Reviewerly/exHarmony huggingface dataset:
Multi-Feature Fusion and Enhancement Single Shot Detector for Traffic Sign Recognition

Road traffic sign detection and recognition play an important role in advanced driver assistance systems (ADAS) by providing real-time road sign perception information. In this paper, we propose an improved (Single Shot Detector) SSD algorithm via multi-feature fusion and enhancement, named MF-SSD, for traffic sign recognition. First, low-level features are fused into high-level features to improve the detection performance of small targets in the SSD. We then enhance the features in different channels to detect the target by enhancing effective channel features and suppressing invalid channel features. Our algorithm gets good results in domestic real-time traffic signs. The proposed MF-SSD algorithm is evaluated with the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The experimental results show that the MF-SSD algorithm has advantages in detecting small traffic signs. Compared with existing methods, it achieves higher detection accuracy, better efficiency, and better robustness in complex traffic environment.
Unveiling the technological trends of augmented reality: A patent analysis

This paper investigates the Augmented Reality (AR) technology with a novel approach based on patent research. We searched the USPTO for AR-related granted patents in the period 1993–2018, we selected and manually browse a total of 2,373, we classified them in five key technological classes i.e., display device, tracking, user interaction, application, and system, and we finally analyzed the results. The main contribution of this paper is the investigation of the technological trends, with outcomes that can be useful for researchers and developers for technical steering, but also for policymakers, managers and entrepreneurs for technology scouting and forecasting. Our study found that AR technological development has especially increased in the last decade. In particular, we evidenced a remarkable steady of 82 % annual growth rate of the number of granted patents after 2012. From geographical distribution, we found that North America is the leader (68 %); Asia (18 %) and Europe (13 %) are lagging behind despite dedicated Industry 4.0 policies actuated by the governments. Another nontrivial result is the incoherency between the owners of a high quantity of patents and those highly impacting. In fact, only Microsoft Corporation and Amazon Technologies are at the same time in the top 10 of the most patent-intensive organizations and the top 10 of highly impacting organizations. Moreover, the majority of the patents are owned by companies, albeit some of the highly impacting ones come from universities or research centers. These findings provide theoretical, managerial, and policy implications for future research activities in the AR domain.
A gradient boosted decision tree-based sentiment classification of twitter data

People communicate their views, arguments and emotions about their everyday life on social media (SM) platforms (e.g. Twitter and Facebook). Twitter stands as an international micro-blogging service that features a brief message called tweets. Freestyle writing, incorrect grammar, typographical errors and abbreviations are some noises that occur in the text. Sentiment analysis (SA) centered on a tweet posted by the user, and also opinion mining (OM) of the customers review is another famous research topic. The texts are gathered from users’ tweets by means of OM and automatic-SA centered on ternary classifications, namely positive, neutral and negative. It is very challenging for the researchers to ascertain sentiments as a result of its limited size, misspells, unstructured nature, abbreviations and slangs for Twitter data. This paper, with the aid of the Gradient Boosted Decision Tree classifier (GBDT), proposes an efficient SA and Sentiment Classification (SC) of Twitter data. Initially, the twitter data undergoes pre-processing. Next, the pre-processed data is processed using HDFS MapReduce. Now, the features are extracted from the processed data, and then efficient features are selected using the Improved Elephant Herd Optimization (I-EHO) technique. Now, score values are calculated for each of those chosen features and given to the classifier. At last, the GBDT classifier classifies the data as negative, positive, or neutral. Experiential results are analyzed and contrasted with the other conventional techniques to show the highest performance of the proposed method.
Smishing Detector: A security model to detect smishing through SMS content analysis and URL behavior analysis

