Reviewerly/exHarmony huggingface dataset:
Online Reviews Sentiment Analysis and Product Feature Improvement with Deep Learning

The text mining of online reviews is currently a popular research direction of e-commerce and is considered the next blue ocean. Online reviews can dig out consumer preferences and provide theoretical guidance for the improvement of product features. However, current research mostly focuses on sentiment analysis methods and rarely involves feature extraction and large-scale data recognition. This article uses word segmentation technology to create a new feature extraction method. With the long short-term memory neural network and latent Dirichlet allocation topic model, we propose a product feature improvement model—CESC (Consumer online reviews–Extract short text–Sentiment analysis–Cluster feature). The model can derive the product features and attitudes that consumers prefer based on consumer online reviews and use it to improve product features. According to the experimental results of three electronic products sold on the e-commerce platform, the model can effectively dig out consumer preferences for online reviews. Enterprises can improve the quality of products and services, better meet the needs of consumers, promote consumers’ consumption, and achieve the enterprises’ goals and values.
A content awareness module for predictive lossless image compression to achieve high throughput data sharing over the network storage

The idea of applying integer Reversible Colour Transform to increase compression ratios in lossless image compression is a well-established and widely used practice. Although various colour transformations have been introduced and investigated in the past two decades, the process of determining the best colour scheme in a reasonable time remains an open challenge. For instance, the overhead time (i.e. to determine a suitable colour transformation) of the traditional colour selector mechanism can take up to 50% of the actual compression time. To avoid such high overhead, usually, one pre-specified transformation is applied regardless of the nature of the image and/or correlation of the colour components. We propose a robust selection mechanism capable of reducing the overhead time to 20% of the actual compression time. It is postulated that implementing the proposed selection mechanism within the actual compression scheme such as JPEG-LS can further reduce the overhead time to 10%. In addition, the proposed scheme can also be extended to facilitate network-based compression–decompression mechanism over distributed systems.
An Input-Independent Loop Type-III Buck Converter With PSRR Improvement and EMI Suppression for Enhancing the Security of Edge Devices

This letter proposes an input-independent loop type-III buck converter (ILT3C) enhancing the security of edge devices. Compared to a conventional type-III buck converter, the ILT3C maximally improves power-supply rejection ratio as much as 34.1 dB to be robust against power injection attack. It also reduces the peak of electromagnetic interference as much as 35.8 dB <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> V to enhance the security from power side-channel attack. Furthermore, the external components such as an inductor and a capacitor of the converter should be integrated into a package to minimize its exposure to unknown hacking risks. Thus, the proposed converter operates with a high switching frequency ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sw</sub> ) of 20 MHz and it can reduce the size of the external components enough to be placed in the package. The prototype converter was implemented in a 0.18- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m CMOS process. This ILT3C achieves the maximum efficiency of 90.7% and occupies an active area of 0.195 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Regressive Pseudo Label for Weakly Supervised Facial Landmark Detection

The progress of the deep neural network and visual sensors promote the facial landmark detection. However, faces are easily collected from Internet of Things (IoT) devices worldwide but are hard-labeled facial landmarks in consistent style. Though many weak supervision (WS) algorithms and theories have been proposed to handle the labeling problem, most of them are tailored for classification tasks and fail in regression tasks, especially in facial landmark detection. To tackle this WS regression task, first, we propose a regressive pseudo-labeling method by analyzing the cluster assumption of facial landmark detection, where overlaps are reduced and interval areas are increased among clusters of facial parts for unlabeled faces. Moreover, auxiliary information and domain loss are utilized to adapt model to samples with different styles. Second, we design a generator–regressor network to first estimate facial boundary attentions and then to locate facial landmarks. However, when generating pseudo labels based on predictive models automatically, there are two major issues. One is that the results of the multistage network highly depend on the former-stage accuracy, and another is that jointing different annotation styles always produces ambiguity feature representation. Thus, we propose two ideas. During training, the generator and regressor are decoupled to alleviate the inner dependence of the multistage network, and the heatmap discriminator is introduced to improve the quality of the predicted facial boundary. We design a transformer structure to fuse face image and boundary attentions, so that it can further complement useful features. Based on these ideas, our methods can automatically annotate accurate pseudo facial landmarks for unlabeled faces. Extensive experiments show that our model achieves good performance on different benchmark data sets, both in accuracy and efficiency.
APIRO: A Framework for Automated Security Tools API Recommendation

