Reviewerly/exHarmony huggingface dataset:
Proximity-Based Compression for Network Embedding

Network embedding that encodes structural information of graphs into a low-dimensional vector space has been proven to be essential for network analysis applications, including node classification and community detection. Although recent methods show promising performance for various applications, graph embedding still has some challenges; either the huge size of graphs may hinder a direct application of the existing network embedding method to them, or they suffer compromises in accuracy from locality and noise. In this paper, we propose a novel N etwork E mbedding method, NECL, to generate embedding more efficiently or effectively. Our goal is to answer the following two questions: 1) Does the network C ompression significantly boost L earning? 2) Does network compression improve the quality of the representation? For these goals, first, we propose a novel graph compression method based on the neighborhood similarity that compresses the input graph to a smaller graph with incorporating local proximity of its vertices into super-nodes; second, we employ the compressed graph for network embedding instead of the original large graph to bring down the embedding cost and also to capture the global structure of the original graph; third, we refine the embeddings from the compressed graph to the original graph. NECL is a general meta-strategy that improves the efficiency and effectiveness of many state-of-the-art graph embedding algorithms based on node proximity, including DeepWalk, Node2vec, and LINE. Extensive experiments validate the efficiency and effectiveness of our method, which decreases embedding time and improves classification accuracy as evaluated on single and multi-label classification tasks with large real-world graphs.
Econometrics at Scale: Spark up Big Data in Economics

This paper provides an overview of how to use “big data” for social science research (with an emphasis on economics and finance). We investigate the performance and ease of use of different Spark applications running on a distributed file system to enable the handling and analysis of data sets which were previously not usable due to their size. More specifically, we explain how to use Spark to (i) explore big data sets which exceed retail grade computers memory size and (ii) run typical statistical/econometric tasks including cross sectional, panel data and time series regression models which are prohibitively expensive to evaluate on stand-alone machines. By bridging the gap between the abstract concept of Spark and ready-to-use examples which can easily be altered to suite the researchers need, we provide economists and social scientists more generally with the theory and practice to handle the ever growing datasets available. The ease of reproducing the examples in this paper makes this guide a useful reference for researchers with a limited background in data handling and distributed computing.
Micro-expression recognition convolutional network based on dual-stream temporal-domain information interaction

There is a shortage of experimental data in the use of deep learning method for micro expression recognition. The neural network can obtain limited knowledge in the learning process and it is difficult to improve the accuracy. To tackle the problems, a dual-stream temporal-domain information interaction neural convolution network (Dual Scale Temporal Interactive Convolution Neural Network, DSTICNN) network is constructed to process micro-expression sequences and realize the automatic recognition of micro-expression. By improving the deep mutual learning strategy to guide the network to learn different temporal domain information of the same image sequence, the final recognition rate can be improved. The algorithm is tested on CASMEII and SMIC databases, and the results show that the algorithm in this paper can effectively improve the recognition rate of micro expressions, and the overall algorithm is better than existing algorithms.
Positron emission tomography image enhancement using magnetic resonance images and U-net structure

Positron Emission Tomography (PET) has become an important tool for diagnosing abnormalities, but it suffers from low spatial resolution and a high-level noise. In this article, a Convolutional Neural Network (CNN)-based Single Image Super-resolution (SISR) method is used to produce a PET image with a desired quality. The T1-Weighted Magnetic Resonance (MR) images are used to enrich the information applied to the network. A network based on U-Net structure is used and residual blocks are inserted into the network to improve system performance. This article also evaluates the impact of various loss functions, such as Mean Squared Error (MSE) and its combination with a perceptual loss on the efficiency of the proposed method. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) on two various databases (simulated and clinical data) are 36.78, 0.9927, and 37.36, 0.9714, respectively, indicating good performance of the proposed method compared to previous works.
Annotating affective dimensions in user-generated content

In an era where user-generated content becomes ever more prevalent, reliable methods to judge emotional properties of these kinds of complex texts are needed, for example for developing corpora in machine learning contexts. In this study, we focus on Dutch Twitter messages, a genre which is high in emotional content and frequently investigated in the field of computational linguistics. We compare three annotation methods to annotate the emotional dimensions valence, arousal and dominance in 300 Tweets, namely rating scales, pairwise comparison and best–worst scaling. We evaluate the annotation methods on the criterion of inter-annotator agreement, based on judgments of 18 annotators in total. On this dataset, best–worst scaling has the highest inter-annotator agreement. We find that the difference in agreement is largest for dominance and smallest for valence, suggesting that the benefit of best–worst scaling becomes more pronounced as the annotation task gets more difficult. However, we also find that best–worst scaling is particularly more time-consuming than are rating scale and pairwise comparison annotations. This leads us to conclude that, in particular when dealing with computational models, a comparative assessment of quality versus costs needs to be made.
Styx

