Reviewerly/exHarmony huggingface dataset:
Eight-Band Orthogonal Wavelets for Fast Calculation

The symmetry or anti-symmetry 8-bank orthogonal wavelet filters for fast calculation with high order vanishing moment and short support are constructed by using the ingenious structure. The low-pass filter can determine seven high-pass filters by changing the sign and position in this wavelet system. This construction brings a lot of convenience for our calculation, which can be applied to the engineering background for using the free degrees.
SCARF: a biomedical association rule finding webserver

The analysis of enormous datasets with missing data entries is a standard task in biological and medical data processing. Large-scale, multi-institution clinical studies are the typical examples of such datasets. These sets make possible the search for multi-parametric relations since from the plenty of the data one is likely to find a satisfying number of subjects with the required parameter ensembles. Specifically, finding combinatorial biomarkers for some given condition also needs a very large dataset to analyze. For fast and automatic multi-parametric relation discovery association-rule finding tools are used for more than two decades in the data-mining community. Here we present the SCARF webserver for generalized association rule mining. Association rules are of the form: a AND b AND … AND x → y, meaning that the presence of properties a AND b AND … AND x implies property y; our algorithm finds generalized association rules, since it also finds logical disjunctions (i.e., ORs) at the left-hand side, allowing the discovery of more complex rules in a more compressed form in the database. This feature also helps reducing the typically very large result-tables of such studies, since allowing ORs in the left-hand side of a single rule could include dozens of classical rules. The capabilities of the SCARF algorithm were demonstrated in mining the Alzheimer's database of the Coalition Against Major Diseases (CAMD) in our recent publication (Archives of Gerontology and Geriatrics Vol. 73, pp. 300-307, 2017). Here we describe the webserver implementation of the algorithm.
Basic Structure of Natural Language Processing Applied on Multiple Domains: Review

Artificial intelligence is the field of computer science and natural language processing is the sub-part method of artificial intelligence. Its major purpose is to remove the transmission gap between human being and computer machine. Their usage in deep learning, machine learning with the help of different algorithms. In this paper, talk about different systems/methods of natural language in the light of multiple six survey-based articles. Defined the basic structure of NLP, their different steps from input sentence to last outcome with the help of figure. Paper represents the NLP techniques, their structure, and their unavoidable importance in the filed of computer science as well as other fields.
Label-Value Extraction from Documents Using Co-SSL Framework

Label-value extraction from documents refers to the task of extracting relevant values for corresponding labels/fields. For example, it encompasses extracting the total amount from receipts, the date value from invoices/patents/forms, or tax amount from receipts/invoices. Automated label-value extraction has widespread applicability in real-world scenarios of document understanding, book-keeping, reconciliation, and content summarization. Recent research has focused on developing label-value extraction models, however, to the best of our knowledge, limited attention has been given to developing a light-weight compact label-value extraction module generalizable across different document types. Since in real-world deployment, a developed model is often required to process different types of documents for the same label/field type, this research proposes a novel Context-based Semi-supervised (Co-SSL) framework for the same. The proposed Co-SSL framework focuses on identifying candidates for each label/field, followed by the generation of their context based on spatial cues. Further, novel data augmentation strategies are proposed which are specifically applicable to the problem of information extraction from documents. The extracted information (candidate and context) is then provided to a deep learning based model trained in a novel semi-supervised setting for applicability in real-world scenarios of limited training data. The performance of the Co-SSL framework has been demonstrated on three challenging datasets containing different document types (receipts, patents, and forms).
A New Cross Detection Method for Triangular Patches

The traditional collision detection of triangular patches is mostly attributed to the position relationship between line segments and points in triangular patches. To judge the position relationship between the three edges and three vertices of triangular patches, it is necessary to calculate the spatial position relationship for many times. The separation, intersection and parallel relationship between spatial triangular patches should be discussed in many cases, which is very complex. While the intersection judgment of triangles on the two-dimensional plane is much simpler. In view of this, this paper proposes a new collision detection method that transforms two spatial triangular patches into the same plane triangular patch. The complexity of triangle collision detection is effectively reduced and the efficiency of collision detection is improved.
Key frame extraction for falling detection

