Reviewerly/exHarmony huggingface dataset:
Risk Analysis of Enterprises’ Investment in Infrastructure in Developing Countries Based on Structural Equation Model

In order to control the risks of Chinese enterprises in infrastructure investment in developing countries, the corresponding evaluation system is constructed through a structural equation model algorithm to analyze these risks, so as to achieve risk prediction and risk controllability. Structural equation modeling is a method to establish, estimate, and test causality. It can replace multiple regression, path analysis, factor analysis, covariance analysis, and other methods and clearly analyze the effect of individual indicators on the population and the interrelationship between individual indicators. It is a multivariate statistical modeling technology mainly applied to confirmatory model analysis. Due to the guidance of national policies, there are more and more opportunities for Chinese enterprises to invest abroad. However, due to the influence of political, economic, and environmental factors, overseas investment is facing many difficulties. This paper analyzes the risks from four aspects: bilateral policy risk, legal difference and litigation risk, international economic risk, and technical risk through structural equation model algorithm. Aiming at these risks, the simulation software of the algorithm is constructed in MATLAB big data analysis software, and the risk control measures are put forward. Finally, with the support of China’s policies, in order to ensure the investment income, we should carry out risk intervention for the foreseeable risk and reduce the impact of risk on the investment income as much as possible, so as to improve the risk prevention and management awareness of overseas investment business. By analyzing the characteristics of venture capital and the various kinds of risks affecting venture capital, the risk structure model estimation of risk sneak attack is established by using the principle of structural equation model, and the impact of various risks on investment risks can be analyzed, so that the risk measurement and control of venture capital provides the basis of theoretical knowledge.
Before the algorithm, what's in the imagination?

This forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations. --- Juho Kim and Henriette Cramer, Editors
Detecting Image Forgery Over Social Media Using Residual Neural Network

Social media have earned fame for consuming the news due to their quick proliferation and availability. However, they are also the main contributor in spreading fake news. Fake images spread over microblogging platforms like Twitter generates misrepresentation and arouses destructive emotions in consumers. This makes the detection of fake images over social platforms an extremely critical challenge. Deep learning methods learn the latent features of images and can be utilized in detecting fake images. In this paper, we have used the ResNet-50 a residual neural network to detect fake images. The images are passed through the Error Level Analysis process before being input to the deep learning model to subside the image's main features and bloat the latent features of manipulation. The model is verified against the Twitter image dataset. The experiment proves that residual neural networks perform well in detecting fake images over social media platforms.
A Hybrid Relation Extraction Model for Knowledge Graph of Heroic Epic “Gesar”

Extracting relations from unstructured text is the primary step of knowledge graph construction. This step is harder in the Tibetan heroic epic “Epic Appreciation King Gesar” than in other Chinese stories because of the difficulties in the identification of the named entities and the complicated character relationships. Given this, based on the named entity corpus constructed with help of domain experts earlier, a hybrid relation extraction model combined with entity features and syntactic-semantic features, EBERT-BiLSTM, is proposed in this paper. The data set, corpus, and model construction principle and process are described in detail. Experiments show that EBERT-BiLSTM has better effectiveness and performance than just BiLSTM-Attention and BERT. Finally, EBERT-BiLSTM is used to extract 320-character relation triples and then construct the heroic epic “Gesar” knowledge graph.
Semantically Informed Slang Interpretation

Slang is a predominant form of informal language making flexible and extended use of words that is notoriously hard for natural language processing systems to interpret.Existing approaches to slang interpretation tend to rely on context but ignore semantic extensions common in slang word usage.We propose a semantically informed slang interpretation (SSI) framework that considers jointly the contextual and semantic appropriateness of a candidate interpretation for a query slang.We perform rigorous evaluation on two large-scale online slang dictionaries and show that our approach not only achieves state-of-the-art accuracy for slang interpretation in English, but also does so in zero-shot and few-shot scenarios where training data is sparse.Furthermore, we show how the same framework can be applied to enhancing machine translation of slang from English to other languages.Our work creates opportunities for the automated interpretation and translation of informal language. Query (target slang in bold italic):That chick is lit!Groundtruth definition of target slang: Attractive. LM Infill baseline prediction:Cute, beautiful, adorable.LM Infill + SSI prediction:Hot, cool, fat. Dual Encoder baseline prediction:Another word for bitch.Dual Encoder + SSI prediction:Word used to describe someone who is very attractive.
The Challenges and Case for Urdu DBpedia

