Reviewerly/exHarmony huggingface dataset:
Emotion Knowledge Driven Video Highlight Detection

This paper addresses video highlight detection which aims to select a small subset of frames according to user's major or special interest. The performances of conventional methods highly depend on large-scale manually labeled training data which are time-consuming and labor-intensive to collect. To deal with this problem, we trace back to the original problem definition and find that whether a user is interested in a specific video segment heavily depends on human's subjective emotions. Leveraging this insight, we introduce an emotion knowledge driven video detection framework for modeling human's general emotion and inferencing highlight strength. Firstly, we obtain the concept-level representation of the video clip with a front-end network. The concepts are used as nodes to build an emotion-related knowledge graph, and their relationships in the graph are modeled via external public knowledge graphs. Then we adopt Siamese GCNs to model the dependencies between nodes in the graph and propagate messages along the edges. Finally, we compute the emotion-aware representation of the video clip based on the GCN layers and further use it to predict the highlight score. Our framework, including the front-end network, graph convolution layers and the highlight mapping network, can be trained in an end-to-end manner with the constraint of a ranking loss. Experiments on two benchmark datasets show that our proposed method performs favorably against the state-of-the-art methods.
<scp>ICT</scp> diffusion, financial development, and economic growth: Panel evidence from <scp>SAARC</scp> countries

This study examines the interrelation of ICT diffusion and financial development with economic growth in SAARC economies, by employing data for the time period 2000–2017. The empirical analysis is carried out using granger causality and cointegration techniques. First generation panel unit root tests namely LLC, IPS, ADF, and PP test were employed for this purpose. All the variables were found to be integrated of order one at first difference. Pedroni's cointegration test along with Kao's residual‐based cointegration test was used which reveals the presence of long‐run relationship among the variables. Further, cointegration coefficients are computed with the help of fully modified ordinary least squares and dynamic ordinary least squares method. While financial development, ICT diffusion, and trade openness were found to increase the growth rate, inflation exhibited negative impact on growth. Both short‐run and long‐run causality were examined using panel granger causality test which revealed unidirectional causality running from ICT diffusion and financial sector development to economic growth. However, the result of causation between financial sector development and the ICT diffusion was statistically insignificant.
Simulation of English part-of-speech recognition based on machine learning prediction algorithm

English part-of-speech intelligent recognition is the scientific and technological basis for the development of intelligent speech systems. The difficulty in the current English speech recognition system lies in the recognition of English parts of speech. In order to improve the effect of English part-of-speech recognition, this study builds the language rules and morphological models of English morphological forms based on machine learning algorithms. Moreover, this study proposes a stemming extraction algorithm and a syllable division algorithm based on English characteristic rules. By studying basic phrases in English, this study analyzes the compositional structure of phrases, and determines the basic phrase structure and composition rules of English such as noun, verb, and adjective. In addition, this research studies the basic English phrase recognition algorithm based on the rule method and the analysis of basic phrase ambiguity resolution. Finally, this study designs a control experiment to analyze the performance of the algorithm proposed in this paper model and confirm the classification algorithm. The research results show that the algorithm proposed in this paper has a certain practical effect.
Development of a “meta-model” to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support

Abstract Objective Like most real-world data, electronic health record (EHR)–derived data from oncology patients typically exhibits wide interpatient variability in terms of available data elements. This interpatient variability leads to missing data and can present critical challenges in developing and implementing predictive models to underlie clinical decision support for patient-specific oncology care. Here, we sought to develop a novel ensemble approach to addressing missing data that we term the “meta-model” and apply the meta-model to patient-specific cancer prognosis. Materials and Methods Using real-world data, we developed a suite of individual random survival forest models to predict survival in patients with advanced lung cancer, colorectal cancer, and breast cancer. Individual models varied by the predictor data used. We combined models for each cancer type into a meta-model that predicted survival for each patient using a weighted mean of the individual models for which the patient had all requisite predictors. Results The meta-model significantly outperformed many of the individual models and performed similarly to the best performing individual models. Comparisons of the meta-model to a more traditional imputation-based method of addressing missing data supported the meta-model’s utility. Conclusions We developed a novel machine learning–based strategy to underlie clinical decision support and predict survival in cancer patients, despite missing data. The meta-model may more generally provide a tool for addressing missing data across a variety of clinical prediction problems. Moreover, the meta-model may address other challenges in clinical predictive modeling including model extensibility and integration of predictive algorithms trained across different institutions and datasets.
Deep Interactive Memory Network for Aspect-Level Sentiment Analysis

