Reviewerly/exHarmony huggingface dataset:
Towards an Authoring Tool to Help Teachers Create Mobile Collaborative Learning Games for Field Trips

Pedagogical activities that combine mobility, collaboration and game mechanics present significant advantages to attract students’ attention and maintain their engagement in learning. Teachers naturally try to combine mobility and collaboration when they create field trips. Indeed, they identify several Points Of Interest that learners physically need to go to, thus requiring mobility, and they usually group students in teams to encourage collaboration, even if such collaboration is seldom attained. However, using the third element - game mechanics - is harder for teachers because they are not familiar with game models and often not gamers themselves. In this article, we present the MOCOGA Model, a MObile COllaborative learning GAme Model that is designed to adapt to many types of field trips. This model also offers a nested design approach that guides teachers in enriching their field trips iteratively. This model is the first step towards the development of an authoring tool that will enable teachers to create their own smartphone applications.
An Effective Convolution Neural Network for Automatic Recognition of Analog and Digital Signal Modulations for Cognitive SDR Applications

This paper proposes a convolution neural network (CNN) architecture for automatic recognition of signals on the basis of their modulations for Cognitive software defined radio (SDR) applications. It is developed starting from two CNNs specifically designed for this problem and is characterized by having a low number of convolution and fully connected layers sharing also a very low number of filters/units. Moreover, Batch normalization is used to increase learning rate and reduce training time. The reduced complexity together with its low operating time make it compliant with real-time SDR applications. The proposed architecture is validated on the RadioML2016.10a dataset showing interesting results in discriminating both analog and digital modulations under different signal to noise ratio (SNR) regimes.
An Approach for Verification of Secure Access Control Using Security Pattern

According to OWASP-2021, more than 3,00,000 web applications have been detected for unauthenticated and unauthorised access leading to a breach of security trust. Security patterns are commonly used in web applications to address the problem of broken access. Web developers are not experts in implementing security patterns. Therefore, it is necessary to verify that the security pattern has been applied, specifying the original intent of the security pattern. In this paper, an approach has been proposed that analyses the behavioural aspect of security patterns to verify that it meets the security requirement of the web application. The proposed approach extracts the class diagram’s structural properties, relations, associations, and security-related constraints and verifies it using the first-order predicate logic. Experiments have been conducted using class diagrams of security patterns to detect instances of broken access control early in the design phase. The proposed approach will help minimise the risk of unauthenticated and unauthorised access to a web application.
CIST: Differentiating Concepts and Instances Based on Spatial Transformation for Knowledge Graph Embedding

Knowledge representation learning is representing entities and relations in a knowledge graph as dense low-dimensional vectors in the continuous space, which explores the features and properties of the graph. Such a technique can facilitate the computation and reasoning on the knowledge graphs, which benefits many downstream tasks. In order to alleviate the problem of insufficient entity representation learning caused by sparse knowledge graphs, some researchers propose knowledge graph embedding models based on instances and concepts, which utilize the latent semantic connections between concepts and instances contained in the knowledge graphs to enhance the knowledge graph embedding. However, they model instances and concepts in the same space or ignore the transitivity of isA relations, leading to inaccurate embeddings of concepts and instances. To address the above shortcomings, we propose a knowledge graph embedding model that differentiates concepts and instances based on spatial transformation—CIST. The model alleviates the gathering issue of similar instances or concepts in the semantic space by modeling them in different embedding spaces, and adds a learnable parameter to adjust the neighboring range for concept embedding to distinguish hierarchical information of different concepts, thus modeling the transitivity of isA relations. The above features of instances and concepts serve as auxiliary information so that thoroughly modeling them could alleviate the insufficient entity representation learning issue. For the experiments, we chose two tasks, i.e., link prediction and triple classification, and two real-life datasets: YAGO26K-906 and DB111K-174. Compared with state of the arts, CIST achieves an optimal performance in most cases. Specifically, CIST outperforms the SOTA model JOIE by 51.1% on Hits@1 in link prediction and 15.2% on F1 score in triple classification.
Executable contracts for Elixir

This paper introduces a new library for implementing executable contracts in Elixir. In addition to classical contract constructs such as preconditions and postconditions, the library permits e.g. to specify exceptional behaviour (i.e. which exceptions are thrown and under which conditions), and to associate timers with function calls to detect non-terminating or too slow computations.
Rapid Development of Douyin Will Have a Great Threat to Taobao and Subvert the Industry Pattern

