Reviewerly/exHarmony huggingface dataset:
Microblog User Interest Recognition Based on Multi-Granularity Text Feature Representation

Microblog user interest discovery is of great significance to the personalized recommendation of social networks and the correct information dissemination guidance. We proposed a method of microblog user interest recognition based on multi-granular text feature representation. First, this paper constructs a text vector for microblog users from three aspects, including topic layer, word order layer, and vocabulary layer. LDA is used to extract the content's topic features, and LSTM learns the semantic features of the sentences. The open-source word vector of Tencent AI Lab is introduced to obtain the semantic features of words; then, the multi-granular text feature representative matrix obtained by the above three feature vectors is input into CNN for text classification training; finally, the interest recognition of weibo users is completed through the multi-terminal output layer. Experimental results show that, compared with the single-granularity feature representation model, the precision, recall, and F1 value of the multi-granularity text feature representation model are improved by 8%, 12%, and 13%, respectively. Based on the careful consideration of text coarse and fine semantic granularity and word granularity, combined with the neural network classification algorithm, the multi-granularity text feature representation model's evaluation index is better than the single-granularity text feature representation model.
Quantum Computing Meets Artificial Intelligence

The world of computing is going to shift towards new paradigms able to provide better performance in solving hard problems than classical computation. In this scenario, quantum computing is assuming a key role thanks to the recent technological enhancements achieved by several big companies in developing computational devices based on fundamental principles of quantum mechanics: superposition, entanglement and interference. These computers will be able to yield performance never seen before in several application domains, and the area of artificial intelligence may be the one most affected by this revolution. Indeed, on the one hand, the intrinsic parallelism provided by quantum computers could support the design of efficient algorithms for artificial intelligence such as, for example, the training algorithms of machine learning models, and bio-inspired optimization algorithms; on the other hand, artificial intelligence techniques could be used to reduce the effect of quantum decoherence in quantum computing, and make this type of computation more reliable. This position paper aims at introducing the readers with this new research area and pave the way towards the design of innovative computing infrastructure where both quantum computing and artificial intelligence take a key role in overcoming the performance of conventional approaches.
Evaluating SIAMESE Architecture Neural Models for Arabic Textual Similarity and Plagiarism Detection

Semantic text similarity in NLP is the study of the degree of resemblance between texts using a certain metric. It has many applications in tasks such as question answering, information retrieval, document clustering, topic detection, topic tracking, questions generation, machine translation, text summarizing and others. Nowadays, neural models are outperforming existing state of the art approaches in major NLP tasks and it is not surprising to see the STS community researchers adopt these models although there are still few works for Arabic language. As Siamese neural architecture has proven recently its relevance for STS in other languages, we evaluate in this work three models within this architecture for Arabic Textual similarity and plagiarism detection: BiLSTM and CNN which we call basic models and a BERT Transformer model.
Saliency detection via coarse-to-fine diffusion-based compactness with weighted learning affinity matrix

Diffusion-based compactness is an effective method for foreground-based saliency detection, in which one key is the conventional graph construction. However, the conventional graph only displays the local structure but not preserves global relevance information. Therefore, diffusion-based compactness cannot highlight complete salient object which contains multiple areas with different features, and the extracted salient regions with weak homogeneous. Aiming to address these problems, we propose a saliency detection method via coarse-to-fine diffusion-based compactness with a weighted learning affinity matrix. Firstly, we construct multi-view conventional graphs to calculate the rough compactness cue. Secondly, we build a two-stage multi-view weighted graphs using a weighted learning affinity matrix and compute the coarse-to-fine compactness cue. Extensive experiments tested on three benchmark datasets, demonstrating the superior against several state-of-the-art methods.
Computer Aided Analysis and Control of Power System Based on Data Mining Technology

The power system is closely related to people's lives and industrial production. The stable operation of the power system is related to the normal production of the country's industrial field and the normal life of the people. With the advent of the computer age, a large amount of power data has emerged in the power system. How to mine this valuable information has become an urgent problem for the power system. In this regard, the purpose of this article is to study the computer-aided analysis and control of power systems based on data mining technology. Based on an in-depth analysis of the current domestic power system status, this paper sorts out the key technologies for constructing a new power system, and then uses data mining technology and SOA and other advanced IT technologies to design and implement a data mining-based computer-aided system for power systems. It also describes the realization of key service modules, including the system login module, database access module and transformer load query Web service module. Finally, this paper carried out a system test experiment, introduced the test environment and test preparation, and gave the design of test cases. Experimental results show that the power system computer-aided system designed in this paper solves the traditional data mining problem based on single-node serial mining, has massive storage and calculation capabilities, and has an accuracy rate of about 87%, which provides important for the development of power systems.
Research on recommendation system based on integrating semantic similarity

