Reviewerly/exHarmony huggingface dataset:
Research on image compression technology based on Huffman coding

With the development of information technology, image has become the mainstream of information transmission. Compared with character, image contains more information, but because image and character need more storage capacity, it will occupy more bandwidth in network transmission. In order to transmit image information more quickly, image compression is a good choice. This paper is based on an eye of image compression. The method of image compression in this paper is that firstly, the image is filtered by wavelet transform to remove the redundant information in the image, and then the Huffman method is used to encode the image. The simulation results of JPEG format image show that the size of the image can be reduced in the same image effect.
Multi-Component Image Translation for Deep Domain Generalization

Domain adaption (DA) and domain generalization (DG) are two closely related methods which are both concerned with the task of assigning labels to an unlabeled data set. The only dissimilarity between these approaches is that DA can access the target data during the training phase, while the target data is totally unseen during the training phase in DG. The task of DG is challenging as we have no earlier knowledge of the target samples. If DA methods are applied directly to DG by a simple exclusion of the target data from training, poor performance will result for a given task. In this paper, we tackle the domain generalization challenge in two ways. In our first approach, we propose a novel deep domain generalization architecture utilizing synthetic data generated by a Generative Adversarial Network (GAN). The discrepancy between the generated images and synthetic images is minimized using existing domain discrepancy metrics such as maximum mean discrepancy or correlation alignment. In our second approach, we introduce a protocol for applying DA methods to a DG scenario by excluding the target data from the training phase, splitting the source data to training and validation parts, and treating the validation data as target data for DA. We conduct extensive experiments on four cross-domain benchmark datasets. Experimental results signify our proposed model outperforms the current state-of-the-art methods for DG.
SAR Image Despeckling Based on Nonlocal Low-Rank Regularization

In this paper, we propose a new synthetic aperture radar (SAR) image despeckling method based on the nonlocal low-rank minimization model. First, some similar image patches are selected for each pixel to construct the patch group matrix (PGM). Then, a new low-rank minimization model, called Fisher-Tippett distribution (FT)-weighted nuclear norm minimization (WNNM), is proposed to recover the underlying low-rank component from the PGM. Specifically, the FT-WNNM is developed by reformulating the despeckling problem as the maximizing a posterior probability problem. The new model consists of a data fidelity term and a regularization term (also called prior term). The data fidelity term is derived from the statistical distribution of SAR images in the logarithm domain, which is known as the Fisher-Tippett distribution, and the regularization term is the recent weighted nuclear norm. Then, the alternating direction method of multipliers (ADMM) is introduced to solve the corresponding optimization problem. Under ADMM framework, the resulting subproblems can be solved efficiently and the convergence can be guaranteed. Extensive experiments on both simulated and real SAR images demonstrate that the proposed method can achieve comparable or even better despeckling performance than some state-of-the-art despeckling algorithms.
Neural Demographic Prediction using Search Query

Demographics of online users such as age and gender play an important role in personalized web applications. However, it is difficult to directly obtain the demographic information of online users. Luckily, search queries can cover many online users and the search queries from users with different demographics usually have some difference in contents and writing styles. Thus, search queries can provide useful clues for demographic prediction. In this paper, we study predicting users' demographics based on their search queries, and propose a neural approach for this task. Since search queries can be very noisy and many of them are not useful, instead of combining all queries together for user representation, in our approach we propose a hierarchical user representation with attention (HURA) model to learn informative user representations from their search queries. Our HURA model first learns representations for search queries from words using a word encoder, which consists of a CNN network and a word-level attention network to select important words. Then we learn representations of users based on the representations of their search queries using a query encoder, which contains a CNN network to capture the local contexts of search queries and a query-level attention network to select informative search queries for demographic prediction. Experiments on two real-world datasets validate that our approach can effectively improve the performance of search query based age and gender prediction and consistently outperform many baseline methods.
HeadNet: An End-to-End Adaptive Relational Network for Head Detection

