Reviewerly/exHarmony huggingface dataset:
Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition

Deep neural networks such as BERT have made great progress in relation classification. Although they can achieve good performance, it is still a question of concern whether these models recognize the directionality of relations, especially when they may lack interpretability. To explore the question, a novel evaluation task, called Relation Direction Recognition (RDR), is proposed to explore whether models learn the directionality of relations. Three metrics for RDR are introduced to measure the degree to which models recognize the directionality of relations. Several state-of-the-art models are evaluated on RDR. Experimental results on a real-world dataset empirically indicate that there are clear gaps among them in recognizing the directionality of relations, even though these models obtain similar performance in the traditional metric (i.e. Macro-F1). Finally, some suggestions are discussed to enhance models to recognize the directionality of relations from the perspective of model design or training.
Building a Model of Signature Verification and Identification System Based on Deep CNN (ConvNet)

Signatures are commonly used as a unique way of identifying and verifying a person’s identity. Legal documents like bank cheques and transactions, government documents, and affidavits require signature-based verification to identify the authenticity of the person. The signature identification and verification system help to distinguish whether an input signature is honest or a fake. For very long years, this was considered to be a challenging mission when the offline mode was taken into consideration. The offline mode uses scanned signature images, where the online mode of signing process isn't existing. To overcome the signature verification challenge, an application of deep learning techniques to identify feature representations from signature images can be leveraged. In this paper, we have demonstrated how to identify valid and invalid signatures by building a signature verification and identification system that uses deep convolution neural network (ConvNet).
DP-BERT: Dynamic Programming BERT for Text Summarization

Extractive summarization aims to extract sentences containing critical information from the original text, one of the mainstream methods for summarization. Generally, extractive summarization is regarded as a sentence binary classification task in many works. Still, the positive samples selected by these methods are incomplete, and the negative samples are composed of random single sentences, which leads to unsatisfactory classification results and incomplete abstract sentences. To address this issue, we propose a Dynamic Programming BERT (DP-BERT), which can dynamically select the positive example with the closest meaning of the reference abstract and adjusts the corresponding negative samples. Specifically, we design a selector responsible for the dynamic selection of positive and negative samples and then utilize the BERT pre-training model to fine-tune the sentence classifier. Extensive experiments show that DP-BERT can better extract the original text’s key sentences and achieve state-of-the-art performance on two widely-used benchmarks.
IGS-Net: Seeking Good Correspondences via Interactive Generative Structure Learning

Feature matching, which aims to seek good correspondences from an image pair of the same or similar scene, is one of the important studies on digital remote sensing (RS) image processing. However, alongside the common degradation problems, such as geometric distortion, RS images also often face nonlinear radiation distortions, thereby posing more complex matching patterns. To reduce the cost of establishing reliable correspondences, this article proposes a simple, but effective end-to-end hierarchical learning framework, termed interactive generative structure learning network (IGS-Net). The key thinking of our approach is to offer a structure self-generate learning mechanism, called interactive generative structure learning (IGSL) block, for modeling the local context information of potential correspondences. Specifically, IGSL contains two novel operations: adaptive structure-aware representation (ASR) and physical constraint embedding. Besides, we introduce a coarse-to-fine geometry estimation pipeline aligning two sets of feature points to weaken the degree of randomness in matching patterns, thus improving the generalization ability of representation learning. Overall, this differentiable representation learning architecture can be inserted into existing classification models easily for robust outlier detection and removal. In order to demonstrate that our IGS-Net can boost the baselines, we intensively experiment on both single modality and multimodal RS image datasets. The large amounts of experiment results reveal that the matching performances of IGS-Net are significantly improved over eight state-of-the-art competitors.
Exergaming: The Impact of Virtual Reality on Cognitive Performance and Player Experience

Exergames can increase cognitive performance compared to sedentary games and the same non-gamified exercise. Whether this advantage can be further enhanced by the high immersion and arousal level VR systems offer remains unclear. Therefore, we developed an exergame that can be played in VR or in front of a TV screen with identical gameplay in both versions. In a study with 32 participants, we collected data on cognitive performance, arousal, presence, motivation, and exertion. Our results show no difference in cognitive performance and arousal between the conditions. However, the VR version significantly increased players' presence and motivation while significantly reducing perceived exertion. Our results help to understand in which aspects VR can have a significant impact on exergames.
A Semi-Supervised Learning Method to Remedy the Lack of Labeled Data

