Reviewerly/exHarmony huggingface dataset:
Improving Unsupervised Extractive Summarization by Jointly Modeling Facet and Redundancy

Unsupervised extractive summarization aims to extract salient sentences from documents without labeled corpus. Existing methods are mostly graph-based by computing sentence centrality. These methods have two main problems: facet bias and redundant problems. Facet bias problem leads summarization models tend to select sentences within the same facet, which often leads to the ignoring of other vital facets, especially on long-document and multi-documents. First, to address the facet bias problem, we proposed a novel Facet-Aware centrality-based Ranking model (FAR). We let the model pay more attention to different facets by introducing a sentence-document weight. The weight is added to the sentence centrality score. FAR can alleviate redundancy to some extent. Then, to further reduce redundancy, we proposed a novel Redundancy- and Facet-Aware Ranking model (RFAR) which jointly models facet and redundancy by incorporating Determinantal Point Process (DPP) into the previous proposed FAR. We evaluate our FAR and RFAR on a wide range of summarization tasks that include 8 representative benchmark datasets. Experimental results show that FAR and RFAR consistently outperforms strong baselines, especially in long- and multi-document scenarios, and even perform comparably to some supervised models. Besides, we find that our methods can alleviate the position bias problem.
Towards Intelligent Compression of Hyperspectral Imagery

For space applications, communications bandwidth is often contested and limited. Limited communications is particularly evident for space vehicles in low Earth orbit, where space-based sensor platforms that collect excess amounts of data will often have difficulty communicating this data in a timely manner. Data compression is a necessary step in transferring large files, such as high resolution images, as it allows a system to make more efficient use of communications bandwidth. Sensing systems continue to evolve with increased resolutions and data rates that effectively increase the overall amount of data. Hyperspectral cameras are one type of imaging sensors that produce vast amounts of data relative to conventional camera systems. Due to the data increase, hyperspectral sensors can potentially benefit significantly from data compression. CNN-JPEG is a state-of-the-art, neural network-based compression framework. This algorithm is a lossy, end-to-end image compression system that has been adapted from previous literature to compress hyperspectral imagery collected from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor. By applying the CNN-JPEG algorithm to hyperspectral imagery, we achieve upwards of 17× compression ratios over the original image on average for each spectral band. This increased compression ratio comes at the cost of decreased reconstruction quality. Additionally, the amount of compression delivered by CNN-JPEG is highly dependent on the content of the image. CNN-JPEG provides a unique option for space platforms in which high compression is desired with acceptable losses in image quality.
Building a semantic web services ontology in the pharmaceutical field using the OWL-S Language

The pharmaceutical field is of paramount importance to the health sector, especially designed to provide pharmaceutical Web services to pharmacists, doctors, nurses and also to the general public. At present, the pharmaceutical Web services are described in a syntactic way (expressed in natural language) and therefore difficult to treat by the machine, so it is necessary to use semantic web service techniques by the construction of pharmaceutical ontological resources and adding semantic to the pharmaceutical web services. In our work, we built two ontologies, the first ontology named “Pharmaceutical domain ontology”, it was created by following the “METHONTOLOGY” method and using “OWL language” and “OWL-PROTÉGÉ” tool. The second ontology named “Pharmaceutical Web services ontology”, it was created by using OWL-S language and OWLS-E-P tool (OWL-S EDITOR for PROTÉGÉ), to implement the Web services we use “NetBeans IDE”. The construction of these ontologies is very important to improve the quality of healthcare services.
Score Matching for Compositional Distributions

Compositional data are challenging to analyse due to the non-negativity and sum-to-one constraints on the sample space. With real data, it is often the case that many of the compositional components are highly right-skewed, with large numbers of zeros. Major limitations of currently available models for compositional data include one or more of the following: insufficient flexibility in terms of distributional shape; difficulty in accommodating zeros in the data in estimation; and lack of computational viability in moderate to high dimensions. In this article, we propose a new model, the polynomially tilted pairwise interaction (PPI) model, for analysing compositional data. Maximum likelihood estimation is difficult for the PPI model. Instead, we propose novel score matching estimators, which entails extending the score matching approach to Riemannian manifolds with boundary. These new estimators are available in closed form and simulation studies show that they perform well in practice. As our main application, we analyse real microbiome count data with fixed totals using a multinomial latent variable model with a PPI model for the latent variable distribution. We prove that, under certain conditions, the new score matching estimators are consistent for the parameters in the new multinomial latent variable model.
Low Rank Based Discriminative Least Squares Regression with Sparse Autoencoder Processing for Image Classification