The continuous advancement of computer vision and deep learning technology provides powerful technical support for supervising the occurrence of falling behaviors. Considering that some frames in the video contribute little to the recognition of falling behaviors, in order to eliminate the video frames irrelevant to falling behaviors, this paper proposes an algorithm to extract key frames in a video by combining LUV local maximum and Mask-RCNN. Firstly, candidate key frames are extracted based on the local maximum of LUV. Video frames containing motion changes can be obtained. Afterwards, in order to further eliminate the video frames that are less relevant to the fall action, Mask-RCNN is used for human body detection. According to the aspect ratio of the bounding box and the motion speed of the human body, the video frames that are more relevant to the fall action are selected as key frames. Experiments and results analysis are carried out on the UR fall detection dataset, Multiple cameras fall dataset, Le2i Fall detection dataset and falling video dataset of real scenes. The effectiveness and accuracy of the proposed method are verified.
Fp-Gnn: Adaptive Fpga Accelerator for Graph Neural Networks

Graph Neural Network (GNN) exhibits great success in graph data analysis and promotes the evolution of artificial intelligence. The computing procedure of GNNs can be categorized into two phases: Aggregation and Combination, which have irregular and regular computing characteristics respectively. In this work, we present an adaptive FPGA accelerator, FP-GNN, to enable flexible and efficient GNN acceleration. First, this paper presents quantitative analysis of the impact on performance when changing the order of Aggregation and Combination. Then we propose an Adaptive GNN Accelerator framework (AGA) that adopts a unified processing module to support these two phases simultaneously, and an Adaptive Graph Partition strategy (AGP) to alleviate memory bottleneck and eliminate graph repartition overhead between GNN layers. In addition, we conduct multiple workflow optimizations for both Aggregation and Combination phases to achieve workload balance and feature sparsity elimination. Finally, we implement the FP-GNN on a Xilinx VCU128 FPGA, and conduct experiments on 24 different GNN configurations. Overall, FP-GNN speeds up end-to-end performance by 665x, 24.9x, and 2.33x compared to state-of-the-art CPU, GPU, and FPGA implementations, respectively.
Marker Information Coding for Structural Clustering of Spectral Space

The article substantiates the necessity of relatively processing fairly informative video segments in conditions of reducing their bit volume; reducing quality losses of reconstructed video; ensuring stability of codograms to the influence of communication channel errors on the video frame recovery process. The main stages of the method of coding arrays of structural clusters markers are presented. Here, it is taken into account that the marker structural clustering is carried out in the spectral space of the discrete cosine transform (hereinafter DCT) transformant. Coding concepts for informative markers and lengths of marker chains are justified. Structural and statistical properties of marker sequences for the clustered spectral space by such a structural feature as the number of unit series are taken into account here. A model of representation of marker sequences by twocomponent tuples in the linearized spectral space is developed.
New Constructions of Quadriphase Periodic Almost-Complementary Pairs

Almost-complementary pairs (ACPs) are sequence pairs whose autocorrelations sum up to zero at all but one non-zero time-shifts. Periodic ACPs (P-ACPs) display almost similar correlation properties to that of the periodic complementary pairs (PCPs). In this letter, we propose systematic constructions of quadriphase P-ACPs (QP-ACPs) from aperiodic (periodic) complementary pairs and almost perfect binary (quadriphase) sequences. The proposed construction gives QP-ACPs of new lengths which are not covered in the literature.
A Heterogeneous Full-stack AI Platform for Performance Monitoring and Hardware-specific Optimizations

Many hardware accelerators are proposed to accelerate the computation of DNN to meet the real-time application. However, constrained by the microarchitecture of accelerators, the same neural network generally will have huge performance differences when deployed on different accelerators. It forces the network designers to rethink the network structure from a hardware view. Such a designed effort is more likely to achieve better performance on the targeted accelerator. In this paper, in order to explore hardware-specific optimizations, we designed a full-stack heterogeneous evaluation platform based on the open-source neural network accelerator NVDLA and TVM with a monitoring function. This evaluation platform integrates two processors with instruction sets of Arm and RISC-V and a DNN accelerator, and DNNs under common frameworks (Pytorch, Keras, ONNX, etc.) can be deployed on the platform to analyze its adaptability to the hardware through a simple process. Based on the platform, we conduct some experiments to demonstrate how can neural network affect the performance of specific hardware design. The experimental results show that the unsuited structure of the neural networks will cause additional data transfer on the target hardware, which is the main source of performance and energy degradation. The order of network operators, the width and depth of networks, and the number of operations that are unsupported by accelerators will all affect the performance of the network on specific accelerators. Designers should do some targeted optimizations toward specific hardware deployment and NAS (Network Automatic Search) should consider these factors.
Stationary Wavelet-Based Adaptive Face Transform for Face Recognition in Lighting Conditions

