Reviewerly/exHarmony huggingface dataset:
Fashion-MNIST Clothing Classification using Deep Learning

Online fashion market is constantly growing, and an algorithm capable of identifying garments can help companies in the clothing sales sector to understand the profile of potential buyers and focus on sales targeting specific niches, as well as developing campaigns based on the taste of customers and improve user experience. Artificial Intelligence approaches able to understand and label humans’ clothes are necessary, and can be used to improve sales, or better understanding users. Convolutional Neural Network models have been shown efficiency in image c1assification. It presents four different Convolutional Neural Networks models that used Fashion-MNIST dataset. Fashion-MNIST is a dataset made to help researchers finding models to classify this kind of product such as clothes, and the paper that describes it presents a comparison between the main classification methods to find the one that better label this kind of data. The main goal of this project is to provide future research with better comparisons between classification methods.
Enhancing E-Commerce Applications with Machine Learning Recommendation Systems

In today’s times everything has moved to a digital platform. Even commerce has moved to a digital mode with people now preferring to buy things online rather than going to a physical store. Recommendation Systems are used in such platforms to help users. Recommendation System is one of the most popular application of Machine Learning with various techniques and algorithms to implement it. We have researched these algorithms and have presented an analysis by taking various factors into consideration.
Exploring the Dynamics of Affordance Actualization – A Configurational View on Voice Assistants

Voice assistants’ (VAs) increasingly nuanced and natural communication opens up new opportunities for the experience of users, providing task assistance and automation possibilities, and also offer an easy interface to digital services and ecosystems. However, VAs face various problems, such as low adoption and satisfaction rates as well as other negative reactions from users. Companies, therefore, need to consider how individuals utilize VAs and what contributes to user satisfaction. Key for the design of VAs are their unique affordances and their agentic nature that distinguish these IT artifacts from non-agentic IS. A configurative and dynamic approach enables to shed light on the complex causalities underlying user outcomes with these novel systems. Consequently, we examine in this study how individuals actualize the affordances of VAs during the initial adoption stage. For this purpose, we draw on a diary study research design that examines affordance actualization processes with new VA users. We examine with a configurational approach, how the actualization of VA affordances contributes to the outcomes of VAs, i.e., in our case user satisfaction. The results of our diary study show distinct patterns of functional affordance configurations. In addition, we show that affordances unfold and evolve over time. The derived implications provide a configurative theoretical understanding for the role of VAs affordances for user satisfaction that provides practitioners useful guidance to actualize the potential of VAs.
Employee Background Verification on Blockchain

In any hiring process, verifying the accuracy of the information provided by the recruiter is the first step in establishing compliance with the company and the employee. For the same reason, miles are too sensitive to make a complete history check for each hired student, without exemption. The study record reveals that employers around the world face a huge loss annually due to misrepresentation of confidential information by hired applicants. Particularly in India, it has been reported that one in six applicants misrepresents themselves. Such alarming data has accelerated the search and visibility of historical experiments in recent years. With the global transformation of the job market, it is very difficult to get a job in a company. People use their curriculum vitae with incorrect details to find the process. International organizations are investing closely to verify the historical facts of job applicants. However, the background verification process completed by those organizations is expensive, time-consuming, and inefficient. To address this issue, we have introduced a powerful response to the use of Design Science Research Methodology where the hash values of all application files offered by multiple companies are stored within the consortium blockchain. Details of the applicant's process can be established at a specific time in the hiring process by checking the hash file value given the document hash value that will be available within the blockchain. This method can be efficient, time-consuming, and cost-effective to include all types of companies.
Object Detection with Theft Alert Using Tensorflow and Yolo

Theft is a common criminal activity that is prevailing over the years and is increasing day by day. To tackle this problem many surveillance systems have been introduced in the market. Some are simply based on video surveillance monitored by a human while some are AI-based capable of detecting suspicious activity and raising an alarm. However, none of them are intelligent enough to identify what kind of suspicious activity is being carried out and what kind of protective measures should be taken in real-time. This blog presents the design of an effective surveillance system using machine learning techniques.
Enhancing Response Time of Cloud Resources Through Energy Efficient Cloud Scheduling Algorithm

Cloud Computing is becoming a dominant trend in providing information technology (IT) services. The cloud comprises many hardware and software resources today, and more people are switching to such services. Users' requests for cloud resources must incur a minimum amount of load on the system while getting a rapid response. In the cloud today, there is too much computational power. Load balancing makes it possible for various components of the cloud computing environment to work efficiently. To balance client requests to available resources so that the system is not overloaded, and the requested resources are delivered as quickly as possible, an effective load balancing strategy is essential. In this research article, we have presented a critical analysis of various existing cloud load balancing and scheduling algorithms. Several task scheduling approaches have been proposed in the literature review, but there appears to be a lack of scheduling algorithms for real-time task works based on historical scheduling records (HSR). The proposed algorithm uses information available in HSR to efficiently distributes incoming user requests to available virtual machines. The proposed scheduling algorithm uses the scaleup and scale down resource algorithm which helps in achieving maximum resource utilization. The algorithm tries to balance the load on VMs by scaling up and down cloud resources. WorkflowSim is used to analyze the performance of the algorithm proposed. The simulation results are compared with the existing scheduling algorithm which shows the proposed algorithm outperforms existing scheduling algorithms in terms of makespan.
Privacy-Preserving Min–Max Value Retrieval Using Data Obfuscation Algorithm in Distributed Environment

