Reviewerly/exHarmony huggingface dataset:
Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning

Natural language inference on tabular data is a challenging task.Existing approaches lack the world and common sense knowledge required to perform at a human level.While massive amounts of KG data exist, approaches to integrate them with deep learning models to enhance tabular reasoning are uncommon.In this paper, we investigate a new approach using BiLSTMs to incorporate knowledge effectively into language models.Through extensive analysis, we show that our proposed architecture, Trans-KBLSTM improves the benchmark performance on INFOTABS , a tabular NLI dataset.
Fast and Low-Drift Visual Odometry With Improved RANSAC-Based Outlier Removal Scheme for Intelligent Vehicles

Visual odometry estimates the ego-motion of a vehicle using only the input of a single or multiple cameras mounted on the vehicle.This paper focuses on the research of the stereo visual odometry system of intelligent vehicles, and discusses how to improve the robustness, accuracy and efficiency.A new robust estimation algorithm, Locally Optimized Progressive Sample Consensus algorithm, is proposed.Compared with the RANSAC algorithm, it can not only improve the accuracy of model estimation, but also can find more inliers to terminate the iteration process in advance, thereby speeding up the algorithm.A decoupling-based motion estimation algorithm is proposed.Monocular method is used to estimate the rotation parameters, which eliminates the influence of mismatching between left and right frames on rotation estimation.Moreover, when estimating the translational motion, the decoupling method makes the normalized re-projection error criterion better distinguish between inliers and outliers.The performance of the method is evaluated on KITTI benchmark dataset by comparing it with the existing visual odometry systems.The experimental results show that the proposed technique has a high accuracy and efficiency.
Biomedical NER using Novel Schema and Distant Supervision

Biomedical Named Entity Recognition (BM-NER) is one of the most important tasks in the field of biomedical text mining.Most work so far on this task has not focused on identification of discontinuous and overlapping entities, even though they are present in significant fractions in real-life biomedical datasets.In this paper, we introduce a novel annotation schema to capture complex entities, and explore the effects of distant supervision on our deep-learning sequence labelling model.For BMNER task, our annotation schema outperforms other BIO-based annotation schemes on the same model.We also achieve higher F1scores than state-of-the-art models on multiple corpora without fine-tuning embeddings, highlighting the efficacy of neural feature extraction using our model.
An Intelligent Patrol System Based on Edge Data Center Station

A centralized intelligent patrol system based on edge data center stations is proposed. By building new digital infrastructures such as edge data center stations, the centralized management of brainy patrols of plant stations in various industry scenarios can be realized. The online intelligent patrol level of each plant and station is improved, and the work pressure of grassroots operation and maintenance personnel is reduced. This kind of system has lower cost, higher deployment efficiency, and better inspection task management than the independent deployment of inspection equipment at the factory site. The system gives full play to the advantages of edge computing technology, improves the level of centralized operation and management of plants and stations, and provides a new inspection mode for the construction of intelligent plant and station management systems.
Cyberbullying Detection with Side Information: A Real-World Application of COVID-19 News Comment in Chinese Language

Cyberbullying is an aggressive and intentional behavior committed by groups or individuals, and its main manifestation is to make offensive or hurtful comments on social media. The existing researches on cyberbullying detection underuse natural language processing technology, and is only limited to extracting the features of comment content. Meanwhile, the existing datasets for cyberbullying detection are non-standard, unbalanced, and the data content of datasets is relatively outdated. In this paper, we propose a novel Hybrid deep Model based on Multi-feature Fusion (HMMF), which can model the content of news comments and the side information related to net users and comments simultaneously, to improve the performance of cyberbullying detection. In addition, we present the JRTT: a new, publicly available benchmark dataset for cyberbullying detection. All the data are collected from social media platforms which contains Chinese comments on COVID-19 news. To evaluate the effectiveness of HMMF, we conduct extensive experiments on JRTT dataset with five existing pre-trained language models. Experimental results and analyses show that HMMF achieves state-of-the-art performances on cyberbullying detection. To facilitate research in this direction, we release the dataset and the project code at https://github.com/xingjian215/HMMF .
Identification of Experts in the Security Field Based on the Hypernet S-edgeRank Algorithm