This article analyzes the data including double eleven turnover, anchor, and the customer satisfaction.It finds that the rapid development of Douyin has a huge threat to Taobao and has a trend to replace it.The study reveals that, firstly, Douyin is developing rapidly, and its turnover is much faster than Taobao.It is popular among people, but Taobao's return rate is rising and its satisfaction is decreasing.Secondly, because of Douyin's unique advantages and Taobao's obvious shortcomings.Therefore, the traditional model of business will be subverted, who can seize the flow entrance is the key.Finally, Taobao relies heavily on Li Jiaqi and Weiya, however, Douyin has many directions of development.Thus, Douyin has made the audience become the disseminators of short video works, thus forming a unique "vibrato" cultural trend.This trend also has Douyin's constantly updated development opportunities.Because Douyin keeps up with the trend, so it is inevitable to replace Taobao.Douyin has created a lot of entrepreneurship and employment opportunities and has made important contributions to ease employment pressure, expand employment scale and enrich employment types.So far, a huge, stable, orderly, and complex employment system has been formed around the platform of shaking.Meanwhile, Douyin has produced a series of positive effects on activating entrepreneurship, absorbing employment, innovating, stimulating consumption, etc.It has greatly promoted China's economic development.
BiGCN: A bi-directional low-pass filtering graph neural network

Graph convolutional networks (GCNs) have achieved great success on graph-structured data. Many GCNs can be considered low-pass filters for graph signals. In this paper, we propose a more powerful GCN, named BiGCN, that extends to bidirectional filtering. Specifically, we consider the original graph structure information and the latent correlation between features. Thus BiGCN can filter the signals along with both the original graph and a latent feature-connection graph. Compared with most existing GCNs, BiGCN is more robust and has powerful capacities for feature denoising. We perform node classification and link prediction in citation networks and co-purchase networks with three settings: Noise-Rate, Noise-Level, and Structure-Mistakes. Extensive experimental results demonstrate that our model outperforms the state-of-the-art graph neural networks in both clean and artificially noisy data.
“It took me back 25 years in one bound”: self-generated flavor-based cues for self-defining memories in later life

KEYWORDS: 3D printed foodself-defining memoriesemotionsmemory cuesflavormultisensory use experiencessensory dataperceptual dataolder adults
Convergence of online learning algorithm with a parameterized loss

&lt;abstract&gt;&lt;p&gt;The research on the learning performance of machine learning algorithms is one of the important contents of machine learning theory, and the selection of loss function is one of the important factors affecting the learning performance. In this paper, we introduce a parameterized loss function into the online learning algorithm and investigate the performance. By applying convex analysis techniques, the convergence of the learning sequence is proved and the convergence rate is provided in the expectation sense. The analysis results show that the convergence rate can be greatly improved by adjusting the parameter in the loss function.&lt;/p&gt;&lt;/abstract&gt;
Intangible Cultural Heritage Based on AR Technology

With the development of society and the improvement of people's material living standards, Internet technology has penetrated into all aspects of people's lives, and AR technology has gradually entered people's lives.At the same time, all sectors of society are paying more and more attention to the development and protection of ICH and other traditional cultures.Although many experts and scholars have conducted research and discussion in this area before and after, due to the late start time, shortage of technology and incomplete technical personnel and other issues, no good solution has been found, and little gain has been achieved.Based on this, this article adopts a new perspective, using the advantages of the modern big data society, through the current advanced AR technology and digital management, aiming to find the best plan for the development and protection of ICH, with a view to the domestic ICH Provide reference and reference for development and protection.This article uses data analysis, comparison and experiment methods.It first introduces the theoretical aspects of ICH and proposes specific measures for its digital development and protection, and then quotes the traditional cultural heritage of shadow play as as a specific example, 60 audiences were randomly surveyed by questionnaire survey, and divided into 3 groups according to age, and they were subjected to issues such as viewing attitude and effect of traditional shadow play based on AR technology and traditional shadow play according to the investigation, it is concluded that the traditional shadow play based on AR technology is more popular with the audience, and compared with the traditional shadow play, the viewing effect is better.
Learning prototypes for building fuzzy color spaces