Head detection plays an important role in localizing and identifying persons from visual data. Most existing methods treat head detection as a specific form of object detection. Head detection is nontrivial due to the considerable difficulty in building the local and global information under conditions of unconstrained pose and orientation. To address these issues, this paper presents an effective adaptive relational network to capture context information, which is greatly helpful to suppress missed detection. We show that the fundamental contextual properties, such as the global shape priors from different heads and the local adjacent relationship between the head and shoulders, can be systematically quantified by visual operators. Specifically, we propose a two-step search algorithm to quantify the global intergroup conflict with adaptive scale, pose and viewpoint. Meanwhile, a structured feature module is introduced to capture the local relation of intraindividual stability. Finally, the global priors and local relation are integrated seamlessly into a single-stage head detector that is end-to-end trainable. An extensive ablation analysis demonstrates the effectiveness of our approach. We achieve state-of-the-art results on two challenging datasets, i.e., HollywoodHeads and Brainwash.
Networked Learning and Postdigital Education

This paper considers the positon of networked learning in higher education learning and teaching in the postdigital era. The underlying origins and principles upon which networked learning are based can be traced back to the critical pedagogy of Freire and emphasise a critical relationship with the digital, the human and the current socio-political and material higher educational context. We examine the theoretical and practical ideas around networked learning in which connections are forged between learners; between learners and their tutors; and between learning communities and learning resources. We describe two case studies to illustrate the theory, pedagogy and practice of networked learning for contemporary higher education learning and teaching. One from the perspective of the online distant learner and their experience of networked learning; the other from the perspective of tutors, which focuses on the challenges they face in moving into the digital world. We conclude by claiming that the way networked learning has developed in theory and practice means it is an approach and pedagogy that makes it entirely suitable for a postdigital world.
Multiview Transfer Learning for Software Defect Prediction

Most software defect prediction models usually assume that enough historical training instances with labels are available. Additionally, the training data and the predicted instances should share the same features to ensure the prediction accuracy. However, in practice, there are many datasets with different granularities containing information in different dimensions. Therefore, it is valuable to effectively use the small scale and different dimensions of data as training instances to improve the prediction performance of the model. We propose a heterogeneous data orienting multiview transfer learning for software defect prediction, denoted as MTDP, which can achieve different dimensions and granularities features to automatically learn labels through neural network models. With this multiview transfer method, lots of training instances are provided for software defect prediction model to ensure the effectiveness of training labels. The proposed MTDP method has four main stages: 1) build heterogeneous transfer models; 2) transfer heterogeneous instances to generate quasi-real instances; 3) label quasi-real instances through co-training and then expand the training set; and (4) construct improved software defect prediction models. The experimental results show that the quasi-real instances have similar effects compared with real instances. Moreover, the software defect prediction performance can be improved by introducing the quasi-real instances into the training dataset.
Quantum circuit compilers using gate commutation rules

The use of noisy intermediate-scale quantum computers (NISQCs), which consist of dozens of noisy qubits with limited coupling constraints, has been increasing. A circuit compiler, which transforms an input circuit into an equivalent output circuit conforming the coupling constraints with as few additional gates as possible, is essential for running applications on NISQCs. We propose a formulation and two algorithms exploiting gate commutation rules to obtain a better circuit compiler.
Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Under the background of “innovation and entrepreneurship,” how to scientifically and rationally choose employment or independent entrepreneurship according to their own comprehensive situation is of great significance to the planning and development of their own career and the social adaptation of university personnel training. This study aims to develop an adaptive support vector machine framework, called RF-CSCA-SVM, for predicting college students' entrepreneurial intention in advance; that is, students choose to start a business or find a job after graduation. RF-CSCA-SVM combines random forest (RF), support vector machine (SVM), sine cosine algorithm (SCA), and chaotic local search. In this framework, RF is used to select the most important factors; SVM is employed to establish the relationship model between the factors and the students’ decision to choose to start their own business or look for jobs. SCA is used to tune the optimal parameters for SVM. Additionally, chaotic local search is utilized to enhance the search capability of SCA. A total of 300 students were collected to develop the predictive model. To validate the developed method, other four meta-heuristic based SVM methods were used for comparison in terms of classification accuracy, Matthews Correlation Coefficients (MCC), sensitivity, and specificity. The experimental results demonstrate that the proposed method can be regarded as a promising success with the excellent predictive performance. Promisingly, the established adaptive SVM framework might serve as a new candidate of powerful tools for entrepreneurial intention prediction.
Intersections between IoT and distributed ledger