The collaborative filtering recommendation algorithm ignores the semantic relationship between the recommendation item and the user, so this paper proposes an improved collaborative filtering recommendation algorithm that fuses semantic similarity of recommended items. This paper aims to improve the performance of the problem of ignoring the relationship between items and data sparsity in collaborative filtering recommendation. First, TransH algorithm was used to extract the relationship between users and movie entities, Second, the similarity between recommended items is calculated and integrated into the similarity matrix of user ratings, which makes up for the fact that the item based collaborative filtering algorithm does not consider the similarity between items. The experimental results show that this collaborative filtering recommendation system based on integrating semantic similarity has good performance in precision, recall and coverage.
FPGA Hardware Design for Plenoptic 3D Image Processing Algorithm Targeting a Mobile Application

Over the past years, widespread use of applications based on 3D image processing has increased rapidly. It is being employed in various fields, such as research, medicine and automation. Plenoptic camera system is used to capture light-field that can be exploited to estimate the 3D depth of the scene. The respective algorithms consist of a large number of computation-intensive instructions. It eventually leads to the problem of large execution time of the algorithm. Moreover, they require substantial amount of memory cells for the storage of intermediate and final results. Desktop GPU based solutions are power intensive and therefore cannot be used in the mobile applications with low energy requirements. The idea presented in this paper is to use the FPGA based hardware design to improve the performance of a 3D depth estimation algorithm by utilizing the advantage of concurrent execution. The algorithm is implemented, evaluated and the results show that FPGA design reduces the respective execution time significantly.
Online Multi-Hop Information Based Kernel Learning Over Graphs

With complex systems emerging in various applications, e.g., financial, biological and social networks, graphs become working horse to model and analyse these systems. Nodes within networks usually entail attributes. Due to privacy concerns and missing observations, nodal attributes may be unavailable for some nodes in real-world networks. Besides, new nodes with unknown nodal attributes may emerge at any time, which require evaluation of the corresponding attributes in real-time. In this context, the present paper reconstructs nodal attributes of unobserved ones via an estimated nodal function based on their connectivity patterns with other nodes in the graph. Unlike existing works which only consider single-hop neighbors, the present paper further explores global information and adaptively combines the effects of multi-hop neighbors together. A multikernel-based approach is developed, which is capable of leveraging global network information, and scales well with network size as well. In addition, it has the flexibility to account for different nonlinear relationship by adaptively selecting the appropriate kernel combination. Experiments on real-word datasets corroborate the merits of the proposed algorithm.
P-DIFF: Learning Classifier with Noisy Labels based on Probability Difference Distributions

Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily overfit on these noisy labels due to its high capability. In this paper, we present a very simple but effective training paradigm called P-DIFF, which can train DNN classifiers but obviously alleviate the adverse impact of noisy labels. Our proposed probability difference distribution implicitly reflects the probability of a training sample to be clean, then this probability is employed to re-weight the corresponding sample during the training process. P-DIFF can also achieve good performance even without prior-knowledge on the noise rate of training samples. Experiments on benchmark datasets also demonstrate that P-DIFF is superior to the state-of-the-art sample selection methods.
Human Activity Recognition using Inertial Data

Human activity recognition is an emerging research domain due to its applications in medicine, sport, surveillance and human-robot interaction. Usually, the classification is based either on data obtained from sensors placed on the human's body or on image or video sequences analysis. In this paper is proposed a procedure based on the k-nearest neighbors, J48 and Random forests classifiers which use data acquired from the accelerometer of a wearable device. The features are extracted using the overlapping time window technique. The recognized activities are: walking, standing and going up-down stairs. The obtained results are better than those obtained in other approaches.
Pretty good state transfer on Cayley graphs over semi-dihedral groups

Let Γ be a graph with adjacency matrix A. The transition matrix of Γ corresponding to A is defined by H(t):=exp⁡(−ιtA), where ι=−1 and t∈R. The graph is said to exhibit pretty good state transfer between a pair of vertices u and v if there exists a sequence of real numbers {tk} and a complex number γ with unit norm such that limk→∞H(tk)eu=γev. In this paper, we explore pretty good state transfer on Cayley graphs over semi-dihedral groups by using the representations of such groups. We show that graphs Cay(SD8n,S) have pretty good state transfer for some suitable subsets S if n is a power of 2. Moreover, we present a sufficient and necessary condition for a non-integral graph Cay(SD8n,S) to admit pretty good state transfer. Some concrete constructions of Cayley graphs over semi-dihedral groups having PGST are also presented.
"Is My Mic On?" Preparing SE Students for Collaborative Remote Work and Hybrid Team Communication