DBpedia is a broad open-source knowledge base continually upgraded and broadened by a large community since 2007. DBpedia’s main goal is to retrieve structured content from the articles of Wikipedia and make it available on the World Wide Web for semantic web technologies. As DBpedia services are available in 127 languages, very few contributions are from the Urdu language. This article objectively presents and discusses the concepts and challenges involved in initiating the Urdu chapter of DBpedia, the contributions required, and conclusions about the issues that need to be addressed effectively. First, a brief overview of the DBpedia concepts is presented. Second, the approach to develop Urdu DBpedia, and the issues encountered during its development are discussed. Third, the limitations and challenges are stated. Lastly, it concluded about the creation of Urdu DBpedia chapter and the complexities that can be faced during its creation.
Two Recurrent Neural Networks With Reduced Model Complexity for Constrained <i>l</i>₁-Norm Optimization

Because of the robustness and sparsity performance of least absolute deviation (LAD or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> ) optimization, developing effective solution methods becomes an important topic. Recurrent neural networks (RNNs) are reported to be capable of effectively solving constrained <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm optimization problems, but their convergence speed is limited. To accelerate the convergence, this article introduces two RNNs, in form of continuous- and discrete-time systems, for solving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm optimization problems with linear equality and inequality constraints. The RNNs are theoretically proven to be globally convergent to optimal solutions without any condition. With reduced model complexity, the two RNNs can significantly expedite constrained <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm optimization. Numerical simulation results show that the two RNNs spend much less computational time than related RNNs and numerical optimization algorithms for linearly constrained <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm optimization.
Multi-agent Q-learning Based Navigation in an Unknown Environment

Collaborative task execution in an unknown and dynamic environment is an important and challenging research area in autonomous robotic systems. It is essential to start the task execution in situations like search and rescue at the earliest. However, the time duration between team announcement and the arrival of team members at the location of a task delays the execution of the task. The distributed approaches for task execution assume that the path is known. However, in an environment, say, a building, the position of the doors may not be known, and some of the doors may get closed during task execution. Hence, an agent should first learn the map of the environment. The learning of the map of an unknown environment can be accelerated with multiple agents. This paper proposes a distributed multi-agent Q-learning-based approach for navigation in an unknown environment. The proposed approach is implemented using ARGoS, a realistic multi-robot simulator.
Reversible data hiding in multispectral images for satellite communications

The Landsat multispectral images are taken by satellites and then transmitted to the monitor center on the ground. Due to that governments and companies sometimes transmit secret messages through the satellite channel, the transmitting process of multispectral images can be a good cover for the secret messages by applying reversible data hiding (RDH) technology. This work proposed a novel RDH method for multispectral images. Before data embedding, each band is creatively matched with a reference band to perform the proposed inter-band complexity computation and inter-band prediction. For the pixels of the to-be-embedded band, we inventively combine the proposed inter-band complexity and the inter-space complexity to classify them into four categories and then process them by the inter-space predictor, inter-band predictor, multi-predictor and second-time prediction, respectively. Moreover, a general complexity is used to control the embedding priority to achieve lower distortion. Compared with the generally used reversible data hiding methods, our proposed method can reach a larger embedding rate and lower distortion for multispectral images.
Performance Evaluation of a DQN-Based Autonomous Aerial Vehicle Mobility Control Method in Corner Environment