Face recognition plays an important role in identifying an individual person. However, lighting conditions, especially low light intensity, are a serious problem which affects the recognition accuracy. The existing method is insufficiently accurate. Therefore, a stationary wavelet-based adaptive face transform (SWAFT) is proposed to extract features which are robust to lighting conditions for face recognition. Due to a property of the shift and translation invariances, stationary wavelet transform is applied to provide reconstructed images from horizontal, vertical, and diagonal detail coefficients which exclude lighting components. Then adaptive maximum histogram thresholding with matrix multiplication adaptively extracts robust features from reconstructed images. Two-dimensional principal component analysis is employed to reduce robust features by retaining the main information of features. Lastly, a support vector machine efficiently constructs the model and then classifies the robust features to identify each person. From the Extended Yale B database, the proposed SWAFT method achieves an accuracy of 97.81%, which is higher than conventional methods. Our method can adaptively extract robust face features, thus improving the effectiveness of face recognition in uneven lighting conditions.
Circuit Quantum Electrodynamics: Towards Realizing Scalable Quantum Information Processing

The present review attempts to sum up the advancements in circuit quantum electrodynamics (CQED) that has raised our hope for realizing scalable quantum information processing (QIP). Research reveals the advantages of (QIP) over classical information processing in terms of information storage capacity and speed of processing. In this context, the mathematical framework for QIP, like Quantum Fisher Information and computations based on Hilbert space is reviewed. CQED architectures of ever improving configuration are being reported in technical literature. Further, improvements are noticed in coupling strengths between the qubit and the resonator by application of robust protocols. The review highlights the Research efforts and the advancements achieved through faithful quantum state transfer between spatially separated qubits. The important phenomenon of entanglement, deterministic and multipartite, as the cornerstone for scalable QIP is discussed to bring forth the advancements achieved in reducing decoherence and decay. The author sums up the review with a discussion of selected patents and commercial endeavours in the field.
Sarcasm Detection in Social Media Using Hybrid Deep Learning and Machine Learning Approaches

Sarcasm refers to the use of ironic language to convey the message. It is mainly used in social sites like Reddit, Twitter, etc. The identification of sarcasm improves sentiment analysis efficiency, which refers to analyzing people's behavior towards a particular topic or scenario. Our proposed methodology has used a hybrid supervised learning approach to detect the sarcastic patterns for classification. The supervised machine learning approaches include Logistic Regression, Naïve Bayes, Random Forest, and hybrid deep learning models like CNN and RNN. Before implementing the models, the dataset has been preprocessed. The data in the dataset is usually not fit for extracting features as it contains usernames, empty spaces, special characters, stop words, emoticons, abbreviations, hashtags, time stamps, URLs. Hence null values, stop words, punctuation marks, etc., are removed, and lemmatization is also done. After preprocessing, the proposed methodology has been implemented using various supervised machine learning models, hybrid neural network models, ensemble hybrid models, and models implementation by using word embeddings. The models have been implemented on two datasets. The outcome revealed that the hybrid neural network model RNN worked the best for both datasets and got the highest accuracy compared to other models.
Investigating the Demographics and Mobile Phone Technology Used by University Students in Nairobi, Kenya

Purpose: The moderating effect of demographic factors on mobile phone technology use by undergraduate public university students in Nairobi, Kenya was investigated in this study. The study's goal was to determine the moderating effect of demographic characteristics on undergraduate university students' use of mobile phone technology. Methodology: As a theoretical framework, the research employed the media technological determinism theory. The target population consisted of 246,871 undergraduate university students from Nairobi's six public universities. The quantitative research design was used. As data collection tools, self-administered questionnaires were used. Purposive sampling was used in this study to generate a sample size of 573 undergraduate students. Descriptive statistics were used to analyse the data, which was then processed using the Statistical Package for Social Sciences (SPSS) version 22. Findings: The findings revealed that undergraduate university students' use of mobile phone technology was not influenced by demographic factors. The study concluded that respondents' demographics had no moderating effect on the relationship between undergraduate public university students and mobile phone technology use levels. Unique Contribution to Theory and Practice and Policy: Since this research focused on undergraduate university students in public universities in Nairobi, Kenya, the researcher recommends that another research could be carried among post graduate students and also among private universities to find out if demographic factors may be affecting mobile phone technology use.
Character-level Adversarial Examples in Arabic

Several adversarial attacks have been pro-posed in the domains of computer vision and natural language processing (NLP). However, most attacks in the NLP domain have been applied to evaluate deep neural networks (DNNs) that were trained on English corpora. This paper proposes the first set of character-level adversarial attacks designed for models trained on Arabic. We present an efficient method to generate character-level adversarial examples against neural classifiers. Our method relies on flip operations that were designed based on the most common spelling mistakes that non-native Arabic learners make. We find that only a few manipulations are needed to mislead powerful and popular DNN-based classifiers trained on Arabic corpora.
Understanding the Structure of Knowledge Graphs with Abstat Profiles