Smartphone’s popularity and their constant connectivity to the World Wide Web have made these devices vulnerable to phishing and smishing attacks. Phishing is a practice of sending malicious emails to users. Smishing is a combined form of SMS and Phishing in which invaders send SMS containing malicious content to the victim. This content sometimes includes links which redirect the user to websites containing malicious applications and user interfaces. Researchers have proposed various methods in past years to detect smishing but still, we lack a method that significantly avoids false-positive results i.e. falsely categorizing a message as malicious when it is genuine. Hence, we have proposed a model called ’Smishing Detector’ to identify smishing messages while reducing false-positive results at every possible step. The proposed method consists of four modules, namely, SMS Content Analyzer, URL Filter, Source Code Analyzer and Apk Download Detector. SMS Content Analyzer analyzes the text message contents. Naive Bayes Classification Algorithm is used to identify the malicious contents and keywords present in the text message. URL Filter inspects the URL to identify malicious features. Source Code Analyzer examines the source code of the website to identify the harmful code embedded in it. Form tag and URL domain present in the source code are also inspected in this module. APK Download Detector identifies whether any malicious file is downloaded while invoking the URL. User consent taken while downloading the file is also inspected in this module. Finally, we have developed a prototype of the proposed system which has been validated with experiments on SMS datasets. In this paper, we have demonstrated the results of each module separately and also we have demonstrated the final results. The results of the experiments show an overall accuracy of 96.29%. We have compared this model with other models proposed by various researchers and we have found that this model covers more security aspects as compared to other models.
Relation Adversarial Network for Low Resource Knowledge Graph Completion

Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficulties for KGC is a low resource problem. Previous approaches assume sufficient training triples to learn versatile vectors for entities and relations, or a satisfactory number of labeled sentences to train a competent relation extraction model. However, low resource relations are very common in KGs, and those newly added relations often do not have many known samples for training. In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available. We propose a general framework called Weighted Relation Adversarial Network, which utilizes an adversarial procedure to help adapt knowledge/features learned from high resource relations to different but related low resource relations. Specifically, the framework takes advantage of a relation discriminator to distinguish between samples from different relations, and help learn relation-invariant features more transferable from source relations to target relations. Experimental results show that the proposed approach outperforms previous methods regarding low resource settings for both link prediction and relation extraction.
Tapping the full potential of the digital revolution for agricultural extension: an emerging innovation agenda

Agricultural extension in the Global South can benefit greatly from the use of modern information and communication technologies (ICT). Yet, despite two decades of promising experiences, this potential is not fully realized. Here, we review the relevant research literature to inform future investments into agricultural information services that harness the full potential of digital media. We describe a recently emerging innovation agenda that is, in part, a response to the eventual failure of many new agro-advisory initiatives. One important cause of failure has been a focus on pushing certain technologies, rather than responding to the particular communication challenges of potential users. To avoid such bias in designing new services, the new innovation agenda rests on two major foundations: strong user-centredness and problem-orientation. In our review, we first describe how user-centred design methods help in specifying both problems and (digital) solutions in agricultural extension. To inform responses to the communication challenges defined by that analysis, we then describe eight emerging aspects of using ICT for development, and how they can address common deficiencies of agricultural extension. Practical examples from the literature highlight the possibilities and limitations of these innovation directions. Beyond digital design, however, technological innovation requires enabling institutions.
Personalized location recommendation by fusing sentimental and spatial context

Internet users would like to obtain interesting location information for a travel. With the rapid development of social media, many kinds of location recommender systems are proposed in recent years. Existing methods mostly focus on mining user check-in information that could be leveraged to understand their trajectories. However, the characteristics and attributes of geographical locations also play an important role in recommender systems. In this paper, sentimental attributes of locations are explored and we propose a Point of Interest (POI) mining method and a personalized recommendation model by fusing sentimental spatial context. First, a Sentimental–Spatial POI Mining (SPM) method is utilized to mine the POIs by fusing the sentimental and geographical attributes of locations. Second, we recommend the POIs to users by a Sentimental–Spatial POI Recommendation (SPR) model incorporating the factors of sentiment similarity and geographical distance. Last, the advantages and superior performance of our methods are demonstrated by extensive experiments on a real-world dataset.
An improved differential evolution algorithm with dual mutation strategies collaboration

To reduce the effect of the selections of mutation strategies and control parameters on the performance of differential evolution (DE), this paper proposes an improved differential evolution algorithm with dual mutation strategies collaboration (DMCDE), in which two main improvements are presented. First, DMCDE introduces an elite guidance mechanism to propose two new variants of the classical DE/rand/2 and DE/best/2 mutation strategies, which we call DE/e-rand/2 and DE/e-best/2 respectively. They use the individuals randomly chosen from superior elite population as the base vector and the first vector of difference vectors, thereby providing clearer guidance for individual mutation without losing randomness. Second, a mechanism of dual mutation strategies collaboration is utilized to obtain a trade-off between global exploration and local exploitation of the algorithm. The performance of DMCDE is evaluated by using the commonly used test functions as well as a real-world optimization problem. The results show that DMCDE can significantly improve the optimization performance of DE, and is superior to the comparative competitors.
Blockchain and federated learning-based distributed computing defence framework for sustainable society

