Reviewerly/exHarmony huggingface dataset:
A Cross-Domain Multimodal Supervised Latent Topic Model for Item Tagging and Cold-Start Recommendation

Cross-domain data analysis is playing an increasingly important role in media convergence and can be adopted for many applications. Most existing methods consider the domain discrimination as the multimodal representation difference or the imbalanced item classification distribution, which ignore the different tag semantics between domains. To this end, we propose an explainable cross-domain multimodal supervised latent topic model (CDMSLT) and evaluate our model on two applications. First, we learn a common topic space which is capable of explaining both domain specification and commonality. Second, we apply our model to multilabel classification task and put forward a cross-domain item tagging method (CDMSLT-tag). Third, combining user behaviors and CDMSLT model, we propose a cross-domain recommendation algorithm (CDMSLT-rec) which could estimate the user preference on new unseen domains. This paper proves the effectiveness of CDMSLT model by comparing these two applications with existing algorithms in a cross-domain scenario on Douban dataset.
THE STUDY OF TRUST AS SOCIAL CAPITAL IN DIFFERENT DIMENSIONS AND ITS ROLE IN ENTREPRENEURSHIP RELATIONSHIP

The biggest share in in market economy belongs to entrepreneurs representing the private sector in implementation of economic activities.Besides it, consumers, who are decision-making units of the economy, take a high place in economy and business relations.In our research when we talk about entrepreneurs, we mean producers producing local and foreign goods and services in Azerbaijan.Though, not only single economic decision makers and real factors affect the economy, but also trust, known as social capital, has an important role.Improving trust between manufacturers and customers has been seen traditionally as a critical factor in the growth and maintenance of a successful business relationship.Firstly, this research will search the trust in different dimensions and notify the relationship between them.In next step , we will look through how some social-demographic factors affect consumer trust on consumer-producer trade relationships.To be more speci ically, We will try to answer questions such as whether suppliers that provide a high level of trust in a consumer or vice versa reduce transaction costs measuring empirically whether factors such as age, education, gender and income have an impact on consumer trust.As a summary result, the fact that producers have a modest effect on the development of trust between themselves and their customers, the trust-based producer-consumer relationship really lowers transaction costs and shows that information sharing among the trusting parties is high.Another result is that factors such as gender and income have no effect on the formation of consumer trust.However , compare to those who have bachelor degree, phd students are more trusted to entrepreneurs .Our indings are very useful for arranging customer demands faster in both private and public sector and forming more value by creating a trust-based relationship.
BITCOIN: THE CURRENCY OF THE FUTURE OR A TOOL FOR SPECULATION?

The gain in the popularity of Bitcoin and recent sharp price volatility enhanced the interest of various individuals toward Bitcoin.The recent discussion of various authors on the Bitcoin brought some doubts in the future of cryptocurrency.Some authors considered it as money of the future while others criticize it as being a speculative vehicle that will be abandoned in future.The purpose of this article is to show the opposing views in the existing literature and identify whether Bitcoin is used as a tool for speculation or currency.This study focusses mainly on the money function of Bitcoin and breach of one of the most important features which is stability as a result of usage it for the purpose of speculative profit gain.The result of this study found that Bitcoin is used as a tool for speculation and considered unstable asset.If the current status of the currency remains the same, the Bitcoin will not be a currency.Further technological advancement, modifications, and regulations should be directed towards the centralization of Bitcoin in order to maintain stability and its future usage as currency.
Metrics Representation and Dashboards

Abstract This section is dedicated to reviewing dashboards for various roles, such as developers and managers, to provide metrics for software development in the most appropriate way. After reviewing various papers, different types, objectives, and visualization features of the dashboards were found. Based on these findings, tailored visualization was designed for the distinct roles of users. Furthermore, the dashboard was implemented within the Innometrics project boundaries with easily maintainable and extensible architecture on the front-end.
Concept and Principles of Measurement

Abstract Measurement is the fundamental mechanism of scientific study and development which allows to understand what is happening and why, and becomes critical for estimating progress, quality control, and productivity assessment throughout a software project. In this section basic principles of measurement and general software development metrics are discussed.
GQM and Recommender System for Relevant Metrics

Abstract Any software project needs metrics to measure closeness to the goal. However, it is a complicated task to identify appropriate and goal-focused metrics by hand. That is why this chapter presents the GQM-based metrics recommender—a recommender system that can automatically deduce metrics from the project goals and questions.
Meta-Learning in Supervised Machine Learning