While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by the daunting task of understanding their content as a result of their size and complexity. Data profiling approaches have been proposed to summarize large KGs into concise and meaningful representations, so that they can be better explored, processed, and managed. Profiles based on schema patterns represent each triple in a KG with its schema-level counterpart, thus covering the entire KG with profiles of considerable size. In this paper, we provide empirical evidence that profiles based on schema patterns, if explored with suitable mechanisms, can be useful to help users understand the content of big and complex KGs. We consider the ABSTAT framework, which provides concise pattern-based profiles and comes with faceted interfaces for profile exploration. Using this tool we present a user study based on query completion tasks, where we demonstrate that users who look at ABSTAT profiles formulate their queries better and faster than users browsing the ontology of the KGs, a pretty strong baseline considered that many KGs do not even come with a specific ontology that can be explored by the users. To the best of our knowledge, this is the first attempt to investigate the impact of profiling techniques on tasks related to content understanding with a user study.
Building Digital Learning Architectures of Participation

This summative chapter outlines how practitioners, learners, managers, and various participants in education and policy might help build learning infrastructures. The authors try to identify who can lead on these processes and how they might do so. They hope to show what a transformed learner-centric education system could look like after consideration of the possibilities they have presented here. Technology has its greatest impact when its use is appropriate to context. Technology projects divorced from their organisational and community contexts solve technical problems and create unintended consequences for the wider organisation and their intended users, and architectures of participation are a means of dealing with such issues.
SMAUG: Streaming Media Augmentation Using CGANs as a Defence Against Video Fingerprinting

Traffic fingerprinting and developing defenses against it has always been an arms race between the attackers and the defenders. The rapid evolution of deep learning methods makes developing stronger traffic fingerprinting models much easier, while overhead, latency, and deployment constraints restrict the abilities of the defenses. As such, there is always the need of coming up with novel defenses against traffic fingerprinting. In this paper, we propose SMAUG, a novel CGAN-based (Conditional Generative Adversarial Network) defense to protect video streaming traffic against fingerprinting. We first assess the performance of various GANs in video streaming traffic synthesis using multiple GAN quality metrics and show that CGAN outperforms other types of GANs such as basic GANs and WGANs (Wasserstein GAN). Our proposed defense, SMAUG, uses CGANs to synthesize video traffic flows and use those synthesized flows to camouflage the original traffic that needs protection. We compare SMAUG with other state-of-the-art defenses - FPA and d*-private methods, as well as a kernel density estimation-based baseline and show that SMAUG provides better privacy with lower overhead and delay.
Issues and Algorithm of Distributed Shared Memory

Internet of things is a rapidly growing industry, and there are more connected devices than humans. There are unlimited internet of things devices around us. The scope of these devices has increased manifold in recent years in different areas like healthcare, unmanned vehicles, automatic monitoring systems, and smart homes, etc. These connected devices produce lots of data that need to be processed to achieve the goal of automation in daily life. The cloud provides many centralized solutions to process data using powerful resources in real-time, but there could be latency issues due to increased network traffic. So a distributed solution is much needed to meet this growing demand of processing data in parallel. In this paper, we propose a distributed shared memory [18] abstraction for the internet of things that uses a graph-aware partitioning algorithm and uses smart message passing techniques to process data in distributed and parallel fashion [19].
Network monitoring and processing accuracy of big data acquisition based on mathematical model of fractional differential equation

Abstract Aiming at the lack of subjectivity of the network security situation assessment method and the complexity and non-linearity of data obtained through situational factors, a fuzzy neural network security situation which is optimised based on an improved gravitational search algorithm combined with fractional differential equation analysis, as an Evaluation model, is proposed. In order to quickly and accurately predict the situation value of the network security situation at that moment, a method for situation prediction of long-term and short-term memory networks based on an improved Nadam algorithm to optimise the online update mechanism is proposed. Note that the situation time series obtained from online assessment cannot be used in a better and efficient manner. The model can minimise the cost function and update the model more effectively by updating the model parameters online Prediction accuracy. In order to improve the problem of slow convergence speed during model network training, the Look-ahead method is used to improve Nesterov's adaptive gradient momentum estimation algorithm to accelerate the model's convergence. Finally, the simulation results analyse and compare the prediction model, which not only improves the convergence speed of the prediction model, but also greatly reduces the prediction error of the model.