Least squares regression is one of the commonly used classification approaches among supervised learning. However, there are several common issues among least square regression at present. One is that it often overlooks the relationship between each sample; the second is that the used zero-one label matrix usually limits its performance in image classification. To overcome the aforementioned problems, we propose a novel method, i.e., low rank based discriminative least square regression with sparse autoencoder processing (LRDLSR_SAE) in this work. This method can utilize the low rank constraint to decrease the distance among samples in the same class. Furthermore, an error term is implemented to relax the zero-one label matrix for exploring a better discriminate transformation matrix. Besides this, we also apply the sparse autoencoder to process raw samples to obtain more suitable features as new input data for classification. These implementations can improve the classification performance compared to other approaches, which is also confirmed by extensive experimental results in this study.
Student Card Consumption Behavior Based on Clustering Algorithm

Nowadays, the campus card system has been widely used in the process of the continuous development of information construction in Colleges and universities. The cartoon brings great convenience to the life and study of the majority of teachers and students, and also makes the management of the school more simple, fast and efficient. At the same time, a large number of data have been generated in the process of operation of the campus card system, which records the daily learning and life information of teachers and students. Cloud classroom learning platform is a cloud integrated learning platform, which is widely used in various undergraduate departments and graduate schools of our university. Its main design goal is to provide students with online learning and curriculum management, so as to better meet the teaching and learning of teachers and students, as well as the teaching interaction between teachers and students. At the same time, students’ learning behavior data is recorded in the background of cloud classroom learning platform. So how to use these data and find hidden rules and information from these data, in order to provide reference and help for school management, education and teaching work, is worth further exploration.
Handwritten Offline Devanagari Compound Character Recognition Using CNN

Character recognition is the most challenging research topic due to its diverse applicable environment. Numerous research on Devanagari basic characters has been conducted, but due to difficulties associated, research on handwritten compound characters has received very little attention. The dilemma becomes much more complicated as a result of the different authors writing styles and moods. The traditional machine earning approach of character recognition focuses more on feature extraction, whereas the deep learning approach is a subset of machine learning that uses deep neural networks for learning. For current research work, we have created our own dataset for handwritten Devanagari compound characters. Our dataset has 5000 instances of 50 classes of compound characters collected from various writers of different age groups. This paper presents a convolutional neural network model for the recognition of Devanagari compound characters. We have implemented the ResNet model of CNN and used ReLu as an activation function as it effectively trains deep neural networks. We have implemented three-layer CNN, four-layer CNN, and five-layer CNN on our dataset, and its results are compared. We have achieved the highest accuracy of 100% on our dataset.
Tradeblock: Blockchain Based System for Online Classified Advertisement Industries

For decades, classified ads were limited to newspapers, which offered advertisers, small-type notices grouped under specific categories. However, owing to the growing use of mobile internet, online classified market is rapidly gaining grounds in our world. This rapid growth in the classified industry has led to an increase in the number of companies seeking to gain share in this ever-growing market and also with a direct relation to increase in online scammers and fraudulent activities and also trust relationship issues. Scammers detected on one classified ad platform replicate their bad behaviors on multiple platforms. This research presents an innovative solution known as “TradeBlock”, for online classified ad industries to solve these key issues and to improve the whole online classified ad industry using blockchain's complete immutable and decentralized technology of keeping records.
Securing the COVID Patients’ Medical Records Using Encrypted Image Steganography

Strengthening the field of medical informatics has become extremely important and challenging in today's era of digital image transmission. The present work discusses a novel image steganography method that can withstand either statistical tool-based or machine learning (ML)-based steganalysis attacks to ensure secure transmission of the medical images like chest X-rays, CT or MRI reports to the medical experts or hospitals for early diagnosis and fast recovery of the infected persons. The Rivest–Shamir–Adalmen (RSA) encryption technique is incorporated to convert a secret medical image into a cipher image which is enveloped inside a unique fractal cover thus enhancing robustness of the proposed method without debasing perceptual quality of the image. A 1-least significant bit (LSB), 2-LSB and 4-LSB spatial domain method is implemented and compared for hiding the encrypted secret medical image inside a fractal cover image which adds another layer of security. The novelty of the proposed algorithm is the use of fractal cover image with zero skewness angle to affirm the secure location for medical image hiding. Furthermore, steganalysis checks are performed using a deep learning feature extraction model 'Xception' and 'WEKA' image classifiers which authenticate security and integrity of the patients` confidential data. Stego performance of the proposed method is also evaluated by steganalysis tools such as StegSecret, StegExpose and histogram analysis. The statistical and differential analysis performed demonstrates the efficiency of the proposed algorithm over the existing techniques.
An Analytical Method for Evaluating the Robustness of Photonic Integrated Circuits