In the present digital era, a popular use of Machine learning is knowledge mining from big data. Machine learning is the sub-branch of Artificial Intelligence (AI) that extracts rules automatically from Big Data for decision-making to build expert systems. Meta-Learning is a sub-branch of machine learning, which uses machine learning classifiers that learns to map and combine predictions and information of data of other ML-models in the field of ensemble-learning. Meta-learning helps us to select the best/right learning algorithms to solve a particular problem. It maps from the meta-data of other machine learning algorithms by evaluating it on different datasets. In this paper, we have presented very recent state-of-the-art research works on meta-learning. We have categorized meta-learning on supervised learning data sets into three categories: (1) Task Independent Recommendation, (2) Configuration Space Design, and (3) Configuration Transfer, and reviewed the recent works on each category.
Integration of Augmented Reality With Blended Learning to Educate Manufacturing and Industrial Engineering: A Conceptual Framework

The quality of the student's learning process has always been a concern and the educational paradigm requires a change in the method of the teaching and learning process to improve the quality of the education system. As such, there are a variety of modern teaching and learning methods currently in use that include traditional classroom methods, online learning, learning management systems (LMS), and massive open online courses (MOOCs), and this paves the way for blended learning (BL). In that respect, BL is capable and can provide extensive knowledge and experience beyond current conventional BL practices. Accordingly, there are many advanced technologies and tools, especially Augmented Reality (AR) and Virtual Reality (VR) supporting the teaching and learning process, which can be integrated with BL practice. Hence, integrating AR technologies along with BL will be the advancement of current BL practice. Accordingly, this study aims to integrate AR with BL to educate Manufacturing and industrial engineering as a conceptual framework. The main focus of this study is to conceptualize a development of an AR application that can be used to achieve higher-order level manufacturing and industrial engineering knowledge both at home and in classroom environments. Consequently, the AR application will be incorporated through a QR code and LMS, enabling students to experience and understand the curriculum from anywhere on a digital platform, especially in home and classroom environments.
Global Horizontal Irradiance Forecasting Using Hybrid Method: Convolutional Neural Network-Long Short Term Memory

Along with the growing demand for energy, it has encouraged researchers to develop technologies and methods to optimize renewable energy, especially solar energy. One of the most influential components of the quality of solar energy is Global Horizontal Irradiance. Uncertain characteristics due to meteorological factors are a challenge to establish an optimal method for forecasting, thus solar technology or photovoltaics can produce constant power to meet the power demand. Several methods have been studied, and each method has its own advantages and disadvantages. The combination of several methods is the right step to achieve optimal results in a forecast. In this paper, the author applied a hybrid method CNN-LSTM. The author uses a data set in one year, which is carried out every 15 minutes in a day. However, the data is processed only from 05.00 to 17.00, considering the characteristics of irradiance. The results of optimal forecasting are shown by curves. In addition, error metrics are also calculated to strengthen the evidence that the proposed method is superior. Likewise, the comparison with several other methods such as CNN, LSTM, and CNN-LSTM, presented the superiority of the proposed hybrid method.
Analyzing AI and the Impact in Video Games

As technologies advance, so is the AI. Progress in AI is routinely measured in isolation. However, it is also crucial to benchmark an AI progress not just in isolation, but also in terms of how it helps humans perform certain task. AI has been a great help to humans in many industries. One of them is the gaming industries. This paper examines different algorithm that are used in a game that contains AI. Our methods when constructing this paper are literature review and we gather people's thought using questioners and we analyzed the answer that they submitted. Our result shows that a games with more complex AI more popular than other games without AI. Our conclusion is that game with complex AI tend to be more popular because it is more challenging and fun to play.
Incremental Multi-view Clustering

With the digital revolution, different issues arise, putting the community in real challenge front to the variety and the quantitative explosion of digital data. Thus, these data are increasingly collected from multiple sources or comprised of several views. The supplementary information describes distinct perspectives of data which can significantly improve the clustering performance and give more accurate results. In this work, we study the problem of multi-view clustering and we propose an incremental multi-view clustering method by considering all the views. The proposed method handles data with any number of views, taking into account the diversity of views. To get a robust result, we propose a fusion strategy of all views using L2-norm and unit variance to guarantee the consistence and the correctness between views. Then, we give an iterative solution allowing to divide the set of views into incremental subspaces. We compare our method with a number of methods proposed in the recent literature. The empirical evaluations on two real-world dataset demonstrate the effectiveness and the potential of our method.
Enhancing Graph Variational Autoencoder for Short Text Topic Modeling with Mutual Information Maximization