The robustness of deep neural network (DNN) is critical and challenging to ensure. In this paper, we propose a general data-oriented mutation framework, called Styx, to improve the robustness of DNN. Styx generates new training data by slightly mutating the training data. In this way, Styx ensures the DNN's accuracy on the test dataset while improving the adaptability to small perturbations, i.e., improving the robustness. We have instantiated Styx for image classification and proposed pixel-level mutation rules that are applicable to any image classification DNNs. We have applied Styx on several commonly used benchmarks and compared Styx with the representative adversarial training methods. The preliminary experimental results indicate the effectiveness of Styx.
Secure multiparty computations in floating-point arithmetic

Abstract Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not without collusion among all parties to put back together all the shares). Thus, the parties may conspire to send all their processed results to a trusted third party (perhaps the data providers) at the conclusion of the computations, with only the trusted third party being able to view the final results. Secure multiparty computations for privacy-preserving machine-learning turn out to be possible using solely standard floating-point arithmetic, at least with a carefully controlled leakage of information less than the loss of accuracy due to roundoff, all backed by rigorous mathematical proofs of worst-case bounds on information loss and numerical stability in finite-precision arithmetic. Numerical examples illustrate the high performance attained on commodity off-the-shelf hardware for generalized linear models, including ordinary linear least-squares regression, binary and multinomial logistic regression, probit regression and Poisson regression.
Applying Blockchain in Agriculture: A Study on Blockchain Technology, Benefits, and Challenges

Agriculture is one of the world’s most influential sectors. Agricultural productivity is beneficial to the economy, privacy, nutrition, and health of a country. In the modern era, agronomists have taken various technologies like IoT and Blockchain to get better farming crops. Blockchain technology used to address several problems from various fields like economy, health, and energy. Blockchain utilized in supply chain management structures in the agriculture sectors that provide accountability, protection, neutrality, and efficiency for all processes in a supply chain. It helps to solve many of the internet’s stability and security complications. In this chapter, we deliberate the use case of blockchain and its functionality process in agriculture. Further explores blockchain technology, application, challenges, and opportunities toward the field of agriculture.
A Smart Chair Design for Recognizing Human-Object Interactions using Pressure Sensors

An important part of identifying the rich variety of human behavior in the visual world is the identification of human object interactions (HOIs). The complete behavior of a human can be understood by recognizing the human, the object, and the activity being performed by the human with the object. Although recent progress has been made in improving HOIs recognition in a completely supervised environment, there is a significant room for possible human-object interactions. In this paper, a smart chair is designed using pressure sensors to identify the object, user, and the activity based on sitting posture of the individual. For this purpose, 8 participants are asked to interact with two different objects i.e., laptop and a book while sitting on a chair and pressure sensor data are recorded. The individuals are then asked to perform three different activities with the object (laptop) and data are recorded. Data are recorded in 10 trials for object and activity. Six different features are extracted from the acquired data to classify the objects, users, and their activities using Naïve Bayes (NB), multilayer perceptron (MLP), J48, and support vector machine (SVM) classifiers. Highest accuracy of 98.75% is achieved to recognize two objects using MLP classifier. Whereas, MLP also recognizes the three activities with a highest accuracy of 80%. Moreover, an accuracy of 98.75% is achieved for identifying users of the object using NB classifier.
Fast analytical motion blur with transparency

We introduce a practical parallel technique to achieve real-time motion blur for textured and semi-transparent triangles with high accuracy using modern commodity GPUs. In our approach, moving triangles are represented as prisms. Each prism is bounded by the initial and final position of the triangle during one animation frame and three bilinear patches on the sides. Each prism covers a number of pixels for a certain amount of time according to its trajectory on the screen. We efficiently find, store and sort the list of prisms covering each pixel including the amount of time the pixel is covered by each prism. This information, together with the color, texture, normal, and transparency of the pixel, is used to resolve its final color. We demonstrate the performance, scalability, and generality of our approach in a number of test scenarios, showing that it achieves a visual quality practically indistinguishable from the ground truth in a matter of just a few milliseconds, including rendering of textured and transparent objects. A supplementary video has been made available online.1
An Efficient Scene Recognition System of Railway Crossing