In this paper, an approach to learn fuzzy color spaces from images is proposed. Fuzzy colors were introduced as a suitable way to model color categories, being the newest and best performance approaches those which use crisp color prototypes in order to build fuzzy color spaces. Current proposals in literature use standard set of colors prototypes, like ISCC-NBS ones, but it is shown that they are not flexible enough to collect the particular colors of a given image. To address this problem, in this paper we propose to extract image relevant colors and use them as prototypes to model fuzzy color spaces. We illustrate the advantages of our approach on the basis of some real experiments.
GLCM and CNN Deep Learning Model for Improved MRI Breast Tumors Detection

Breast cancer is one of the most common types of cancer among Iraqi women. MRI has been used in the detection of breast tumors for its efficient performance in the diagnosis process providing high accuracy. In this paper, breast MRI image data from 89 patients were classified using GLCM and CNN feature extraction methods. Four models were evaluated consisting of GLCM, CNN, combined GLCM and CNN features based models. The statistical ANOVA feature selection method was used to reduce the redundant features. The reduced feature subset was fed to CNN classifier for obtaining either normal or abnormal breast images. The proposed method was assessed in terms of accuracy, precision, recall and F1-score. The model provided 100% classification accuracy.
Undergraduate English Translation Teaching Based on Multimedia Technology

As a language teaching method, carrying out translation activities should serve the teaching goals of the whole class, and be combined with other teaching methods to jointly promote the improvement of English comprehension ability and students' application ability.This paper takes the application of multimedia technology in undergraduate English classroom teaching as the research object, combined with my specific teaching practice in undergraduate English classroom teaching, analyses the positive and negative factors of multimedia-assisted teaching, and summarizes teachers, students, teaching materials, The corresponding situation of changes in teaching process and teaching methods; 91.6% of students prefer English teachers to use auxiliary teaching resources for translation teaching.The development prospect of multimedia in classroom teaching and the application of multimedia in education and teaching in the future are prospected.It provides a reference for new teachers and scholars who wish to apply multimedia technology to assist English teaching in undergraduate classroom learning.
Fuzzy Quantified Relational Properties for Referring Expression Generation

Referring expressions aim to distinguish an object or a set of objects within a collection in terms of their properties. Within the Natural Language Generation field, Referring Expression Generation is one of the key problems, where graduality plays a very relevant role. In this paper we focus on quantified relational properties of objects, which are defined through their relations to other objects, and are of special interest in the generation of referring expressions. Our proposal involves fuzzy sets in the definition of relations, properties, and generalized quantification for such purpose. General quantified relation patterns are introduced, of which particular examples are To be related to at least n objects of type A, To be related to more objects of type A than objects of type B, To be related to all objects of type A except n, and To be next to many more objects of type A than objects of type B, among many others.
Improving Publlic Speaking Ability through Speech

Public Speaking is one of the most important skills one should have. In the industrial 4.0 era, the ability to speak and elaborate on certain issues and topics in front of the public is highly significant due to the fact that now information can no longer be limited. Information is available on the internet, especially on social media. Therefore, in order to avoid misunderstanding the content of the information, public speaking ability is needed. The ability helps people to grasp information correctly. Moreover, the public speaking ability also teaches how to convince people which will be useful for those who are working and also for job seekers. This article discusses the public speaking skills of SMA Way Jepara students, especially in grades 11 and 12. The students are encouraged to practice their speaking ability. They are given some materials and topics that would help them to be more confident. After the 2nd meeting was conducted most of the students were able to gain a much better speaking ability and they also became more confident.
Application of Internet of Things Audio Technology Based on Parallel Storage System in Music Classroom

In order to improve the effect of music classroom teaching, this paper combines the parallel storage system and the Internet of Things audio technology to construct a music education system to improve the effect of music education in colleges and universities. Moreover, this paper expounds a grid-free method for sparsity and parameter estimation from the model and mathematical principles and proposes a grid-free DOA method based on fast reconstruction of the T-matrix. This method is guaranteed to produce sparse parameter estimates. At the same time, the numerical simulation shows that the method has stable estimation performance as sparse and parameter estimation methods. In addition, this paper constructs a music classroom teaching system based on parallel storage system and Internet of Things audio technology. Through the experimental research, it can be seen that the parallel storage system and the Internet of Things audio technology has obvious application effect in the music classroom, which can effectively improve the teaching effect of the music classroom.
(Retracted) News image text classification algorithm with bidirectional encoder representations from transformers model