Neural topic models can successfully capture thematic patterns of the document with black-box variational inference, but they still suffer from sparsity problem when facing short texts with limited contextual information. To alleviate the sparsity problem, some graph-based methods have been proposed to explicitly model the word co-occurrence patterns. However, they ignore sequential information and word relevance degree in the document, resulting in inaccurate topic representations. Therefore, we propose a novel graph-based neural topic model, namely mutual Information enhanced Graph Topic Model (InfoGTM), which leverages the sequential information and takes into account the word relevance degree into topic modeling using a more accurate semantic graph. More specifically, instead of pre-computing statistical word co-occurrence, we develop an automatic way to dynamically construct semantic graph with a multi-head attention mechanism, which integrates both contextual and words structure information into the semantic graph, thereby providing more accurate word co-occurrence information. After that, a graph variational auto-encoder topic modeling framework is adopted to generate topic proportions for each short text. To further enhance the topic representation, we maximize the mutual information between input words and topic representations to ensure more semantic information could be compressed. Besides, mutual information maximization could preserve the smooth manifold structure of short texts, which enables the spread the similar topic representation from neighboring short texts. Substantial experiments are conducted on several benchmark data sets that verify the superiority of our method compared to the state-of-the-arts regard to the topic coherence performance.
Encrypted Message Hiding On GIF Image Using the Gifshuffle Algorithm

This paper implements steganography application with Gifshuffle algorithm in the GIF image. The development of information technology gives a chance for people to share message and communicate through Local Area Network (LAN), internet, email, and mobile internet. To balance the rapid communication, some safety aspects are needed in sending the message. This study was purposing to hide the secret message by randomize the message's content. By using the Gifshuffle algorithm and Vigenere Cipher technique in hiding the message show the GIF image, the result was confronted with four aspects of steganography evaluation, such as: Imperceptibility, Fidelity, Recovery, and Robustness. As a result, the program was able to fulfill the recovery aspect besides its necessity for essential improvement in fulfilling the Imperceptibility, Fidelity, and Robustness aspects. Immediate implications are needed for the development of the algorithm Gifshuffle in the animated GIF image.
Evaluating Organization's Portfolio of Long-horizon Strategic Action Plans: An Integrated Delphi-DEMATEL-ANP Approach

Strategic foresight allows the organization to adapt and cope with the challenges of the next decade and beyond by revealing internal mental models, encouraging reflection and understanding complexity further on the boundaries of organizational knowledge. This paper thus demonstrates a novel method to facilitate collaborative planning using long-range foresight for an organization. Using the Delphi technique, the proposed method involves the identification of key products, periods of products realization, products dependency and ranking, and the formation of a portfolio of long-horizon strategic action plans that are perceived as achievable within the time horizon of 2030. The prioritization of these actions is then investigated using an integrated Decision Making Trial and Evaluation Laboratory (DEMATEL)-Analytic Network Process (ANP) approach.
A New Design of VQA System based on Weighted Contextual Features

Visual question answering (VQA) is a challenging task that requires a deep understanding of language and images. Currently, most VQA algorithms focus on finding the correlations between basic question embeddings and image features by using an element-wise product or bilinear pooling between these two vectors. Some algorithms also use attention models to extract features. In this paper, deeper analyses of these attention features are enabled by capturing their importance by weighting their contextual information. A novel interpretable VQA system leveraging weighted attention contextual features (WACF) is proposed for VQA tasks. This is a multimodal system which can assign adaptive weights to the features of questions and images themselves and to their contextual features based on their importance. Our new model yields state-of-the-art results on the MS COCO VQA datasets for open-ended question tasks.
Learning Vector Quantization (LVQ) For Colorectal Cancer Identification Based on Microscopic Network Image

Colorectal is a type of malignant cancer that occurs on the surface of the large intestine (colon) and the lower part of the intestine to the anus (rectum) due to environmental influences and unhealthy lifestyles. The main function of the large intestine is to reabsorb water and to secrete mucus which serves to lubricate and help expel feces and gases. Colorectal cancer malignancy can attack anyone, from toddlers, teenagers, and adults. Identification of colorectal cancer is still using hispathological examination. Where this examination is a diagnostic act carried out by taking samples of cells or tissues for analysis in the laboratory. The examination is still done manually, namely using a microscope. In this way, a doctor who has the knowledge, thoroughness and accuracy is needed. This inspection takes time and effort. Therefore we need a way to help doctors identify colorectal cancer through microscopic images of colorectal cancer so that the identification results obtained are more efficient and have a better level of accuracy than manual identification. The identification process using input data includes the number of epohs as much as 100, the number of hidden layers of 3, the learning rate of 0.05. The test in this test uses colorectal tissue image testing data with the IMG18 filename. Produces Normal identification results with an accuracy of 60%, meaning that img18 is identified as normal colorectal.
Sentiment Identification System for E-Commerce Mobile App Reviews Using Single Layer Neural Network