Railway crossing is one of the places where mobility scooter accidents happen relatively often. To support drivers to prevent such accidents, we propose a scene recognition system for the railway crossing scene. This system can detect railway crossing scene, objects which typically exist close to the railway crossing scene, and the distance to the detected railway crossing. In this system, we propose an efficient four-stage recognition scheme that combines scene screening based on a compact CNN, CNN-based object detection, railway crossing detection, and distance estimation based on the detected warning sign of railway crossing. In the experiments, we demonstrate our system improves precision and F-score for each class by up to 20.6% and 35.0% for the same recall, respectively compared with existing object detection. Moreover, by using the proposed scene screening, we achieved 1.7 to 1.9 times faster execution for scenes in which a railway crossing does not exist on the desktop PC, Raspberry Pi3 model B, Raspberry Pi model B with Neural Compute Stick 2.
A novel adversarial example detection method for malicious PDFs using multiple mutated classifiers

PDF malware remains as a major hacking technique. To distinguish malicious PDFs from massive PDF files poses a challenge to forensic investigation. Machine learning has become a mainstream technology for malicious PDF document detection either to help analysts in a forensic investigation or to prevent a system being attacked. However, adversarial attacks against malicious document classifiers have emerged. Crafted adversarial example based on precision manipulation may be easily misclassified. This poses a major threat to many detectors based on machine learning techniques. Various analysis or detection techniques have been available for specific attacks. The challenge from adversarial attacks is still not yet completely resolved. A major reason is that most of the detection methods are tailor-made for existing adversarial examples only. In this paper, based on an interesting observation that most of these adversarial examples were designed on specific models, we propose a novel approach to generate a group of mutated cross-model classifiers such that adversarial examples cannot pass all classifiers easily. Based on a Prediction Inversion Rate (PIR), we can effectively identify adversarial example from benign documents. Our mutated group of classifiers enhances the power of prediction inconsistency using multiple models and eliminate the effect of transferability (a technique to make the same adversarial example work for multiple models) because of the mutation. Our experiments show that we are better than all existing state-of-the-art detection methods.
Feature Extraction of Unstructured Product Reviews

Today is the world of Internet and online shopping plays a very important role. There are many e-commerce websites from where user can buy a product. But it is very difficult for the user to select which feature of the product is good and which feature of the product is bad because there are thousands of reviews on an e-commerce website. With a huge number of reviews, it is very cumbersome for the user to read all the reviews and take a decision whether which feature is important for this product. It is a very stressful job to determine the important feature of the product from thousands of reviews. A review is a sequence of texts containing the opinion of customer based on their experience. There is no fixed format for the customer to write the reviews. The solution to this problem is to develop a technique that extracts and identifies important features from the unstructured reviews. In this paper, the proposed technique will extract the feature from unstructured reviews and matrix will be constructed to identify the importance of that features is known as Important Feature Review (IFR).
Fractional Order Graph Filters: Design and Implementation

Existing graph filters, polynomial or rational, are mainly of integer order forms. However, there are some frequency responses which are not easily achieved by integer order approximation. It will substantially increase the flexibility of the filters if we relax the integer order to fractional ones. Motivated by fractional order models, we introduce the fractional order graph filters (FOGF), and propose to design the filter coefficients by genetic algorithm. In order to implement distributed computation on a graph, an FOGF can be approximated by the continued fraction expansion and transformed to an infinite impulse response graph filter.
A Multifarious Diagnosis of Breast Cancer Using Mammogram Images – Systematic Review

Abstract Breast Cancer is most common disease in worldwide leads to high rate in mortality. Detection of symptoms at early stage is difficult to identify the breast cancer. Diagnosis through General Medical Examination cannot detect the disease. This paper represents various common methods to detect the Breast Cancer and detection of tumors using mammogram images with Artificial Intelligence (AI). Algorithms helps Images in preprocessed using various feature extraction methods and classification algorithms to predict the class of tumors is benign or malignant. This article insight the recent approaches used in detection of tumors and stage of breast cancer.
Low Cost Early Exit Decision Unit Design for CNN Accelerator

Early exit has been studied as a way to reduce the complex computation of convolutional neural networks. However, in order to determine whether to exit early in a conventional CNN accelerator, there is a problem that a unit for computing softmax layer having a large hardware overhead is required. To solve this problem, we propose a low cost early exit decision unit. The proposed architecture uses only fully-connected (FC) layer outputs to make early exit decisions, so the computation of the softmax layer is not necessary. Our implementation results show an energy reduction of 68% with an accuracy drop of less than 0.3%.
Analysis and Design of High Speed Residue to Binary Reverse Converter for the Moduli Set {2n+ 1, 2n,2n-1}