The goal of aspect-level sentiment analysis is to identify the sentiment polarity of a specific opinion target expressed; it is a fine-grained sentiment analysis task. Most of the existing works study how to better use the target information to model the sentence without using the interactive information between the sentence and target. In this article, we argue that the prediction of aspect-level sentiment polarity depends on both context and target. First, we propose a new model based on LSTM and the attention mechanism to predict the sentiment of each target in the review, the matrix-interactive attention network (M-IAN) that models target and context, respectively. M-IAN use an attention matrix to learn the interactive attention of context and target and generates the final representations of target and context. Then we introduce two gate networks based on M-IAN to build a deep interactive memory network to capture multiple interactions of target and context. The deep interactive memory network can excellently formulate specific memory for different targets, which is helpful in sentiment analysis. The experimental results of Restaurant and Laptop datasets of SemEval 2014 validate the effectiveness of our model.
Network Coding for Federated Learning Systems

Nowadays, artificial intelligence is limited by privacy and security problems. Compared with the ordinary machine learning, federated learning (FL) enables multiple participants to collaboratively learn a shared machine learning model while keeping all the training data on local devices. However, most of the current secured federated learning systems (FLSs) are built up with high computational and communication costs. On the other hand, optimizing the network structure of federated learning systems can reduce communication complexity by considering the correlation of the transmission channels. In this paper, we propose Network Coding Federated Learning Systems (NC-FLSs). Specifically, it considers the whole communication network by connecting all the clients and the server. Applying a linear NC scheme to construct a linear combination of the original messages, which is transmitted over the network instead of the messages themselves. Based on NC-FLSs, the communication cost is halved and both data privacy and security are improved with the imperceptibly higher computational cost. Moreover, considering that the network coding structure is independent of the FL model, any FLSs can also be upgraded to its corresponding NC-FLSs. We also implement differential privacy on an NC-FLS to train an image classifier while keeping clients' local data secure and private, which achieves superior performance and efficiency.
MAGIC: Machine-Learning-Guided Image Compression for Vision Applications in Internet of Things

The emergent ecosystems of intelligent edge devices in diverse Internet-of-Things (IoT) applications, from automatic surveillance to precision agriculture, increasingly rely on recording and processing a variety of image data. Due to resource constraints, e.g., energy and communication bandwidth requirements, these applications require compressing the recorded images before transmission. For these applications, image compression commonly requires: 1) maintaining features for coarse-grain pattern recognition instead of the high-level details for human perception due to machine-to-machine communications; 2) high compression ratio that leads to improved energy and transmission efficiency; and 3) large dynamic range of compression and an easy tradeoff between compression factor and quality of reconstruction to accommodate a wide diversity of IoT applications as well as their time-varying energy/performance needs. To address these requirements, we propose, MAGIC, a novel machine learning (ML)-guided image compression framework that judiciously sacrifices the visual quality to achieve much higher compression when compared to traditional techniques, while maintaining accuracy for coarse-grained vision tasks. The central idea is to capture application-specific domain knowledge and efficiently utilize it in achieving high compression. We demonstrate that the MAGIC framework is configurable across a wide range of compression/quality and is capable of compressing beyond the standard quality factor limits of both JPEG 2000 and WebP. We perform experiments on representative IoT applications using two vision data sets and show 42.65× compression at similar accuracy with respect to the source. We highlight low variance in compression rate across images using our technique as compared to JPEG 2000 and WebP.
Weakly-supervised sensor-based activity segmentation and recognition via learning from distributions

Sensor-based activity recognition aims to recognize users' activities from multi-dimensional streams of sensor readings received from ubiquitous sensors. It has been shown that data segmentation and feature extraction are two crucial steps in developing machine learning-based models for sensor-based activity recognition. However, most previous studies were only focused on the latter step by assuming that data segmentation is done in advance. In practice, on the one hand, doing data segmentation on sensory streams is very challenging. On the other hand, if data segmentation is considered as a pre-process, the errors in data segmentation may be propagated to latter steps. Therefore, in this paper, we propose a unified weakly-supervised framework based on kernel embedding of distributions to jointly segment sensor streams, extract powerful features from each segment, and train a final classifier for activity recognition. We further offer an accelerated version for large-scale data by utilizing the technique of random Fourier features. We conduct experiments on four benchmark datasets to verify the effectiveness and scalability of our proposed framework.
Continuous variable tripartite entanglement and steering using a third-order nonlinear optical interaction