Communication is essential for the success of student and professional software engineering (SE) team development projects. The projects delivered by SE courses provide valuable learning experiences for students because they teach industry-required skills such as teamwork, communication, and scheduling. Professional SE teams have adopted communication software such as Slack, Miro, Microsoft Teams, and GitHub Discussions to share files and convey information between team members. Likewise, they have distributed software development tools such as Visual Studio CodeSpaces and Jira to support productivity. In contrast, within academia, students have focused on having face-to-face meetings for team communication and communication tools for file sharing. Due to the COVID-19 pandemic, universities have been forced to switch to an online or hybrid modality abruptly, thus compelling SE students to quickly adopt communication software. This paper proposes a study on the use of communication software in industry to prepare students for remote software development positions after graduation.
Disocclusion filling for depth-based view synthesis with adaptive utilization of temporal correlations

The depth image-based rendering paves the path to success of 3-D video. However, one issue still remained in 3-D video is how to fill the disocclusion areas. To this end, Gaussian mixture model (GMM) is commonly employed to generate the background, and then to fill the holes. Nevertheless, GMM usually has poor performance for sequences with big foreground reciprocation. In this paper, we aim to enhance the synthesis performance. Firstly, we propose an expectation maximization based GMM background generation method, in which the pixel mixture distribution is derived. Secondly, we propose a refined foreground depth correlation approach, which recovers the background frame-by-frame based on depth information. Finally, we adaptively choose the background pixels from these two methods for filling. Experimental results show that the proposed method outperforms existing non-deep learning based hole filling methods by around 1.1 dB, and significantly surpasses deep learning based alternative in terms of subjective quality.
Graph Signal Denoising Via Unrolling Networks

We propose an interpretable graph neural network framework to denoise single or multiple noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to the graph domain and provide an interpretation of the architecture design from a signal processing perspective. We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the feed-forward process is equivalent to iteratively denoising graph signals. We train the graph unrolling networks through unsupervised learning, where the input noisy graph signals are used to supervise the networks. By leveraging the learning ability of neural networks, we adaptively capture appropriate priors from input noisy graph signals, instead of manually choosing signal priors. To validate the proposed methods, we conduct extensive experiments on both real-world datasets and simulated datasets, and demonstrate that our methods have smaller denoising errors than conventional denoising algorithms and state-of-the-art graph neural networks. For denoising a single smooth graph signal, the normalized mean square error of the proposed networks is around 40% and 60% lower than that of graph Laplacian denoising and graph wavelets, respectively.
Effective Seed-Guided Topic Labeling for Dataless Hierarchical Short Text Classification

Hierarchical text classification has a wide application prospect on the Internet, which aims to classify texts into a given hierarchy. Supervised methods require a large amount of labeled data and are thus costly. For this purpose, the task of dataless hierarchical text classification has attracted more and more attention of researchers in recent years, which only requires a few relevant seed words for given categories. However, existing approaches mainly focus on long texts without considering the characteristics of short texts, so are not suitable in many scenarios. In this paper, we tackle dataless hierarchical short text classification for the first time, and propose an innovative model named Hierarchical Seeded Biterm Topic Model (HierSeedBTM), which effectively leverages seed words in Biterm Topic Model (BTM) to guide the hierarchical topic labeling. Specifically, our model introduces iterative distribution propagation mechanism among topic models in different levels to incorporate the hierarchical structure information. Experiments on two public datasets show that the proposed model is more effective than the state-of-the-art methods of dataless hierarchical text classification designed for long texts.
Scaling &amp; fuzzing: Personal image privacy from automated attacks in mobile cloud computing