Contrastive learning has been attracting increasing attention in various research domains recently. There have been some works studying the effectiveness of representation learned by contrastive learning on ImageNet which gives good results for many other vision problems. However, studying the performance of representation learned by contrastive learning on other datasets has not been analysed deeply and systematically. In this study, we conduct comprehensive experiments to fill this important gap and combine those results with the analysis of distribution distance relationship between datasets. We Figure out a negative correlation between the distribution distance and the effectiveness of representation. These observations motivate us to introduce a new semi-supervised framework called Self-Contrastively Supervised Learning (SelfCSL) that uses data from the same domain of the current problem to build a pre-trained model via contrastive learning. By using the data source of the problem itself, the learned pre-trained model has unique characteristics for the problem, thereby increasing efficiency and stability. To test the method, we performed an evaluation on the MedMNIST dataset - a set of 10 pre-processed medical open datasets. The proposed method offers higher classification AUC compared to the model initialized by ImageNet in 5/10 datasets and higher stability in 9/10 datasets.
Work of Fiction Interpretation: Corpus Approach

The research aims to determine an optimal method for interpreting works of fiction in terms of reliability within the framework of the corpus approach. The scientific originality lies in the fact that the main provisions of the universal method are formulated, which allows extracting data for modelling the spatial-temporal and qualitative structures of a work, and ascertaining the features of the author’s idiostyle. The results obtained have shown that working with unannotated linguistic corpora can be effective when using modern software tools for natural language processing.
LEMABE: a novel framework to improve analogy-based software cost estimation using learnable evolution model

One of the most important and critical factors in software projects is the proper cost estimation. This activity, which has to be done prior to the beginning of a project in the initial stage, always encounters several challenges and problems. However, due to the high significance and impact of the proper cost estimation, several approaches and methods have been proposed regarding how to perform cost estimation, in which the analogy-based approach is one of the most popular ones. In recent years, many attempts have been made to employ suitable techniques and methods in this approach in order to improve estimation accuracy. However, achieving improved estimation accuracy in these techniques is still an appropriate research topic. To improve software development cost estimation, the current study has investigated the effect of the LEM algorithm on optimization of features weighting and proposed a new method as well. In this research, the effectiveness of this algorithm has been examined on two datasets, Desharnais and Maxwell. Then, MMRE, PRED (0.25), and MdMRE criteria have been used to evaluate and compare the proposed method against other evolutionary algorithms. Employing the proposed method showed considerable improvement in estimating software cost estimation.
The Wavelet Compressibility of Compound Poisson Processes

In this paper, we precisely quantify the wavelet compressibility of compound Poisson processes. To that end, we expand the given random process over the Haar wavelet basis and we analyse its asymptotic approximation properties. By only considering the nonzero wavelet coefficients up to a given scale, what we call the greedy approximation, we exploit the extreme sparsity of the wavelet expansion that derives from the piecewise-constant nature of compound Poisson processes. More precisely, we provide lower and upper bounds for the mean squared error of greedy approximation of compound Poisson processes. We are then able to deduce that the greedy approximation error has a sub-exponential and super-polynomial asymptotic behavior. Finally, we provide numerical experiments to highlight the remarkable ability of wavelet-based dictionaries in achieving highly compressible approximations of compound Poisson processes.
CRNN Based OCR for American and British Sign Language Fingerspelling

Optical Character Recognition (OCR) technology is mostly used to convert image containing written text (typed, handwritten, printed or scanned) into machine-readable text data. This work explores the first investigation of American Sign Language (ASL) and British Sign Language (BSL) fingerspelling font images to the corresponding English text conversion system. The proposed system is implemented by the Convolutional Recurrent Neural Network (CRNN) model with three different feature extraction methods. We also investigated two types of hyper-parameters such as hidden size and number of iterations. The experimental results show that our system achieved significantly higher conversion quality on the open-test dataset for both ASL and BSL fingerspelling. Our proposed technique can also be used in deaf education, for example, to extract fingerspelling images from exam answer sheets.
Can Competitive Digital Games Support Real-Time Music Creation?