Nonlinear optical devices are very useful for generating entanglement. One proposal for the generation of genuine tripartite entanglement [ Phys. Rev. Lett. 120 , 043601 ( 2018 ) PRLTAO 0031-9007 10.1103/PhysRevLett.120.043601 ] is to use a third-order nonlinear optical interaction. Here, we investigate a generalization of this, where we consider the quantum nature of all modes; moreover, we also investigate quantum steering, which is a stronger and asymmetric non-local correlation. We show that the final state presents not only tripartite continuous variable entanglement but also tripartite steering.
<scp>TamForen</scp>: A <scp>tamper‐proof</scp> cloud forensic framework

Abstract Cloud forensics has become increasingly critical in cloud computing security in recent years. A fundamental problem in cloud forensics is how to safely and effectively obtain, preserve, and analyze evidence. With massive cloud forensic systems and tools having been proposed over the years, we identify one challenge that is not adequately addressed in the current literature. The problem is “credibility of cloud evidence”; this is where the evidence collected in the cloud is unreliable due to its multitenancy and the multiple participants in the forensic process. In this paper, we develop a new Cloud Forensics Tamper‐Proof Framework (TamForen) for cloud forensics, which can be used in an untrusted and multitenancy cloud environment. This framework relies on the cloud forensics system independent of the daily cloud activities and is implemented based on the Multilayer Compressed Counting Bloom Filter. Unlike existing cloud forensics methods that depend on the support and trust of cloud service providers, TamForen takes into account the untrustworthiness of participants in the forensics process and conducts tamper‐proof protection of data in a decentralized way without violating users' privacy. We simulate a cloud forensics environment to evaluate TamForen, and the results show that TamForen is feasible.
A Comparison of Deep Learning Models for Pallet Detection in Industrial Warehouses

We consider the problem of automatic pallet detection in industrial environments using a single RGB camera. The problem is relevant in the context of autonomous guided vehicle navigation, and for tasks like pallet storage and retrieval. In particular, we present an approach based on a convolutional neural network (CNN) followed by a decision making step. The convolutional neural network is trained to recognize two elements of a pallet, namely the pallet front side and its pockets. We also report a comparison between three state-of-the-art CNNs: Faster R-CNN, SSD and YOLOv4. For training and evaluation, a dataset was collected in a warehouse. The dataset contains images of pallets in different configurations, either on the ground or on racks, and with arbitrary orientation. Overall, results indicate that Faster R-CNN and SSD perform better than YOLOv4.
Image Hashtag Recommendations Using a Voting Deep Neural Network and Associative Rules Mining Approach

Hashtag-based image descriptions are a popular approach for labeling images on social media platforms. In practice, images are often described by more than one hashtag. Due the rapid development of deep neural networks specialized in image embedding and classification, it is now possible to generate those descriptions automatically. In this paper we propose a novel Voting Deep Neural Network with Associative Rules Mining (VDNN-ARM) algorithm that can be used to solve multi-label hashtag recommendation problems. VDNN-ARM is a machine learning approach that utilizes an ensemble of deep neural networks to generate image features, which are then classified to potential hashtag sets. Proposed hashtags are then filtered by a voting schema. The remaining hashtags might be included in a final recommended hashtags dataset by application of associative rules mining, which explores dependencies in certain hashtag groups. Our approach is evaluated on a HARRISON benchmark dataset as a multi-label classification problem. The highest values of our evaluation parameters, including precision, recall, and accuracy, have been obtained for VDNN-ARM with a confidence threshold 0.95. VDNN-ARM outperforms state-of-the-art algorithms, including VGG-Object + VGG-Scene precision by 17.91% as well as ensemble–FFNN (intersection) recall by 32.33% and accuracy by 27.00%. Both the dataset and all source codes we implemented for this research are available for download, and our results can be reproduced.
Development of optimum watermarking algorithm for radiography images