Security Orchestration, Automation, and Response (SOAR) platforms integrate and orchestrate a wide variety of security tools to accelerate the operational activities of Security Operation Center (SOC). Integration of security tools in a SOAR platform is mostly done manually using APIs, plugins, and scripts. SOC teams need to navigate through API calls of different security tools to find a suitable API to define or update an incident response action. Analyzing various types of API documentation with diverse API format and presentation structure involves significant challenges such as data availability, data heterogeneity, and semantic variation for automatic identification of security tool APIs specific to a particular task. Given these challenges can have negative impact on SOC team’s ability to handle security incident effectively and efficiently, we consider it important to devise suitable automated support solutions to address these challenges. We propose a novel learning-based framework for automated security tool API R ecommendation for security O rchestration, automation, and response, APIRO . To mitigate data availability constraint, APIRO enriches security tool API description by applying a wide variety of data augmentation techniques. To learn data heterogeneity of the security tools and semantic variation in API descriptions, APIRO consists of an API-specific word embedding model and a Convolutional Neural Network (CNN) model that are used for prediction of top three relevant APIs for a task. We experimentally demonstrate the effectiveness of APIRO in recommending APIs for different tasks using three security tools and 36 augmentation techniques. Our experimental results demonstrate the feasibility of APIRO for achieving 91.9% Top-1 Accuracy. Compared to the state-of-the-art baseline, APIRO is 26.93%, 23.03%, and 20.87% improved in terms of Top-1, Top-2, and Top-3 Accuracy and outperforms the baseline by 23.7% in terms of Mean Reciprocal Rank (MRR).
The <scp>SemIoTic</scp> Ecosystem: A Semantic Bridge between IoT Devices and Smart Spaces

Smart space administration and application development is challenging in part due to the semantic gap that exists between the high-level requirements of users and the low-level capabilities of IoT devices. The stakeholders in a smart space are required to deal with communicating with specific IoT devices, capturing data, processing it, and abstracting it out to generate useful inferences. Additionally, this makes reusability of smart space applications difficult, since they are developed for specific sensor deployments. In this article, we present a holistic approach to IoT smart spaces, the SemIoTic ecosystem, to facilitate application development, space management, and service provision to its inhabitants. The ecosystem is based on a centralized repository, where developers can advertise their space-agnostic applications, and a SemIoTic system deployed in each smart space that interacts with those applications to provide them with the required information. SemIoTic applications are developed using a metamodel that defines high-level concepts abstracted from the smart space about the space itself and the people within it. Application requirements can be expressed then in terms of user-friendly high-level concepts, which are automatically translated by SemIoTic into sensor/actuator commands adapted to the underlying device deployment in each space. We present a reference implementation of the ecosystem that has been deployed at the University of California, Irvine and is abstracting data from hundreds of sensors in the space and providing applications to campus members.
Benchmarking of quantum protocols

Quantum network protocols offer new functionalities such as enhanced security to communication and computational systems. Despite the rapid progress in quantum hardware, it has not yet reached a level of maturity that enables execution of many quantum protocols in practical settings. To develop quantum protocols in real world, it is necessary to examine their performance considering the imperfections in their practical implementation using simulation platforms. In this paper, we consider several quantum protocols that enable promising functionalities and services in near-future quantum networks. The protocols are chosen from both areas of quantum communication and quantum computation as follows: quantum money, W-state based anonymous transmission, verifiable blind quantum computation, and quantum digital signature. We use NetSquid simulation platform to evaluate the effect of various sources of noise on the performance of these protocols, considering different figures of merit. We find that to enable quantum money protocol, the decoherence time constant of the quantum memory must be at least three times the storage time of qubits. Furthermore, our simulation results for the w-state based anonymous transmission protocol show that to achieve an average fidelity above 0.8 in this protocol, the storage time of sender's and receiver's particles in the quantum memory must be less than half of the decoherence time constant of the quantum memory. We have also investigated the effect of gate imperfections on the performance of verifiable blind quantum computation. We find that with our chosen parameters, if the depolarizing probability of quantum gates is equal to or greater than 0.05, the security of the protocol cannot be guaranteed. Lastly, our simulation results for quantum digital signature protocol show that channel loss has a significant effect on the probability of repudiation.
Three-dimensional volume reconstruction from multi-slice data using a shape transformation