Ensuring social security through the defense organization determines the creation of links between the army and society. Realizing the benefits of the Internet of Battle Things in the defense system can perfectly monetize intelligence and strengthen the armed forces. It establishes a network for strong connectivity in the army with good coordination between complex processes to effectively edge out the enemies. However, this new technology poses organizational and national security challenges that present both opportunities and obstacles. The current framework of the defense IoT network for sustainable society is not adequate enough to make actionable situational awareness decisions in order to infer the state of the battlefield while preserving the privacy of sensitive data. In this paper, we propose a distributed computing defence framework for sustainable society using the features of blockchain technology and federated learning. The proposed model presents an algorithm to meet the challenges of limited training data in order to obtain high accuracy and avoid a reason specific model. To evaluate the effectiveness of the proposed model, we prepare the dataset and investigate the performance of our framework in various scenarios. The result outcomes are promising in terms of accuracy and loss compared to baseline approach.
Strong Mixed-Integer Programming Formulations for Trained Neural Networks

We present an ideal mixed-integer programming (MIP) formulation for a rectified linear unit (ReLU) appearing in a trained neural network. Our formulation requires a single binary variable and no additional continuous variables beyond the input and output variables of the ReLU. We contrast it with an ideal "extended" formulation with a linear number of additional continuous variables, derived through standard techniques. An apparent drawback of our formulation is that it requires an exponential number of inequality constraints, but we provide a routine to separate the inequalities in linear time. We also prove that these exponentially-many constraints are facet-defining under mild conditions. Finally, we study network verification problems and observe that dynamically separating from the exponential inequalities (1) is much more computationally efficient and scalable than the extended formulation, (2) decreases the solve time of a state-of-the-art MIP solver by a factor of 7 on smaller instances, and (3) nearly matches the dual bounds of a state-of-the-art MIP solver on harder instances, after just a few rounds of separation and in orders of magnitude less time.
A quantum-behaved simulated annealing algorithm-based moth-flame optimization method

This study develops an improved moth-flame optimization algorithm, which is a recently proposed optimizer based on moth behavior in nature. It has achieved favorable results in medical science, educational evaluation, and other fields. However, the convergence rate of the original moth-flame optimization algorithm is too fast in the running process, and it is prone to fall into local optimum, which leads to the failure to produce the high-quality optimal result. Accordingly, this paper proposes a reinforced technique for the moth-flame optimization algorithm. Firstly, the simulated annealing strategy is introduced into the moth-flame optimization algorithm to boost the advantage of the algorithm in the local exploitation process. Then, the idea of the quantum rotation gate is integrated to enhance the global exploration ability of the algorithm and ameliorate the diversity of the moth. These two steps maintain the relationship between exploitation and exploration as well as strengthen the performance of the algorithm in both phases. After that, the method is compared with ten well-regarded and ten alternative algorithms on benchmark functions to verify the effectiveness of the approach. Also, the Wilcoxon signed rank and Friedman assessment were performed to verify the significance of the proposed method against other counterparts. The simulation results reveal that the two introduced strategies significantly improve the exploration and exploitation capacity of moth-flame optimization algorithm. Finally, the algorithm is utilized to feature selection and two engineering problems, including pressure vessel design and multiple disk clutch brake problems. In these practical applications, the novel algorithm also achieves particularly notable results, which also illustrates that the algorithm is qualified is an effective auxiliary appliance in solving complex optimization problems.
Text-based Person Search via Attribute-aided Matching

Text-based person search aims to retrieve the pedestrian images that best match a given text query. Existing methods utilize class-id information to get discriminative and identity-preserving features. However, it is not well-explored whether it is beneficial to explicitly ensure that the semantics of the data are retained. In the proposed work, we aim to create semantics-preserving embeddings through an additional task of attribute prediction. Since attribute annotation is typically unavailable in text-based person search, we first mine them from the text corpus. These attributes are then used as a means to bridge the modality gap between the image-text inputs, as well as to improve the representation learning. In summary, we propose an approach for text-based person search by learning an attribute-driven space along with a class-information driven space, and utilize both for obtaining the retrieval results. Our experiments on benchmark dataset, CUHK-PEDES, show that learning the attribute-space not only helps in improving performance, giving us state-of-the-art Rank-1 accuracy of 56.68%, but also yields humanly-interpretable features.
Review of automatic text summarization techniques &amp; methods

