NP-hard class. We also design an evolutionary algorithm that uses, in the crossover and mutation operators, exact solutions of sub-problems which were modeled by a new mathematical formulation. We test our approach over a benchmark of instances constructed from real-world data sets.