With the development of Internet technology and the transformation of news media to informatization, news images, texts, and other information have exploded. The news image and text classification can effectively solve the disorder problem of news information. The early news image text classification is to establish artificial classifiers according to specific classification rules, but the classification error rate is high and the classification speed is slow. Later, machine learning technology replaced manual classification of news image texts. Although the classification efficiency has been greatly improved, the classification time is still a bit long. The bidirectional encoder representations from transformers (BERT) model uses transformer and encoder to pretrain news image text to improve classification efficiency. By comparing the differences between machine learning and BERT models in news image text classification, the experiments showed that the average precision, recall, and F1 values of the news image text classification algorithm using the BERT model were 96.6%, 95.7%, and 96.1%, respectively. All three evaluation criteria were about 5% more than the classification algorithm of the machine learning model. The classification speed of the news image text classification algorithm using the BERT model was 1.8 times that of the news image text classification algorithm based on support vector machine. Therefore, the news image text classification algorithm using the BERT model can improve the classification accuracy and efficiency of news image text.
Mind the compensation gap: towards a new European regime addressing civil liability in the age of AI

Abstract As artificial intelligence (‘AI’) becomes pervasive, it is inevitable that there will be a corresponding increase in AI disputes. However, it is often no easy task to determine liability in AI-related disputes due to the unique features of many AI systems, including their complexity, opacity, and the ability of some AI systems to self-adapt. This paper considers the existing EU liability framework and concludes that, at present, it is not adequate to ensure that injured parties can be compensated for damage caused by AI. The argument presented is that Product Liability Directive 85/374/EEC should be amended to provide greater protection for claimants that suffer harm caused by AI, and proposals for revision are suggested. In addition, this paper argues that there are cogent reasons to design sector specific legislation and identify ex ante which party should be held strictly liable for certain classes of AI.
The analysis of dynamic emotional contagion in online brand community

Online brand communities (OBCs) could benefit firms in many usages, ranging from collecting consumers’ suggestions or advice to interacting with community members directly and transparently. Creating a positive emotional atmosphere is essential for such communities’ healthy development as its boosts the continuous involvement of each member. However, the dynamic cross-influences and evolution of emotions in OBCs have not been fully explored, which was the research gap this paper tried to fill. Based on emotional contagion theory, this study identifies three sources of textual sentiment through machine learning methods in OBCs: member’s posts, other members’ feedback, and the focal firm’s official feedback. This study further tested the dynamic emotional contagion process among these sources on valence (mean) and volatility (dispersion), namely how they affected each other. Data was collected from the MIUI forum, a large forum launched by Xiaomi corporate on August 1, 2011, which contained 17,622 posts and 99,426 feedback. Results showed that: (1) in the emotional contagion process, there existed differences in the influence of emotional valence and volatility from different sources; (2) all emotional interactions were temporary and mostly lasted no more than three days; (3) the most significant contributor of each sources’ emotion was itself, which could be explained by lagged effect; (4) the valence of focal firm’s emotion (focal firm’s official feedback) was the second contributor of the valence of member’s emotion (member’s posts) and other members’ emotion (other members’ feedback). Three sources of emotion in OBCs and emotional valence/volatility should be considered when firms try to guide the emotional changes in such communities. Furthermore, firms could proactively influence members’ emotions by carefully designing the feedback to members’ posts. Besides, since all interactions are temporary, firms need to engage in online communities frequently, like consistently offering feedback.
Working with SAP BTP Estimator and SAP HANA Cloud

In this chapter, you will learn how to work with the SAP Discovery Center and use SAP BTP Estimator to calculate capacity units and determine the sizing for your SAP HAN Cloud implementations.
Transformer-Based Automatic Speech Recognition of Formal and Colloquial Czech in MALACH Project

Czech is a very specific language due to its large differences between the formal and the colloquial form of speech. While the formal (written) form is used mainly in official documents, literature, and public speeches, the colloquial (spoken) form is used widely among people in casual speeches. This gap introduces serious problems for ASR systems, especially when training or evaluating ASR models on datasets containing a lot of colloquial speech, such as the MALACH project. In this paper, we are addressing this problem in the light of a new paradigm in end-to-end ASR systems – recently introduced self-supervised audio Transformers. Specifically, we are investigating the influence of colloquial speech on the performance of Wav2Vec 2.0 models and their ability to transcribe colloquial speech directly into formal transcripts. We are presenting results with both formal and colloquial forms in the training transcripts, language models, and evaluation transcripts.