This article presents practice-based research exploring the interplay of real-time music creation and competitive gameplay. Musically creative video games, apps, and sound art are first surveyed to highlight their characteristic avoidance of competitive game elements. The relationship between play, games, and musical activity is then examined with reference to theoretical perspectives from ludomusicology and game studies, revealing a series of mechanical and aesthetic design tensions emerging between competitive gameplay and music creation. Two original music games are presented to explore this interplay across contrasting design approaches: EvoMusic engenders an abstract competitive dialogue between the player and system for authorial control, while Idea presents a more explicit ludic framework with goals, progression, danger, and victory. The games are evaluated in a comparative user study to capture the player experience of composing within competitive game settings. Participant responses revealed conflicting expectations for ludic and compositional experiences. Idea was the preferred game, yet its strong ludic elements distracted from or disincentivized music creation; EvoMusic offered more focused music creation yet also a weaker gameplay experience for lacking these same competitive elements. This relationship reflects the theoretical design tensions suggested by ludomusical scholarship. Further, a majority of participants characterized EvoMusic as being simultaneously competitive and creatively stimulating. The implication is that competitive games can support music creation for certain players, though it remains challenging to satisfy expectations for both within any one system. Design recommendations are drawn from these insights, and the potential for future research into creative music games is discussed.
Recommender Systems for E-commerce in online video advertising: Survey

recommendation systems (RS) have become very widely used in recent years. They assist clients in getting data and making selections when they lack the knowledge required to judge on certain item. They can help the customer in efficacious information sorting. They are software system techniques that make suggestions supporting the client’s taste to find new things acceptable for them from a huge amount of data by filtering personal information. The user’s likes and preferences should precisely be identified in order to make the most appropriate suggestions. Recommendation systems have a crucial role in online video advertisement through introducing new products onto the market. They encourage people to purchase the items and provide an opportunity for e-commerce companies to introduce their products in videos. This survey introduces the recent techniques to compare various types of the recommender systems, recent recommendation algorithms and their use in the online videos advertisement. This comparison paves the way for knowing the advantages and disadvantages for each technique.
Apocalypse or Redemption: How the Portuguese Media Cover Artificial Intelligence

Artificial intelligence (AI) occupies an increasingly important place in the contemporary societies. One of the most visible aspects is the personalization of online content, news or advertising, but the emergence of personal assistants and autonomous vehicles are phenomena that also attracts the attention of the media and, by this way, reach the population. This article studies the Portuguese reality by analyzing the coverage of artificial intelligence by five national newspapers. The conclusions allow us to say that, although it was considered huge, the coverage proved to be superficial, with a high rate of reuse of sources from other texts, presents little diversity in opinions, and does not stimulate debate in society. Among the most common approaches are economics and political. The coverage was positive in most cases, especially in the health area, where the media never criticized the effects of AI.
Dual-Branch CNN for the Identification of Recyclable Materials

The classification of recyclable materials, and in particular the recovery of plastic, plays an important role in the economy, but also in environmental sustainability. This study presents a novel image classification model that can be efficiently used to distinguish recyclable materials. Building on recent work in deep learning and waste classification, we introduce the so-called “Dual-branch Multi-output CNN”, a custom convolutional neural network composed of two branches aimed to i) classify recyclables and ii) distinguish the type of plastic. The proposed architecture is composed of two classifiers trained on two different datasets, so as to encode complementary attributes of the recyclable materials. In our work, the Densenet121, ResNet50 and VGG16 architectures were used on the Trashnet dataset, along with data augmentation techniques, as well as on the WaDaBa dataset with physical variation techniques. In particular, our approach makes use of the joint utilization of the datasets, allowing the learning of disjoint label combinations. Our experiments confirm its effectiveness in the classification of waste material.
Content-Based Medical Image Retrieval Using Pretrained Inception V3 Model

The rapid increase in medical images makes it more difficult and time-consuming to find relevant images when they are needed. This opens an opportunity to develop technological solutions to solve this problem and help the medical field. This paper proposes a solution for content-based medical image retrieval using a pre-trained deep convolutional neural network. In this work, the Inception V3 model was used with ImageNet weights modifying the last layer to fit the data used. These comprise an open dataset with six classes. The model has been optimized using regularization techniques to obtain 99.98% accuracy in the classification stage and 99% accuracy in the image retrieval stage.
A Blockchain-Based Collaborative Filtering Recommendation System Based on Trust