In recent years, national security has become increasingly important. However, since most security fields are still in their infancy and expert resources are scarce, governments and enterprises often fall into the predicament of not being able to effectively select experts and the evaluation system for experts is not scientific and objective. This paper draws on the PageRank algorithm, improves the transition matrix M and the damping coefficient d, and improves to the S-edgeRank algorithm, which is more suitable for the hypernet, which is a complex network, to calculate various metrics of the hypernet. Crawl CSSCI security-related papers, and build a hypernet with four layers of subnets of citation subnet, domain subnet, and keyword subnet. The S-edgeRank algorithm is used to rank experts and scholars in the thesis database, so as to effectively select experts in related fields, and provide a reference for more accurate and objective evaluation of the ability of experts and scholars in the follow-up.
Blockchain-Based “APP+Applet” Mobile Government Service Models

With blockchain, the “APP+Applet” paradigm will be able to overcome technical barriers such as data security and secrecy, as well as information exchange. Through cross-system user identity authentication and other techniques, it achieves interoperability and integration of public service matters in the realms of government, business, and social organizations. The government APP can use the commercial APP’s social, business, and entertainment users to promote government services in the form of mini programs to the clients of the social, business, and entertainment apps. They can also use the applet as a bridge to attract additional users and sustain user stickiness with the resource advantages of government APPs for social, business, and entertainment applications. This would fundamentally deconstruct the old online government service model and promote the merger of government, public, and corporate services.
Light Field Image Super-Resolution Based on Multilevel Structures

Light field (LF) images suffer from low spatial resolution due to the trade-off between angular and spatial resolutions. Thus, spatial super-resolution (SR) of LF images is an essential task to obtain high-quality LF images. However, the existing SR networks still have limitations, since they exploit only single-level features to use sub-pixel information in LF images. In this paper, we proposed a light field super-resolution (LFSR) network to effectively improve the spatial resolution of light field images. The proposed network takes one target image and its 8-neighboring images for references. We construct multi-level structures for the proposed network to effectively estimate and mix sub-pixel information in reference images. The proposed network is composed of a feature extractor, a feature warping module, a feature mixing module, and a upscaling module. The feature extractor provides multi-level features for SR and offsets to the feature warping module to obtain aligned features for multiple reference images. The feature mixing module mixes multiple aligned features based on the similarity between the target and reference images to obtain multi-level mixed features. Finally, the upscaling module generates a high-resolution residual image using the multi-level mixed features. Experimental results demonstrate the proposed network outperforms the state-of-the-art methods on various light field datasets. The pre-trained model and source codes are available at https://github.com/Hwa-Jong/LF_MLS.
Attack on AI Smart Speakers with a Laser Beam

Smart speakers and virtual assistants seem so unremarkable that, at times, we lose sight of their role in managing a smart home, as well as how much personal data they store. For this reason, hackers view such devices as entry points into people’s lives, through which they can gain access to personal data, eavesdrop on conversations, and perform other malicious actions without the user’s knowledge. With the help of a laser pointer, it is now easy to hack devices with Google Home, Alexa, and Siri, which are at a great distance, by pointing a laser pointer or even a flashlight at their microphones. If you focus the laser with a lens, you can hack into voice assistants located at a distance, in our case, up to 87 m. Thus, a person with bad intentions can easily make purchases on the Internet and unlock a door protected by a smart lock. This movement can be simulated by aiming a laser or flashlight at a diaphragm, which converts light into electrical signals. As a result, the system will react to light in the same way as to sound.
A Briefed Review on Phishing Attacks and Detection Approaches

Phishing is contemplated to be one of the most deadliest threats to internet users and still continues to grow in an exponential rate. This has resulted the hackers to generate more creative attacks and execute them on a large scale. The working concept of a phishing attack is to create the exact replica of an original site and direct users to land on the sites page. Due to the sites page being fake and similar to the original one, legit users are generally fooled to perform activities on such pages. Phishing is an attack that is mostly executed by combining fundamentals of social engineering with evolving technical methods. This cloaking of the fake site to function as the real one convinces the user to reveal their personal details such as their bank accounts and respective passwords. Hence, in recent times a comprehensive study on past attacks is mandatory to prepare ourselves from being a victim to such threats. This research paper puts forward a brief discussion on previous and existing attacks to provide a better understanding on the working principles of such threats s to encourage the development of anti-phishing techniques in the future. The primary objective of the presented work is to generate awareness amongst individuals and educate them from such attacks. Further, this review aims to assist policy makers and software developers in making right decisions and creating a virus free environment.
Modular Framework for Constructing IoT-Server AKE in Post-Quantum Setting