Text summarization automatically produces a summary containing important sentences and includes all relevant important information from the original document. One of the main approaches, when viewed from the summary results, are extractive and abstractive. An extractive summary is heading towards maturity and now research has shifted towards abstractive summation and real-time summarization. Although there have been so many achievements in the acquisition of datasets, methods, and techniques published, there are not many papers that can provide a broad picture of the current state of research in this field. This paper provides a broad and systematic review of research in the field of text summarization published from 2008 to 2019. There are 85 journal and conference publications which are the results of the extraction of selected studies for identification and analysis to describe research topics/trends, datasets, preprocessing, features, techniques, methods, evaluations, and problems in this field of research. The results of the analysis provide an in-depth explanation of the topics/trends that are the focus of their research in the field of text summarization; provide references to public datasets, preprocessing and features that have been used; describes the techniques and methods that are often used by researchers as a comparison and means for developing methods. At the end of this paper, several recommendations for opportunities and challenges related to text summarization research are mentioned.
Multimodal Learning Analytics research with young children: A systematic review

Abstract Learning Analytics and Multimodal Learning Analytics are changing the way of analysing the learning process while students interact with an educational content. This paper presents a systematic literature review aimed at describing practices in recent Multimodal Learning Analytics and Learning Analytics research literature in order to identify tools and strategies useful for the assessment of the progress and behaviour of children under 6 years old in respect of their learning. The purpose is to provide guidance for Multimodal Learning Analytics research with children under 6 years old to assess their engagement in a task, their emotions, attention, understanding and achievement of a goal. The current state of knowledge on Multimodal Learning Analytics research suggests how performance analytics, face and speech recognition systems, eye tracking, Kinect analytics and wristbands could be used with children. The results show the complexity of collecting data using non‐invasive methodologies with children under 6 years old. Ethical implications related to multimodal data from audio, visual, biometric and quantitative measures of child behaviour are discussed.
Exploring key indicators of social identity in the #MeToo era: Using discourse analysis in UGC

Recent years have been characterized by the ubiquitous use of social networks as a mean of self and social identity, which offers new opportunities for qualitative and quantitative research in social sciences. The dynamics of interactions on social platforms such as Twitter promote the development of social movements around hashtags, such as #MeToo. According to previous research, this movement has set the beginning of an era. The present study aims to determine the key indicators of social identity in the #MeToo movement in Twitter using textual analysis and sentiment analysis of user-generated content. To this end, we use a cognitive pragmatics point of view to study a corpus of 31.305 tweets. Using the methodological approaches of corpus linguistics (CL) and discourse analysis (DA), we identify keywords, topics, frequency, and n-grams or collocations to understand the social identity of the #MeToo movement. The key indicators of the social identity in the #MeToo Era are validated using association statistical measures of Log-Likelihood and Mutual Information (MI). Our results reveal the polarization of sentiments where UGC is associated with both negative and positive topics. The social identity is particularly strongly correlated with women and the workplace. Finally, regardless the industry or area, these results present a holistic approach to the social identity of #MeToo.
Fully convolutional attention network for biomedical image segmentation

In this paper, we embed two types of attention modules in the dilated fully convolutional network (FCN) to solve biomedical image segmentation tasks efficiently and accurately. Different from previous work on image segmentation through multiscale feature fusion, we propose the fully convolutional attention network (FCANet) to aggregate contextual information at long-range and short-range distances. Specifically, we add two types of attention modules, the spatial attention module and the channel attention module, to the Res2Net network, which has a dilated strategy. The features of each location are aggregated through the spatial attention module, so that similar features promote each other in space size. At the same time, the channel attention module treats each channel of the feature map as a feature detector and emphasizes the channel dependency between any two channel maps. Finally, we weight the sum of the output features of the two types of attention modules to retain the feature information of the long-range and short-range distances, to further improve the representation of the features and make the biomedical image segmentation more accurate. In particular, we verify that the proposed attention module can seamlessly connect to any end-to-end network with minimal overhead. We perform comprehensive experiments on three public biomedical image segmentation datasets, i.e., the Chest X-ray collection, the Kaggle 2018 data science bowl and the Herlev dataset. The experimental results show that FCANet can improve the segmentation effect of biomedical images. The source code models are available at https://github.com/luhongchun/FCANet