In this paper, we present a DQN-based AAV mobility control method and show the performance evaluation results for a corner environment scenario. The performance evaluation results show that the proposed method can decide the destination based on LiDAR for TLS-DQN. Also, the visualization results of AAV movement show that the TLS-DQN can reach the destination in the corner environment.
Manajemen perpustakaan dalam mendukung gerakan literasi digital di Madrasah Aliyah Negeri 2 Kota Madiun

Information and technology developments have changed the culture of manual library services to digital. School libraries have a role in providing digital information to students in schools. This study aimed to determine library management in supporting the digital literacy movement in Madrasah Aliyah Negeri 2, Madiun City, through an analysis of library planning, implementation, and evaluation activities. The results showed that the library's digital literacy program planning activities were based on class, school culture, and the community. The process of formulating digital literacy program planning used an analysis of the madrasa work plan, previous programs, internal and external factors, analysis of the competence and potential of program implementers, and socialization. The library implementation activities are formulating programs, dividing the implementing team, and creating work instructions by establishing coordination and good relations with program implementers, the head of the library, the implementing team and teachers, the head of the library, and the head of the madrasa. Evaluation activities are in the form of internal evaluation by the head of the library and the head of the madrasa, performance evaluation by the implementing team, evaluation of performance results, and evaluation of program achievement. Madrasah Aliyah Negeri 2, Madiun City, has developed android-based library access, electronic catalog, and social media. Thus, it concludes that library management has been running quite well in supporting the digital literacy movement in Madrasah Aliyah Negeri 2 Madiun City.
Modified Spotted Hyena Optimizer Based Leukemia Microscopic Images Classification

Applications of accurate machine learning techniques in medical field are increasing every day. This research paper proposes a novel Modified Spotted Hyena Optimizer as transformation technique to improve the performance of popular machine learning algorithms in classifying the Leukemia microscopic images. In the proposed transform, control parameter of Spotted Hyena Optimizer is found using Weightless Swarm Algorithm iteratively. Microscopic images of 26 leukemia and 26 normal subjects are obtained from C-NMC website and considered in this study. Statistical features namely mean, variance, skewness and kurtosis are extracted from microscopic images and given as input to the proposed transform. Then the transformed values are given as input to any one of the four supervised classifiers: K-Nearest Neighbor, Decision Trees, Random Forest, and Stochastic Gradient Descent. When the Modified Spotted Hyena Optimizer is for transforming the features in Stochastic Gradient Descent classifier, highest accuracy of 90% is achieved while the accuracy offered is only 65% when no transform is used.
Glocal, Holistic and Performative Education

We look at innovative education through a global and local approach to the philosophy of existence, known as glocalization, and through the psychological Gestalt of experiencing a whole that is inevitably viewed within social science by a sociological holistic principle. Our glocal, holistic, aesthetic practice has been experienced and realized through intensive performative education that enhances the existential capacity in multi / media projects through the humanistic approach, affectively using digital framing. Performative projects are performed in the Media Culture course at the Faculty of Teacher Education in Osijek, Department in Slavonski Brod (FOOZOS), including vertical education of primary and secondary students. The education process is performed through lectures, intensified through e-learning, and finalized by group orchestration in a large project supported by the non-profit organization Culture, Media and Education – Arthea and the Ministry of Culture of the Republic of Croatia. Examples of successfully implemented glocal, holistic and performative education can be seen in the videos: Baroque in Postmodern Glocal Approach, (created for the European Schoolnet Academy, 2019), Situationally Performative Smartphone Orchestra (conducted performance, Slavonski Brod town square, 2018), Post-Modern Dystopic Deconstruction for Mobile Phones Orchestra (FOOZOS, Department in Slavonski Brod, 2017), Project with Mobile phones (mobile learning for the course Contemporary Media in Literature Lectures, FOOZOS, Department in Slavonski Brod, 2016), multimedia poem The Soul of the World (Music Biennale Zagreb, 2007) and New Education Model: Multimedia Art (Christmas in Musical Pictures, 2004, Concert Hall, Slavonski Brod).
CONDITIONS FOR FORMATION OF COMPETENCES WITHIN THE FRAMEWORK OF MODERN PROFESSIONAL TRAINING OF FUTURE ECOLOGISTS