Authenticated key exchange (AKE) allows two parties to authenticate each other and establish a secret session key to build a secure channel, and it has been well studied in the literature. With the approaching of quantum computers, designing post-quantum secure AKE schemes becomes an urgent task. Today, several KE schemes have been proposed while only a small number of AKE schemes exist. Very few of them have considered resource-constrained participants like IoT devices. In this paper, assuming a typical client-server setting where the client is an IoT device, we propose a modular framework that converts any post-quantum secure KE scheme into a post-quantum secure AKE scheme. Equipped with two authentication factors, the resulted AKE scheme provides a number of security guarantees including Perfect forward secrecy (PFS), Key compromise impersonation (KCI) resilience and Server compromise impersonation (SCI) resilience. We further instantiate the framework by selecting the most efficient KE scheme, namely NewHope Compact, and implement the scheme with some optimisation techniques and conduct relevant analysis and comparisons. In a nutshell, the computational time of the server side is 1.11 ms on a PC and 6.22 ms of the IoT device simulated on Raspberry Pi 3B+, and it seems to be efficient enough for most IoT application scenarios.
Stabilization of a Fuzzy Controller Using an Interval Type-2 Fuzzy System Designed with the Bee Colony Optimization Algorithm

A Bee Colony Optimization algorithm (BCO) for stabilization of a benchmark controller is presented. The main goal of BCO is finding the optimal design of the Membership Functions (MFs) in the Interval Type-2 Fuzzy Logic System (IT2FLS). The results demonstrated that the BCO algorithm shows good stabilization when a IT2FLS is evaluated in the Fuzzy Logic Controller (FLC). A comparative analysis is implemented to demonstrated and verified that BCO algorithm presents excellent results in comparison with the Type-1 Fuzzy Systems (T1FLS).
Research on the User Experience of Affordance of the Cube Game Interface Design

Revisiting the classic, the Tetris game was once a leader in handheld games and a well-known. The advancement of digitization has changed the way people entertain and live, and the wide application of mobile terminal devices has enriched and diversified game content and forms. This study focuses on affordance visual perception to explore the user experience of Tetris game iterative development in new media communication. A 2 × 3 mixed factorial experimental design was applied to help comprehend whether different Gender (i.e., Male and Female) and Operation Mode (i.e., Single, Double, and Multiple) may influence users’ task performance and subjective evaluation. Using the convenience sampling method, a total of 12 participants were recruited to take part in the experiment. The results show that: (1) There is a significant difference in task performance among the three operation modes, the participants spent time the least with the “Single” and “Double” types, especially the “Single” type. (2) There is a significant difference in subjective evaluation among the three operating modes, the total score result shows that participants think the “Single” type and “Double” are easy to use and has the highest evaluation, especially the “Double” type. (3) Participants of different genders have different perceptions of casual puzzle games, and female players prefer games that are simple and easy to operate and have beautiful graphics.
Anomaly Detection in Float-Zone Crystal Growth of Silicon

Float-Zone (FZ) crystal growth can manufacture silicon crystal with high purity and low oxygen concentration. However, due to the limited capability of FZ machines, oxidation contamination cannot be avoided, and an oxide layer sometimes would form on unmelted polycrystalline silicon surface. Oxide layers, so-called watermark, would negatively affect the crystal quality. Consequently, it is desirable to have an automated watermark detection in order to take corrective action in the early stages of the process. This paper proposed a complete framework for watermark detection on FZ images, including pre-processing, feature selection and classification. The results show it can act as a promising quality assurance tool for the FZ process.
Sentiment Analysis: A Recent Survey with Applications and a Proposed Ensemble Algorithm

With the dramatic increase in the massive amount of data on e-commercial sites, it becomes difficult to analyze the sentiment manually. Sentiment analysis automatically analyzes the user sentiment in terms of positive, negative, or neutral using natural language processing. This paper presents a survey on sentiment analysis with its various approaches and algorithms in detail based on supervised and unsupervised learning. Further, this paper analyzes the pros and cons of some current studies with their approaches and compares their results on various datasets. Also, this paper discusses the uses of sentiment analysis in real life. Finally, this paper proposes a new hybrid algorithm carrying various features for sentiment analysis using AdaBoost and majority voting ensemble techniques.
Interpretable Machine Learning Assessment

With the surge of machine learning in AI and data science, it remains an urgent challenge to compare the performance of different machine learning methods under different datasets in an explainable approach. In this study, we propose an interpretable diagnostic index: d-index to tackle the challenge by overcoming the weakness of the traditional evaluation in classification. Unlike traditional measures, d-index describes machine learning performance by synthesizing classification effectiveness from different perspectives. It demonstrates an explainable, rigorous, and sensitive machine learning performance assessment and makes model selection more accurate and robust. Unlike biased or non-explainable classic metrics in imbalanced learning, it provides explainable and sensitive imbalanced learning evaluation and sheds new light on deciphering imbalanced learning behaviors. Furthermore, the proposed d-index demonstrates effectiveness in interpreting and analyzing the learning behaviors of different machine learning models by integrating with new concepts: breakeven states and imbalanced points proposed in this work. In addition, it contributes to identifying the learning singularities that are non-deterministic imbalanced (NDI) learning problems failing almost all existing machine learning models and imbalanced handling techniques. This study also unveils different imbalanced point generation mechanisms in machine learning using d-index and proposes a novel technique to find priori informative kernels to achieve good d-index values with the least number of support vectors for support vector machine classification. To the best of our knowledge, it is the first work in explainable machine learning assessment and will inspire more future work in this study.
MESR: Multistage Enhancement Network for Image Super-Resolution