The Internet of Things (IoT) is growing exponentially. It allows not only humans but also all various devices and objects in the environment to be connected over the Internet to share their data to create new applications and services which result in a more convenient and connected lifestyle. However, the current centralized IoT architecture faces several issues. For instance, all computing operations of all nodes in the network are carried out using a single server. This creates a single point of failure in which if the server goes down, the entire system will be unavailable. Also, the IoT centralized architecture is an easy target of various types of security and privacy attacks, since all IoT data collected from different devices is under the full authority of a single server. Therefore, adopting one of the Distributed Ledger Technologies (DLTs) for the IoT may be the right decision. One of the popular types of DLTs is the blockchain. It provides an immutable ledger with the capability of maintaining the integrity of transactions by decentralizing the ledger among participating nodes in the blockchain network which eliminates the need for a central authority. Integrating the IoT system with the blockchain technology can provide several benefits which can resolve the issues associated with the IoT centralized architecture. Therefore, this chapter provides a discussion of the intersection between IoT and DLTs. It started by providing an overview of the DLT by highlighting its main components, benefits and challenges. The centralized IoT system is also discussed with highlighting its essential limitations. Then, the integration of blockchain with IoT is presented by highlighting the integration benefits. Various application and challenges of integrating blockchain with IoT are also discussed.
Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools toward real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption, and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models, and the validation results based on the benchmarking metrics are reported.
Wordnet-Based Criminal Networks Mining for Cybercrime Investigation

Cybercriminals exploit the opportunities provided by the information revolution and social media to communicate and conduct underground illicit activities, such as online fraudulence, cyber predation, cyberbullying, hacking, blackmailing, and drug smuggling.To combat the increasing number of criminal activities, structure and content analysis of criminal communities can provide insight and facilitate cybercrime forensics.In this paper, we propose a framework to analyze chat logs for crime investigation using data mining and natural language processing techniques.The proposed framework extracts the social network from chat logs and summarizes conversation into topics.The crime investigator can use information visualizer to see the crime-related results.To test the validity of our proposed framework, we worked in a joint effort with the cybercrime unit of a Canadian law enforcement agency.The experimental outcomes on real-life data and feedback from the law enforcement officers suggest that the proposed chat log mining framework meets the need for law enforcement agencies and is very effective for crime investigation.
GraphSAR

Large-scale graph processing has drawn great attention in recent years. The emerging metal-oxide resistive random access memory (ReRAM) and ReRAM crossbars have shown huge potential in accelerating graph processing. However, the sparse feature of natural graphs hinders the performance of graph processing on ReRAMs. Previous work of graph processing on ReRAMs stored and computed edges separately, leading to high energy consumption and long latency of transferring data. In this paper, we present GraphSAR, a sparsity-aware processing-in-memory large-scale graph processing accelerator on ReRAMs. Computations over edges are performed in the memory, eliminating overheads of transferring edges. Moreover, graphs are divided considering the sparsity. Subgraphs with low densities are further divided into smaller ones to minimize the waste of memory space. According to our extensive experimental results, GraphSAR achieves 4.43x energy reduction and 1.85x speedup (8.19x lower energy-delay product, EDP) against previous graph processing architecture on ReRAMs (GraphR [1]).
Hierarchical Topic Modeling of Twitter Data for Online Analytical Processing

Social platforms, such as Twitter, reveal much about the tastes of the public. Many studies focus on the content analysis of social platforms, which assists in product promotion and sentiment investigation. On the other hand, online analytical processing (OLAP) has been proven to be very effective for analyzing multidimensional structured data. The key purpose of applying OLAP to text messages, (e.g., tweets), called text OLAP, is to mine and construct the hierarchical dimension based on the unstructured text content. In contrast to the plain texts which text OLAP usually handles, the social media content includes a wealth of social relationship information which can be employed to extract a more effective dimensional hierarchy. In this paper, we propose a topic model called twitter hierarchical latent Dirichlet allocation (thLDA). Based on hierarchical latent Dirichlet allocation, thLDA aims to automatically mine the hierarchical dimension of tweets' topics, which can be further employed for text OLAP on the tweets. Furthermore, thLDA uses word2vec to analyze the semantic relationships of words in tweets to obtain a more effective dimension. We conduct extensive experiments on huge quantities of Twitter data and evaluate the effectiveness of thLDA. The experimental results demonstrate that it outperforms other current topic models in mining and constructing the hierarchical dimension of tweeters' topics.
Semantic vector learning for natural language understanding