We present a computational method for the 3D volume reconstruction from cross-sectional data. The proposed method is based on the Allen–Cahn (AC) equation with a source term. The source term is related to shape transformation from a source object to a target object. Using the operator splitting method, the governing equation is solved by splitting it into three steps. The numerical solution is obtained explicitly using the Euler's methods and the separation of variables. To reconstruct the 3D object from two slice data, we set one slice as the target data and the other data as the initial data. We solve the governing equation and stack intermediate solutions based on the relative fraction of the symmetric difference of two regions occupied by the target and source data. To demonstrate that the proposed method can reconstruct a 3D model through extracted intermediate slice data during shape transformation, we perform several computational tests. Furthermore, the proposed method is applied to a 3D volume reconstruction from multi-slice data of human vertebra.
Simulating Energy Transfer in Molecular Systems with Digital Quantum Computers

Quantum computers have the potential to simulate chemical systems beyond the capability of classical computers. Recent developments in hybrid quantum-classical approaches enable the determinations of the ground or low energy states of molecular systems. Here, we extend near-term quantum simulations of chemistry to time-dependent processes by simulating energy transfer in organic semiconducting molecules. We developed a multiscale modeling workflow that combines conventional molecular dynamics and quantum chemistry simulations with hybrid variational quantum algorithm to compute the exciton dynamics in both the single excitation subspace (i.e., Frenkel Hamiltonian) and the full-Hilbert space (i.e., multiexciton) regimes. Our numerical examples demonstrate the feasibility of our approach, and simulations on IBM Q devices capture the qualitative behaviors of exciton dynamics, but with considerable errors. We present an error mitigation technique that combines experimental results from the variational and Trotter algorithms, and obtain significantly improved quantum dynamics. Our approach opens up new opportunities for modeling quantum dynamics in chemical, biological, and material systems with quantum computers.
An Improved Structural-Based Ontology Matching Approach Using Similarity Spreading

An increasing number of ontologies demand the interoperability between them in order to gain accurate information. The ontology heterogeneity also makes the interoperability process even more difficult. The existing ontology matching systems are mainly focusing on subject derivatives of the concern domain. Since ontologies are represented as data models in a structured format, in this paper, a new modified model of similarity spreading for ontology mapping is proposed. In this approach, the mapping mainly involves with node clustering based on edge affinity, and then the graph matching is achieved by applying coefficient similarity propagation. This process is carried out by iterative manner, and at the end, the similarity score is calculated for iteration. This model is evaluated in terms of precision, recall, and f-measure parameters, and it is found that it outperforms similar systems.
An Interactive Number Learning Augmented Reality Application for Autistic Preschool Children

Autism in children is characterized by challenges that include conditions like speech and non-verbal disorder, lack of social interactivity, low cognitive skills, and repetitive behavioral regulation. Augmented Reality (AR) is an enhanced version of the real world by overlaying digital elements and this technology is used to build up engagement and motivation to learn for children with autistic disorder. This study designs a prototype of a mobile-based application called NUM09 as a learning tool for autistic pre-school children. This application exploits flashcards as a learning aid by incorporating them with Vuforia and Augmented Reality (AR) technology. The concept includes designing numeral flashcards from 0 to 9 and scanning them through the developed mobile application NUM09 where the targets of each image are identified to play the corresponding video clip according to the scanned number in the card and this helps children to improve their attention to a focused study process to practice number learning. The result of this preliminary study is in form of the design and implementation of Augmented Reality mobile application development as a teaching assistant for autistic deficit children.
DMPNet: densely connected multi-scale pyramid networks for crowd counting

Crowd counting has been widely studied by deep learning in recent years. However, due to scale variation caused by perspective distortion, crowd counting is still a challenging task. In this paper, we propose a Densely Connected Multi-scale Pyramid Network (DMPNet) for count estimation and the generation of high-quality density maps. The key component of our network is the Multi-scale Pyramid Network (MPN), which can extract multi-scale features of the crowd effectively while keeping the resolution of the input feature map and the number of channels unchanged. To increase the information transfer between the network layer, we used dense connections to connect multiple MPNs. In addition, we also designed a novel loss function, which can help our model achieve better convergence. To evaluate our method, we conducted extensive experiments on three challenging benchmark crowd counting datasets. Experimental results show that compared with the state-of-the-art algorithms, DMPNet performs well in both parameters and results. The code is available at: https://github.com/lpfworld/DMPNet.
An anchor-free object detector based on soften optimized bi-directional FPN