Recently, the deep-learning-based image super-resolution methods have achieved astounding advancement.Whereas most of these methods utilize features from the low-resolution image space exclusively, and ignore the dependency between contextual features simultaneously, resulting in their limited ability to restore details.To this end, a multi-stage enhancement image network for super-resolution (MESR) is proposed.The network consists of two stages, where the first stage is used to generate a coarse reconstructed image, and the second one is to refine the coarse image, which enhances the super-resolution performance.Specifically, in the first stage, to acquire more abundant features, an effective funnel-like multi-scale feature extractor is proposed, incorporating a channel attention mechanism to boost the feature representation capability.Moreover, an adaptive weighted residual feature fusion block is designed to effectively explore and exploit the dependency between contextual features for generating more beneficial features.In the second stage, a refinement block is proposed to additionally strengthen the details of the reconstructed image by exploring the feature information from the high-resolution image space.Experimental results demonstrate that the proposed method achieves superior performance against the state-of-the-art SR methods in terms of both subjective visual quality and objective quantitative metrics.
User-Level Handover Decision Making Based on Machine Learning Approaches

This letter covers a broad comparison of methods for classification and regression applications for a user-level handover decision making in scenarios with adverse propagation conditions involving buildings, coverage holes, and shadowing effects. The simulation campaigns are based on network simulator ns-3. The comparison encompasses classical machine learning approaches, such as KNN, SVM, and neural networks, but also state-of-the-art fuzzy logic systems and latter boosting machines. The results indicate that SVM and MLP are the most suitable for the classification of the best handover target, although fuzzy system SOFL can perform similarly with lower processing time. Additionally, for the download time estimation, LightGBM provides the smallest error with short processing time, even in hard propagation scenarios.
Predicting visual attention in graphic design documents

We present a model for predicting visual attention during the free viewing of graphic design documents. While existing works on this topic have aimed at predicting static saliency of graphic designs, our work is the first attempt to predict both spatial attention and dynamic temporal order in which the document regions are fixated by gaze using a deep learning based model. We propose a two-stage model for predicting dynamic attention on such documents, with webpages being our primary choice of document design for demonstration. In the first stage, we predict the saliency maps for each of the document components (e.g. logos, banners, texts, etc. for webpages) conditioned on the type of document layout. These component saliency maps are then jointly used to predict the overall document saliency. In the second stage, we use these layout-specific component saliency maps as the state representation for an inverse reinforcement learning model of fixation scanpath prediction during document viewing. To test our model, we collected a new dataset consisting of eye movements from 41 people freely viewing 450 webpages (the largest dataset of its kind). Experimental results show that our model outperforms existing models in both saliency and scanpath prediction for webpages, and also generalizes very well to other graphic design documents such as comics, posters, mobile UIs, etc. and natural images.
Application of Support Vector Machine and Convolutional Neural Network for Sentence-Level Sentiment Analysis of Companies Products Review

The longevity of any company’s sustainability is largely dependent on its’ capability to satisfy customers’ needs. Hence, many industrial analysts and researchers have acknowledged the impact of customers’ reviews, which is the most common approach and tool used to ascertain the level of satisfaction or dissatisfaction of. To determine the sentiment polarity and classification of product reviews, several authors have employed different tools with varying degrees of accuracy when compared to other models. In this study, a hybridized CNN-SVM algorithm model was developed for sentence-level sentiment analysis on musical products reviews on Amazon. A total of 44,463 training samples and 19,056 samples were used for testing and validating the model. Based on the performance metric of the hybrid models, CNN had an accuracy of 85.38%, a precision of 90.56%, a recall of 95.14%, and an AUC of 0.836, whereas, SVM had an accuracy of 85.74%, a precision of 85.62%, a recall of 100%, and an AUC of 0.5. It was observed that there are more positive sentiments and satisfaction obtained from these products as against the negative sentiments. Hence, the level of satisfaction on these products reflected on the analyzed sentiments.