Natural language understanding (NLU) is a core technology for implementing natural interfaces and has received much attention in recent years. While learning embedding models has yielded fruitful results in several NLP subfields, most notably Word2Vec, embedding correspondence has relatively not been well explored especially in the context of NLU, a task that typically extracts structured semantic knowledge from a text. A NLU embedding model can facilitate analyzing and understanding relationships between unstructured texts and their corresponding structured semantic knowledge, essential for both researchers and practitioners of NLU. Toward this end, we propose a framework that learns to embed semantic correspondence between text and its extracted semantic knowledge, called semantic frame. One key contributed technique is semantic frame reconstruction used to derive a one-to-one mapping between embedded vectors and their corresponding semantic frames. Embedding into semantically meaningful vectors and computing their distances in vector space provides a simple, but effective way to measure semantic similarities. With the proposed framework, we demonstrate three key areas where the embedding model can be effective: visualization, distance based semantic search, similarity-based intent classification and re-ranking.
Broad echo state network for multivariate time series prediction

In this paper, a broad echo state network with multiple reservoirs in parallel configuration (Broad-ESN) is proposed for a class of multivariate time series prediction. Firstly, through the unsupervised learning algorithm of restricted Boltzmann machine (RBM), the number of reservoirs of Broad-ESN can be determined, such that the dynamic characteristics of a class of multivariate time series can be fully reflected. Secondly, a parameter optimization method based on Davidon–Fletcher–Powell (DFP) quasi-Newton algorithm is proposed to optimize the reservoir parameters of Broad-ESN. Meanwhile, an output weights learning method based on output error is given to train the output weights of Broad-ESN. Thirdly, a sufficient condition for the echo state property of Broad-ESN is given. Finally, four examples are given to verify the effectiveness of Broad-ESN.
Multimodal Review Generation for Recommender Systems

Key to recommender systems is learning user preferences, which are expressed through various modalities. In online reviews, for instance, this manifests in numerical rating, textual content, as well as visual images. In this work, we hypothesize that modelling these modalities jointly would result in a more holistic representation of a review towards more accurate recommendations. Therefore, we propose Multimodal Review Generation (MRG), a neural approach that simultaneously models a rating prediction component and a review text generation component. We hypothesize that the shared user and item representations would augment the rating prediction with richer information from review text, while sensitizing the generated review text to sentiment features based on user and item of interest. Moreover, when review photos are available, visual features could inform the review text generation further. Comprehensive experiments on real-life datasets from several major US cities show that the proposed model outperforms comparable multimodal baselines, while an ablation analysis establishes the relative contributions of the respective components of the joint model.
A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records

Adverse drug events (ADEs) as well as other preventable adverse events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in the United States alone. Therefore, it is of paramount importance to reduce the impact and prevalence of ADEs within the healthcare sector, not only since it will result in reducing human suffering, but also as a means to substantially reduce economical strains on the healthcare system. One approach to mitigate this problem is to employ predictive models. While existing methods have been focusing on the exploitation of static features, limited attention has been given to temporal features.In this paper, we present a novel classification framework for detecting ADEs in complex Electronic health records (EHRs) by exploiting the temporality and sparsity of the underlying features. The proposed framework consists of three phases for transforming sparse and multi-variate time series features into a single-valued feature representation, which can then be used by any classifier. Moreover, we propose and evaluate three different strategies for leveraging feature sparsity by incorporating it into the new representation.A large-scale evaluation on 15 ADE datasets extracted from a real-world EHR system shows that the proposed framework achieves significantly improved predictive performance compared to state-of-the-art. Moreover, our framework can reveal features that are clinically consistent with medical findings on ADE detection.Our study and experimental findings demonstrate that temporal multi-variate features of variable length and with high sparsity can be effectively utilized to predict ADEs from EHRs. Two key advantages of our framework are that it is method agnostic, i.e., versatile, and of low computational cost, i.e., fast; hence providing an important building block for future exploitation within the domain of machine learning from EHRs.