The fusion of captured multi-focus radiography images using the same scene is essential for adaptation of image information. Besides, transmission of secure radiography image between practitioners with keeping all image details is a challenge. These issues are handled using three dependent goals that include fusion amongst neutron and x-ray radiography images, watermarking of fused radiography images and optimization of watermarked images. The watermarking algorithms are analyzed and evaluated under various attacks. Development of optimization technique is an essential purpose for higher robustness with acceptable imperceptibility and negligible image distortion of radiography images. Backtracking search optimization algorithm(BSA) is functionalized for this target. Paired objective functions are suggested for the BSA algorithm to achieve simultaneously high robustness and imperceptibility. It is fulfilled that proficient results with respect to quality fitness function(FF) are realized with proposed ridgelet watermarking. Consequently, secure transmission of multi-focus radiography images within industrial and medical applications can be fulfilled.
OID: Outlier Identifying and Discarding in Blind Image Deblurring

Blind deblurring methods are sensitive to outliers, such as saturated pixels and non-Gaussian noise. Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers are not conforming to the linear formation of the blurring process. Prior arts develop sophisticated edge-selecting steps or noise filtering pre-processing steps to deal with outliers (i.e. indirect approaches). However, these indirect approaches may fail when massive outliers are presented, since informative details may be polluted by outliers or erased during the pre-processing steps. To address these problems, this paper develops a simple yet effective Outlier Identifying and Discarding (OID) method, which alleviates limitations in existing Maximum A Posteriori (MAP)-based deblurring models when significant outliers are presented. Unlike previous indirect outlier processing methods, OID tackles outliers directly by explicitly identifying and discarding them, when updating both the latent image and the blur kernel during the deblurring process, where the outliers are detected by using the sparse and entropy-based modules. OID is easy to implement and extendable for non-blind restoration. Extensive experiments demonstrate the superiority of OID against recent works both quantitatively and qualitatively.
Disjunctive Fuzzy Neural Networks: A New Splitting-Based Approach to Designing a T–S Fuzzy Model

This article proposes a new network approach toward the implementation of Takagi–Sugeno (T–S) fuzzy models referred to as disjunctive fuzzy neural networks (DJFNNs). The proposed DJFNN involves a novel network architecture and a greedy learning algorithm. Being different from the existing grid-based and clustering-based network architectures, the proposed architecture adds an OR neural layer positioned between the fuzzification layer and the rule layer. In this way, the implied constraint between the number of rules and the number of fuzzy labels is excluded so that a curse of dimensionality can be overcome and more interpretable models are formed. Furthermore, inspired by the core algorithm for building a decision tree, a top–down, nonbacktracking, and greedy algorithm is proposed to learn the unknown parameters of the networks. The input space splits into smaller and smaller subspace along the predefined fuzzy grids in a supervised manner meanwhile the associated conditions of the T–S fuzzy model are identified. The greedy algorithm is applicable to high-dimensional problems since there is no exponential growth in time or space as the dimensionality increases. The new network architecture and greedy learning algorithm make the proposed DJFNN a regression model of high interpretability and good prediction capability, particularly suitable for solving the high-dimensional problems. The DJFNN was experimented with using a synthetic dataset and 28 real-world datasets and compared with classical and state-of-the-art methods through nonparametric statistical tests. The results confirmed the effectiveness of the DJFNN in terms of accuracy, interpretability, and computational cost.
Identifying comparable entities with indirectly associative relations and word embeddings from web search logs

Comparable entity identification plays an essential role in the decision making of both consumers and firms in competitive environment. In contrast to traditional cooccurrence approaches, this paper proposes a novel method, namely, ICE (identifying comparable entities) for effectively identifying comparable entities from web search logs, which are online user-generated contents that reflect users' attention and preferences. ICE consists of two stages: the formulation of directly and indirectly associative relations, followed by a generative procedure that is designed for deriving a broad set of candidate entities that are indirectly associative with a specified focal entity; and a deep-learning-based semantic analysis with a word embedding procedure for measuring the similarities between entity profiles so as to target comparable entities from the candidate set. Extensive experiments show that ICE outperforms several baseline methods in the identification of accurate, broad and novel comparable entities with suitable rankings.