The conditions of competence formation within the framework of professional training of future ecologists are analyzed in the work: realization of system and competence approaches in professional training of future ecologists; actualization of the process of formation of readiness of future ecologists for professionally oriented activity; the need to form environmental and digital competencies of future environmentalists during professional training. The didactic conditions of formation of digital competence of future ecologists are defined: maintenance of a proper level of theoretical and methodical preparation of teachers and students for use of digital technologies in the course of training; strengthening the motivation of future environmentalists for self-education, stimulating the desire to work independently; computer literacy at the user level; development of skills of information retrieval and effective network navigation; development of skills of work with educational resources of the Internet.
Improving Next-Application Prediction with Deep Personalized-Attention Neural Network

Recently, due to the ubiquity and supremacy of E-recruitment platforms, job recommender systems have been largely studied. In this paper, we tackle the next job application problem, which has many practical applications. In particular, we propose to leverage next-item recommendation approaches to consider better the job seeker’s career preference to discover the next relevant job postings (referred to jobs for short) they might apply for. Our proposed model, named Personalized-Attention Next-Application Prediction (PANAP), is composed of three modules. The first module learns job representations from textual content and metadata attributes in an unsupervised way. The second module learns job seeker representations. It includes a personalized-attention mechanism that can adapt the importance of each job in the learned career preference representation to the specific job seeker’s profile. The attention mechanism also brings some interpretability to learned representations. Then, the third module models the Next-Application Prediction task as a top-K search process based on the similarity of representations. In addition, the geographic location is an essential factor that affects the preferences of job seekers in the recruitment domain. Therefore, we explore the influence of geographic location on the model performance from the perspective of negative sampling strategies. Experiments on the public CareerBuilder12 dataset show the interest in our approach.
Sentiment Analysis of Product Reviews to Identify Deceptive Rating Information in Social Media: A SentiDeceptive Approach

Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products.[Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions.[Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products.[Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decisionmaking.[Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes.Next, we develop a coding guideline used as a base for the comments annotation process.We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers.Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard crossvalidation approach (KFold and Shuffle Split).[Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions.In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01%precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.
An Agile Software Engineering Method to Design Blockchain Applications

Cryptocurrencies and its underlying technology, Blockchain, are transforming banking and economics by allowing trustworthy programs to exist without the requirement for a trusted counterpart. In recent years, the Blockchain and the programs that run on it, known as Smart Contracts, have seen increased use across all businesses that demand trust and verifiable credentials. Some have compared the "Blockchain revolution" to the early days of the Internet and the World Wide Web. As a result, all Blockchain-based software development is accelerating at a dizzying pace. Many software engineers argue that the widespread interest in Blockchain technology has resulted in haphazard and rushed software creation, a type of first-come, first-served competition that fails to assure software quality or take into consideration core software engineering ideas. This research tries to solve this issue by proposing a software development strategy for gathering requirements, assessing them, designing, creating, testing, and deploying Blockchain applications. Several Agile approaches are employed in the process, including User Stories and iterative and incremental development based on them. It does, however, employ more formal notations, such as UML diagrams that define the system's design, with improvements to express unique Blockchain concepts. The method is well discussed, including an example to illustrate how it works.
Artificial Intelligence in Study-Abroad Program Recommendations

Students willing to study abroad may find it challenging to go through tons of website pages on the application process. The complication of the studying-abroad application process makes it even harder for many students to figure out their dream and fit schools. With artificial intelligence being widely promoted across different subjects and disciplines, we made attempts to incorporate artificial intelligence into education program recommendations. We thus designed a recommendation system for studying-abroad programs through the K-nearest neighbor algorithm. The designed system may recommend up to six colleges and universities to students according to their input of grade average points, language testing scores, acceptable fees, target majors, and location preference. In addition, the analysis of the application plan and the report on the academic entrance requirements are also offered through the system. The project simplifies the application process and thus saves the time and energy of the applicants.