In the technological era, e-commerce offers business opportunities, particularly through the simplicity of the process of buying or selling products through the Internet. The upkeep of the customer experience must be recognized by e-commerce service providers as a top priority for businesses. Customers can access the global market, compare prices across regions, and even easily compare the services of various e-commerce apps. Online customer reviews on e-commerce mobile apps play an important role, which can be used as personal recommendations for other customers. Because customers rely on the opinions of other customers, negative reviews from customers will deter potential users from downloading the e-commerce mobile app in the future. The system described in this paper uses a single-layer neural network to automatically predict and analyze customer sentiments from online customer reviews. The proposed sentiment identification system model achieved the best performance among the algorithms; it attained an overall sensitivity of 96.2%, specificity of 93.8%, accuracy of 95.0%, and MCC of 0.90. Additionally, the researchers developed a fast and reliable web-based system for identifying sentiment from customer reviews.
Multi-Kernel-FM: A Multi-Embedding &amp; Kernelization Factorization Machine Framework for CTR Prediction

Click-Through Rate (CTR) Prediction is one of the most critical components in recommender systems, where the task is to estimate the probability that a user clicks an item. In CTR models, embedding methods are widely used in feature representation to map categorical features into lower dimensional vectors, and thus those representations can be further exploited by various machine learning algorithms such as Factorization Machines (FMs) for CTR prediction. However, in the literature, most existing embedding models can only extract one latent vector for each individual feature as they calculate the feature interaction based on simple element product or inner product, limiting its ability to model user-item interactions in a high-dimensional space. It may miss some deep and complex interacted latent features, and therefore lead to a less proper representation as well as an inaccurate prediction. Motivated by the status quo, in this paper, we therefore propose a novel Multi-Kernel-FM (MKFM) framework for the task of CTR prediction. First of all, an embedding-based approach called Multi-FM (MFM) is proposed. It uses multiple embedding strategy and considers multiple representation sub-spaces for representing user-item features. After that, we construct a MKFM framework which combines kernel function and MFM to capture non-linear feature interactions. Then, the concept of kernel function is introduced and employed for capturing more high-dimensional feature interactions to further improve prediction accuracy. The results of our experiments on four public datasets demonstrate the superiorities of the proposed framework to some existing methods with respect to both prediction accuracy and training cost.
A Comparison of Container Systems for Machine Learning Scenarios: Docker and Podman

With the rising needs of corporations and their users, as well as the ever-increasing complexity of applications, Container engines and technologies have become a pivotal point in every modern technological infrastructure. This has standardized execution runtimes and when combined with modern virtualization techniques, it has lowered costs and given access to the power of cloud computing to many more people. The aim of this paper is to compare two of the most popular container engines to see what differences exist in performance and architectural levels between the so-called “drop-in” replacements. To ensure consistency and replicability of testing, we standardize the benchmark environment with a custom-built tool that describes differences among container engines in the millisecond range. On this basis, we also present quantified results and compare differences with real-world metrics and pricing. From our results, we observe a small but not insignificant difference among the container systems, in favor of Docker. Our results are quantified with real-world pricing of cloud resources to figure out how much more costly Podman would be in a cloud deployment.
Learning from Crowds with Mutual Correction-Based Co-Training

Collecting labels from crowds is a feasible solution for large knowledge graph creation. However, errors in crowd-sourced labels damage the predictive model training. Although various methods have been developed to correct noisy labels, these traditional methods on the one hand do not take full advantage of the information from crowdsourced labels, and on the other hand do not perform well facing complicated annotation conditions, especially when some crowd workers make highly correlated mistakes. To address this issue, this paper proposes a novel learning from crowds method, which can simultaneously correct label noises and learn predictive models. The proposed method trains two data classifiers and a crowd aggregator. Each data classifier divides crowdsourced labeled data into a clean set and a noisy set and predicts the labels of the instances with a large loss in the noisy set. These instances are removed from the noisy set and put into the clean set. Then, the loss of the classifier is updated using the new clean and noisy sets. The data classifier with smaller loss is selected and trained with the crowd aggregator by maximizing their mutual information. Simultaneously, the smaller loss is back propagated to the other data classifier for training it. Experimental results on both synthetic and real-world datasets consistently show that the proposed method significantly outperforms the state-of-the-art methods in noise correction and predictive model learning.