We propose an anchor-free object detector that combines a weighted bi-directional Feature Pyramid Network (BiFPN) and Soft Anchor Point Detector to address the object detection problem in a pixel-wise paradigm. The current mainstream object detection methods are anchor-based, which require to set hyper parameters such as scale and aspect ratio. This requires strong prior knowledge and can be difficult to design. Therefore, we propose an anchor-free detector that completely avoids the complex calculations and all the hyper parameters related to the anchor box by eliminating the predefined set of anchor boxes in an anchor-free way. Anchor-free detectors are essentially dense prediction methods. Although the huge solution space can yield high recall, simple anchor-free methods tend to return too many false positives, which leads to the problem of semantic ambiguity caused by the high overlap of object centers. Therefore, we propose BiFPN to alleviate the impact of high overlap which also effectively addresses the problems related to multi-scale features. Moreover, in order to utilize the power of feature pyramid better, we tackle the issues with a novel training strategy that involves two soften optimization techniques, i.e., soft-weighted anchor points and soft-selected pyramid levels. This training strategy further re-weights the quality of the detection results to make our detection results more stable.
Displacement-Invariant Cost Computation for Stereo Matching

Abstract Although deep learning-based methods have dominated stereo matching leaderboards by yielding unprecedented disparity accuracy, their inference time is typically slow, i.e. , less than 4 FPS for a pair of 540p images. The main reason is that the leading methods employ time-consuming 3D convolutions applied to a 4D feature volume. A common way to speed up the computation is to downsample the feature volume, but this loses high-frequency details. To overcome these challenges, we propose a displacement-invariant cost computation module to compute the matching costs without needing a 4D feature volume. Rather, costs are computed by applying the same 2D convolution network on each disparity-shifted feature map pair independently. Unlike previous 2D convolution-based methods that simply perform context mapping between inputs and disparity maps, our proposed approach learns to match features between the two images. We also propose an entropy-based refinement strategy to refine the computed disparity map, which further improves the speed by avoiding the need to compute a second disparity map on the right image. Extensive experiments on standard datasets (SceneFlow, KITTI, ETH3D, and Middlebury) demonstrate that our method achieves competitive accuracy with much less inference time. On typical image sizes ( e.g. , $$540\times 960$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>540</mml:mn> <mml:mo>×</mml:mo> <mml:mn>960</mml:mn> </mml:mrow> </mml:math> ), our method processes over 100 FPS on a desktop GPU, making our method suitable for time-critical applications such as autonomous driving. We also show that our approach generalizes well to unseen datasets, outperforming 4D-volumetric methods. We will release the source code to ensure the reproducibility.
Improving Minimum Cross-Entropy Thresholding for Segmentation of Infected Foregrounds in Medical Images Based on Mean Filters Approaches

Mean-based thresholding methods are among the most popular techniques that are used for images segmentation. Thresholding is a fundamental process for many applications since it provides a good degree of intensity separation of given images. Minimum cross-entropy thresholding (MCET) is one of the widely used mean-based methods for images segmentation; it is based on a classical mean that remains steady and limited value. In this paper, to improve the efficiency of MCET, dedicated mean estimation approaches are proposed to be used with MCET, instead of using the classical mean. The proposed mean estimation approaches, for example, alpha trim, harmonic, contraharmonic, and geometric, tend to exclude the negative impact of the undesired parts from the mean computation process, such as noises, local outliers, and gray intensity levels, and then provide an improvement for the thresholding process that can reflect good segmentation results. The proposed technique adds a profound impact on accurate images segmentation. It can be extended to other applications in object detection. Three data sets of medical images were applied for segmentation in this paper, including magnetic resonance imaging (MRI) Alzheimer's, MRI brain tumor, and skin lesion. The unsupervised and supervised evaluations were used to conduct the efficiency of the proposed method.
WCM‐WTrA: A Cross‐Project Defect Prediction Method Based on Feature Selection and Distance‐Weight Transfer Learning

Cross-project defect prediction is a hot topic in the field of defect prediction. How to reduce the difference between projects and make the model have better accuracy is the core problem. This paper starts from two perspectives: feature selection and distance-weight instance transfer. We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WTrA and multi-source model Multi-WCM-WTrA. We have tested on AEEEM and ReLink datasets, and the results show that our method has an average improvement of 23% compared with TCA+ algorithm on AEEEM datasets, and an average improvement of 5% on ReLink datasets.