We propose an efficient analytical method to evaluate the robustness of integrated photonic devices and circuits in the presence of independently-distributed random variations in the device parameters. By approximating the output of a photonic system in terms of a first or second-order Taylor series, we derive closed-form expressions for the mean and variance of the system output, which allow us to compute the one-standard-deviation (1-sigma) bounds on the expected system performance. Compared to other approaches for evaluating robustness, our method does not require computationally-intensive numerical simulations of the system output and can apply to any statistical distribution of parameter variations, including uniform and normal distributions. We demonstrate the method by analyzing the robustness of two coupled resonator systems: a fifth-order microring filter, and optical delay lines based on 1D Coupled Resonator Optical Waveguides and 2D Floquet topological microring lattice. Our method could provide a useful tool in the design and analysis of robust optical devices and circuits.
Large-Scale Interactive Recommendation With Tree-Structured Reinforcement Learning

Although reinforcement learning (RL) techniques are regarded as promising solutions for interactive recommender systems (IRS), such solutions still face three main challenges, namely, i) time inefficiency when handling large discrete action space in IRS, ii) inability to deal with the cold-start scenarios in IRS, iii) data inefficiency during training the RL-based methods. To tackle these challenges, we propose a generic tree-structured RL framework taking both policy-based and value-based approaches into consideration. We propose to construct a balanced tree over representations of the items, such that picking an item is formulated as seeking a suitable path from the root to a leaf node in the balanced tree, which dramatically reduces the time complexity of item recommendation. Further, for cold-start scenarios where prior information of the items is unavailable, we initialize a random balanced tree as the starting point and then refine the tree structure based on the learned item representations. Besides, we also incorporate a user modeling component to explicitly model the environment, which can be utilized in the training phase to improve data efficiency. Extensive experiments on two real-world datasets are conducted and demonstrate that our framework can achieve superior recommendation performance and provide time and data efficiency improvement over state-of-the-art methods in both warm-start and cold-start IRS scenarios.
Privacy, Ethics, and the Dark Web

This article examines the impact that dark web activities are having on society. Hacking and data breach activities have created serious challenges to cybersecurity leading to new data privacy legislation in Europe and the United States. The dark web is a segment of the web where people employ special browsers that can mask their identity and hide their network activity. Here can be found a wide range of illicit activities that are oftentimes criminal in nature, including sales of stolen documents, the information of others, and other contraband. Companies are actively trying to monitor dark web activities because new legislation requires them to inform authorities if a breach compromising data privacy has occurred; otherwise, they can be penalized. It is argued that as governments act to reign in dark web activities, they must employ an ethical perspective that is grounded in theory to weigh the intentions of darknet actors and their impact. This is due to the fact that some dark web activities such as whistleblowing can actually benefit society.
Topological Learning for Motion Data via Mixed Coordinates

Topology can extract the structural information in a dataset efficiently. In this paper, we attempt to incorporate topological information into a multiple output Gaussian process model for transfer learning purposes. To achieve this goal, we extend the framework of circular coordinates into a novel framework of mixed valued coordinates to take linear trends in the time series into consideration. One of the major challenges to learn from multiple time series effectively via a multiple output Gaussian process model is constructing a functional kernel. We propose to use topologically induced clustering to construct a cluster based kernel in a multiple output Gaussian process model. This kernel not only incorporates the topological structural information, but also allows us to put forward a unified framework using topological information in time and motion series.
A Data Preservation Method Based on Blockchain for Digital Sensitive Text Integrity