The mobile cloud computing (MCC) paradigm provides a range of useful services to smart phone users and enhances the user experience significantly. But, as MCC requires the data to be offloaded to an external server, there are serious concerns regarding the privacy of the users’ personal data such as images. For instance, a cloud server could perform image segmentation on user images to extract interesting artifacts such as restaurants, user’s clothing preferences, tourist locations, participation in social events and so on, which characterize the user’s personal life. The leakage of such private information could lead to milder consequences like targeted advertising or more serious consequences like identity theft. In this work, to protect the privacy of user images, we describe a privacy-preserving image filtering for mobile cloud computing that protects against automated inference attacks based on techniques like image segmentation. The key intuition of our approach is to leverage the inherent properties of the discrete Fourier transform (DFT), which transforms each image pixel into a complex value real and imaginary parts which can be processed independently. By dividing the image in this manner, we are able to process distinct parts of the image on different non-colluding servers and aggregate the results at the client. Furthermore, to prevent information leakage at individual servers, we obfuscate the data sent to any given server using an efficient reversible transformation. We prove our approach to be secure under the semi-honest model and non-colluding servers where at least one server does not collude with the rest of the servers. In comparison to the existing paradigm of outsourced privacy preserving computation, i.e., processing encrypted data using homomorphic encryption, our approach employs easy-to-implement obfuscation techniques without any key management overhead at the client. Using experimental evaluation as well as information theoretic leakage evaluation, we show that our approach is efficient and suitable for users of mobile devices.
Adversarial network embedding on heterogeneous information networks

Abstract Network embedding has been proven to be helpful for solving real-world problems. Moreover, real-world networks are often heterogeneous information networks(HINs). In this paper, we propose a new adversarial framework for heterogeneous network embedding, namely AGNE-HIN. AGNE-HIN can learn latent code distribution in the network through a generative adversarial way. What’s more, to reduce the global smoothness of the embedded vector caused by GAN, we apply perturbation to the input to form adversarial data. Experimental results verify our design and demonstrate the effectiveness of the proposed method in node clustering, link prediction and similarity ranking tasks.
Applying Hindsight Experience Replay to Procedural Level Generation

Designing a video game level requires a precise balance in difficulty adjustment as a level that is too simple will cause players to lose interest quickly. On the other hand, making a level too complicated will frustrate the players, making them abandon the game. We propose a new method to make a level generator that can learn how to design a game level by itself. Our proposed method can be used for different games with only minimal adjustments. We improve the previously proposed method by making our generator able to design a level that satisfies every user's criterion. We do this by combining Procedural Content Generation via Reinforcement Learning with the Hindsight Experience Replay method. We use our model to generate levels from 4 different games and compare the success rate with a random agent. Our model achieves more than 90% success rate for almost every scenario and performs much better when compared to a random agent.
Chinese Sentiment Classification Model of Neural Network Based on Particle Swarm Optimization

Due to the differences in features between different languages, Chinese text is more complicated and difficult in natural language processing tasks than English text. This paper proposes a neural network Chinese sentiment classification model based on particle swarm optimization (PSO-Attention-LSTM), the model uses the Long Short Term Memory Network superimposed attention mechanism to extract information from Chinese review data and determine the sentiment polarity of the sentence; aiming at the problem that parameters such as the number of hidden layer neurons in the LSTM unit and the number of batches of the neural network are difficult to determine, the global optimization capability of the particle swarm optimization (PSO) is used to optimize the parameters. The experimental results show that the neural network Chinese sentiment classification model based on particle swarm optimization has improved the accuracy of the hotel data set by nearly 6 percentage points.
A Framework for Pruning Deep Neural Networks Using Energy-Based Models

A typical deep neural network (DNN) has a large number of trainable parameters. Choosing a network with proper capacity is challenging and generally a larger network with excessive capacity is trained. Pruning is an established approach to reducing the number of parameters in a DNN. In this paper, we propose a framework for pruning DNNs based on a population-based global optimization method. This framework can use any pruning objective function. As a case study, we propose a simple but efficient objective function based on the concept of energy-based models. Our experiments on ResNets, AlexNet, and SqueezeNet for the CIFAR-10 and CIFAR-100 datasets show a pruning rate of more than 50% of the trainable parameters with approximately < 5% and < 1% drop of Top-1 and Top-5 classification accuracy, respectively.
Printed Light Tags and the Magic Bookmark

Paper books offer a unique physical feel, which supports the reading experience through enhanced browsing, bookmarking, free-form annotations, memory and reduced eye strain. In contrast, electronic solutions, such as tablets and e-readers, offer interactive links, updatable information, easier content sharing, and efficient collaboration. To combine the best aspects of paper and digital information for reading, we demonstrate two mechanisms for augmenting paper with light sensors that trigger digital links on a nearby smartphone. Light Tags on every page of a book are used in a first demonstration to identify which pages are open. These are replaced with an electronic Magic Bookmark in a second demonstration, avoiding the need to instrument every page.