Residue Number System (RNS) has the specific feature to perform addition, subtraction independently with carry-free propagation. In RNS, conversion is of two types: Forward Conversion and Reverse Conversion. Chinese Remainder Theorem (CRT) and Mixed Radix Conversion (MRC) are the two extensively used techniques adapted for reverse conversion and in this case, CRT is proved faster than MRC. This paper proposes a residue to binary reverse converter for three moduli set {2n+ 1, 2n, 2n- 1} by modifying the CRT. It is simplified in order to design a reverse converter that does not require modular operations. The operations required in the existing designs are mod (2n) mod (2n - 1) and mod (2n - 1) operations. The proposed design provides better results in terms of power, area, delay and number of cells over the existing CRT design and does not require mod operations which are however necessary in the existing CRT technique. This design has been simulated using NC Launch - Encounter tool in Cadence.
Selection of audio materials in college music education courses based on hybrid recommendation algorithm and big data

Abstract Aiming at the problem of audio material selection in college music education courses and to solve the problem of students’ low enthusiasm caused by improper selection of audio materials, this paper adopts the hybrid recommendation algorithm combining big data, personalized recommendation algorithm based on Collaborative Filtering Recommendation Algorithm (CF). Big data is used to obtain user evaluation matrix, and Pearson correlation coefficient is used to calculate the similarity between users, so as to form the nearest neighbor set, obtain the nearest neighbor set of target users, and generate the user-based recommendation set. At the same time, a questionnaire is set up to obtain the real evaluation scores of each user on the audio materials. 20% of the questionnaire data are used for model testing and 80% for model training. The accuracy of the square root error is measured, and the prediction score obtained by the model is compared with the real score. It is found that the mean RMSE value of the model adopted in this paper is 0.3813, which is at least 2.564% higher than that of similar models, and has a higher accuracy. Meanwhile, the algorithm is relatively simple, providing a reference for audio selection of college students in courses.
Using Argumentation to Solve Conflicting Situations in Users’ Preferences in Ambient Assisted Living

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Attention Fusion for Audio-Visual Person Verification Using Multi-Scale Features

In the domain of audio-visual person recognition, many approaches use naive fusion techniques, such as scorelevel fusion or concatenation, to fuse the features obtained by face and audio extraction networks. More sophisticated methods fuse both features taking into account the quality of their corresponding inputs. In this paper, we propose a novel architecture to improve the prediction of feature quality. In contrary to previous works, which estimate feature quality based on the features themselves, we combine the information obtained from different layers of the feature extraction networks. In our analysis, we show that our approach outperforms state-of-the-art fusion approaches on well-established benchmarks for multimodal person verification. Moreover, we show that our model is robust against degradation of the visual input.
Multimedia Emergency Event Extraction and Modeling Based on Object Detection and Bi-LSTM network

Worldwide public emergency events are becoming a significant problem that is threatening the peace and development of the world. Facing at the frequent occurrence of violent and emergency cases around the world, there is no country or region can be entirely outside the incident. Typical events including Hong Kong occupy central incident and Coronavirus have brought significant impact on countries and regions all around the world. Inspired by the occurrence of such events, we proposed two general models that can detect the objects in the events and find the pattern of the events for further prediction and classification.The current object detection and classification models mostly focus on one specific type of objects. When dealing with universal model, most of them will have inferior performance in single model due to the construction of universal model. In this paper, we proposed a new general object detection model to address to the problem that outperforms most detection models that focus on single object detection. In the first part, we proposed a web crawler to find the related emergency events videos for training. Then, we extract the key frames of these videos for the effective training progress. The Universal Event Extraction Model we proposed includes large amounts of objects including human gathering, geological location, police siren, protest and explosion scenarios and the arguments of the scenarios. In the last part of the paper, we applied Graph Attention Model+Bi-LSTM to find the pattern of the emergency events for further prediction and classification.
Construction of reversible cyclic codes over ℤ<sub><i>p</i></sub><i>k</i>

For a prime p and integer k > 1, we find the unique set of generators for cyclic codes over . Besides, the necessary and sufficient conditions for cyclic codes to be reversible are obtained. Finally, as an application, some computational examples are given in support of our results.