Blockchain has enabled businesses to innovate by creating a decentralized system that transforms power affects a wide range of industries, including banking, finance, manufacturing, supply chain management, healthcare, and government. Many industries, including e-commerce, could use this model to promote scalability, security, and decentralization. We are entering a new age of information sharing powered by blockchain technology. Building trust in distributed environments without relying on authorities represents a technological advance that could transform many industries, including recommendation systems. A new and advanced way of exploring Blockchain and recommendation systems (RS) will be explored in this research. We propose a decentralized recommendation system by formulating a decentralized matrix completion form based on the popular collaborative filleting (CF) model by incorporating it into a decentralized network. Our goal is to make online activities more secure and trusted by incorporating smart contracts in the main blockchain protocol. Using blockchain technology in recommendation systems, we will be able to make online activities more secure. A smart contract system is created that enables enterprises to use a secure database to cooperate and continuously maintain an updated model. As part of the learning case studies, a collaborative filtering model is used for recommendation purposes along with the decentralized concept of blockchain for storage and computation in such a way that data is fully distributed. An incentive mechanism evaluates the model accuracy by offering a trust-based, high-performing recommendation system.
Improving the Keyword Co-occurrence Analysis: An Integrated Semantic Similarity Approach

Bibliometric studies help yield useful information about the quantity and quality of research works in a particular academic domain. We used the popular bibliometric technique of co-occurrence analysis to explore emergent topical areas in the field of urban mobility. Our work contributes from a methodological perspective to improve the conventional co-occurrence analysis method. The modified co-occurrence analysis is two-fold: firstly, we used the Rapid Automatic Keyword Extraction (RAKE) algorithm to extract keywords from abstracts of documents in a corpus to generate a new co-occurrence matrix. Secondly, we produced “semantic similarity” between each keyword in matrix form, which is combined with the co-occurrence matrix of the extracted keywords from documents in a corpus to yield a weighted co-occurrence matrix. We analyzed the unweighted and weighted matrices in terms of their network structure and cluster quality. We demonstrated that the weighted matrix shows network structures with higher modularity and superior cluster quality than its unweighted counterpart. These observations are consistent in terms of more meaningful content and greater ease of exposition in the emergent themes.
Design of Offline Analysis System for Remote Sensing Data Service Based on Hive

With the wide application of remote sensing data service platform, the platform accumulates more and more data, and the remote sensing users' demand for remote sensing data service becomes more and more diverse. Therefore, how to deal with remote sensing service data, make statistics and analyze its potential value, and meet the diverse needs of remote sensing users becomes particularly important. Based on big data technology, this paper firstly collects and stores remote sensing user behavior data and remote sensing service platform business data, then uses Hive to build offline data warehouse, performs dimensional modeling and hierarchical design of the data, and finally obtains relevant index data values by statistical analysis. Through the statistical analysis of remote sensing service data can not only provide data support for active service mode, but also provide support for the decision of remote sensing application department managers.
Mathematical Modeling of Information Warfare - Status, Challenges and Future Trends

Information warfare is a new threat that has the potential to become a major global security problem in the future, particularly if the relationship between information and power strengthens. Information warfare will emerge as a national security threat in the next years, according to futures projections. The four scenarios examined provide plausible outcomes that provide early warning indicators of how knowledge warfare will emerge as a national security threat. Polarization is a big future issue in terms of utilizing conflict as a weapon of mass disruption. This paper will provide the status, challenges facing this important type of new warfare, and also concentrates on the future trends facing this important type of warfare
Development and Evaluation of a PCB‘s Manual Assembly system using Augmented Reality and Total Quality

Manual assembly remains an important step for many products manufacturing and it is responsible for employing many workers. Nevertheless, manual assembly is susceptible to human errors. Augmented Reality (AR) is one of the enabling technologies to industry 4.0, which has the potential to help by providing on-demand guidance and support monitoring features that comply with Total Quality Management (TQM). Based on these assumptions, the Augmented Reality for Manual Assembly system (RAMM) was developed as an AR system to help PCB‘s manual assembly. A questionnaire was submitted to 124 evaluators. The answers raised questions about the system interface, the devices’ usability and the possible long-term implications of using RAMM. They also found that the software would benefit the assembly of Printed Circuit Boards (PCBs) in many aspects, such as reducing errors, improving quality, novices training, and the way that information resources are presented to the assembler. The functionalities of the system were positively evaluated, which resulted in an overall score of 8.14 (on a scale from 0 to 10). The conclusion is that the RAMM system was able to satisfactorily integrate AR functionalities identified for PCBs assembly, following the principles of TQM and thereby helping to improve the process of manual assembly of PCBs.