Since the popularity of digital data sharing has grown, data authentication and integrity have received increased attention, and data preservation technology has evolved to suit the demands of the times. However, the current models and techniques for data preservation are simply a mixture of encryption technology. The process of applying these techniques in data authentication requires the participation of the human factor as a third party, which may lead to the risk of data tampering, attacks, and cracks. Recently, Blockchain technology has occupied the place of the most disruptive technological innovation. Blockchain is based on the concept of decentralizing administration, so instead of having a single authoritative source, the system will have a plethora of records that hold the truth. Whenever a change is required or introduced, all records will need to be updated with the new information after being validated. This one-of-a-kind approach prevents the possibility of adding new information to a single record because there are so many to check to see whether it is correct. If we take the most sensitive text as a case study, then the Holy Quran represents the main reference for authenticity and integrity. However, attacks directed at the digital type of this text have witnessed a continuous increase due to the ease and speed of publishing and sharing verses and surahs and the lack of monitoring and confirmation bodies. To solve the present problem, we introduce a blockchain-based Ayat/Surah preservation model. Through the decentralization and smart contract features of the blockchain, tokens can be maintained automatically without human participation to form a guarding sub-chain in the state unit, and the blockchain has a good performance in combating attacks, which is called the 51% attack. Meanwhile, to solve the problem of data confusion and query difficulty caused by an excessive number of cases, the union of cryptography, hashes, and timestamps is used to form a concatenated master string for preservation. Due to the problem of the repetition of some verses of the Quran in some surahs and the need to absolutely guarantee the sequence of verses, the Merkle tree feature for segmentation is used to calculate the segmentation of the verses of each surah. In this way, memorizing verses becomes an automatic process that allows the Holy Quran to withstand the tests of distortion and distortion. On the other side, it gives us the ability to verify the authenticity and integrity of the Quranic texts scattered on websites and applications.
New Multiphase Encryption Scheme for Better Security Enhancement

With the progression of technology, nowadays, it becomes essential to communicate through unsecure open network. Several researchers have proposed various methods to conceal the messages in order to securely transfer them. In this paper, we propose a new multiphase encryption method which combines the modified Caesar cipher, Atbash Cipher, and XOR cipher, where the encryption is done in multiple phases. It also utilizes the polyalphabetic substitution concept to enrich the encryption process.
Sensor-based human activity recognition using fuzzified deep CNN architecture with <i>λ</i><sub>max</sub> method

Purpose This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone sensor-based human activity recognition. This work mainly focuses on fusing the λ max method for weight initialization, as a data normalization technique, to achieve high accuracy of classification. Design/methodology/approach The major contributions of this work are modeled as FDCNN architecture, which is initially fused with a fuzzy logic based data aggregator. This work significantly focuses on normalizing the University of California, Irvine data set’s statistical parameters before feeding that to convolutional neural network layers. This FDCNN model with λ max method is instrumental in ensuring the faster convergence with improved performance accuracy in sensor based human activity recognition. Impact analysis is carried out to validate the appropriateness of the results with hyper-parameter tuning on the proposed FDCNN model with λ max method. Findings The effectiveness of the proposed FDCNN model with λ max method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795. Practical implications The proposed fuzzy associate rule layer (FAL) layer is responsible for feature association based on fuzzy rules and regulates the uncertainty in the sensor data because of signal inferences and noises. Also, the normalized data is subjectively grouped based on the FAL kernel structure weights assigned with the λ max method. Social implications Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition. Originality/value A novel FDCNN architecture is implemented with appropriate FAL kernel structures.
Learning Unsupervised Face Normalization Through Frontal View Reconstruction

Face normalization from large pose is a challenging problem. Many Generative Adversarial Network (GAN) based models can infer frontal view of profile faces, while they require paired faces and pose label. Instead, we focus on frontal face synthesis with unpaired and unlabeled training data. We present a Frontal View Reconstruction based GAN (FVR-GAN) for large pose face normalization and recognition. The generator of FVR-GAN can be considered as a dual-input auto-encoder, where the identity encoder extracts identity features from an identity image and the template encoder extracts contour features from a frontal image. The decoder combines those two kinds of features and synthesizes a corresponding frontal face. To learn face normalization effectively, we incorporate the Frontal View Reconstruction (FVR) operation into training stage. The FVR operation includes self-reconstruction and frontalization mapping. A group of sub-discriminators which receive different facial parts are employed for discrimination. Considering that different face parts have different contributions to discrimination, we introduce a dynamic weighting mechanism to balance the output of sub-discriminators. FVR-GAN can recover high-quality frontal images under arbitrary poses. Experimental results on datasets of Multi-PIE, IJB-A, LFW and CFP demonstrate the efficacy of our model in terms of quality of synthesized images and recognition accuracy.