A comprehensive and efficient p-based algorithm is being proposed to retrieve minimum and maximum of particular data field of database tuple distributed as a horizontal partitioning of single database table among the multiple servers without losing the privacy of individual server. Data obfuscation technique is used in the proposed algorithm to preserve the privacy of individual server. This algorithm is useful in many practical scenarios where privacy-preserving data computing is desirable.
Implementing Domain-Driven Design (by V. Vernon)

This article provides a review of the book Implementing Domain-Driven Design, written by Vaughn Vernon and published by Addison Wesley, © 2013, ISBN-13: 978-0-32183457-7, 612 pp., $64.99.
The Mediating Role of Open Innovation Mindset on the Relationship Between Human Resource Management and Knowledge Management Toward Innovation Performance in the Jordanian SMEs

This paper explores the impact of human resource management and knowledge management on innovation performance, the relationship mediated by open innovation mindset. Thus, this study targeted the Jordanian SMEs as a population of the study, the researcher reviewed around 50 articles published in the last five years to catch up with latest findings. The findings approved the need of the SMEs in Jordan for such factors to develop the industry and contribute in the economy development, which means there is an innovation performance issue in the industry. Human resource management found as one of the top factors effect innovation performances in Jordanian SMEs, knowledge management as an essential factor toward activate and enhance the human resource presence in the firms. Finally open innovation mindset found as not studied and explored factor in the industry, which indicates an issue in the implementation of innovation performance in the country. Furthermore, the researcher recommends the future research to hold studies on open innovation mindset as well empirically examine the role on open innovation mindset role in Jordanian SMEs industry. This could clearly figure out the role of open innovation mindset on the implementations of innovation performance in Jordanian SMEs.
Item-Provider Co-learning for Sequential Recommendation

Sequential recommender systems (SRSs) have become a research hotspot recently due to its powerful ability in capturing users' dynamic preferences. The key idea behind SRSs is to model the sequential dependencies over the user-item interactions. However, we argue that users' preferences are not only determined by their view or purchase items but also affected by the item-providers with which users have interacted. For instance, in a short-video scenario, a user may click on a video because he/she is attracted to either the video content or simply the video-providers as the vloggers are his/her idols. Motivated by the above observations, in this paper, we propose IPSRec, a novel Item-Provider co-learning framework for Sequential Recommendation. Specifically, we propose two representation learning methods (single-steam and cross-stream) to learn comprehensive item and user representations based on the user's historical item sequence and provider sequence. Then, contrastive learning is employed to further enhance the user embeddings in a self-supervised manner, which treats the representations of a specific user learned from the item side as well as the item-provider side as the positive pair and treats the representations of different users in the batch as the negative samples. Extensive experiments on three real-world SRS datasets demonstrate that IPSRec achieves substantially better results than the strong competitors. For reproducibility, our code and data are available at https://github.com/siat-nlp/IPSRec.
An Adaptive State Supervising Method for Power Data Blockchain Based on Relay Mechanism

With the continuous advancement of Power Blockchain constructions, many domestic companies have established multiple blockchain applications such as Smart Contract, Smart Finance, Energy Cloud and so on. While blockchain is in the stage of developing, thus there exists a lot of problems on these established blockchain applications. Supervision on chain data is one of these problems and it is always difficult, in order to resolve this problem, we propose a supervising method based on relay mechanism to monitor the chain data adaptively and to detect and control the abnormal data timely. We generate the data according to the data structure of the power distribution automation system for anomaly detection. The experimental results show that the proposed supervision algorithm detects abnormal power data in time, and adaptively updates the algorithm parameters according to the changes of the data on the chain, ensuring the accuracy of the detection results, and can help the supervisory unit to supervise the data on chain.
Multilingualism in Lebanon

Lebanon is a diverse multicultural country, and multilingualism has always shaped the language use of most Lebanese people. In particular, code-switching has become one of the most distinctive features of the mosaic that is Lebanese society and, consequently, a cultural representation of those who code-switch. For this research, a corpus of 1680 SMS messages, the technology of sending short text messages from one mobile phone to another, was collected from 58 Lebanese undergraduates in order to explore the linguistic features associated with code-switching in the SMS messages of Lebanese undergraduates. Data analysis shows significant mixing of Romanized Lebanese Arabic, English and French. All the messages that included Arabic were written in Romanized script. The results show that students are using code-switching as a means to translate their socioeconomic or sociocultural background. Code-switching has become their own way to express their identifiable ideas, beliefs, cultures and feelings and then represent them to others. The findings reveal that code-switching in young people’s SMS messages is governed by diverse factors and reflects different aspects of those users such as their cultural, social and religious backgrounds, as well as their linguistic repertoire. The data collected through this study reveals distinctive features of code-switching in the SMS text messages of undergraduates, along with an understanding of their evolving linguistic identity.
A social media platform using blockchain technology: Topic analysis and sentiment analysis of steemit user-generated content

The "blockchain + media" model is showing great vitality. In this paper, we study the detection of the most common topics in Steemit. Compared to the currently popular centralized online social media, the blockchain-based decentralized online social media platform presents a different business model, and thus different or platform-specific topics emerge. We extract knowledge from content posted on Steemit, using NLP term frequency and topic modeling techniques combined with sentiment analysis theory, with the aim of studying the topic preferences and sentiment preferences of user-generated content. Our research will provide a more comprehensive understanding of identified themes and detect strategies adopted by users to add value to user-created content; provide guidance for business model improvements; and enrich existing interdisciplinary research.
What the Actual...Examining User Behaviour in Information Retrieval

Conducting studies involving actual users is a recurring challenge in information retrieval. In this tutorial we will address the main strategic and tactical choices for engaging with, designing and executing user studies, considering both evaluation and formative investigation. The tension between reproducibility and ensuring natural user behaviour will be a recurring focus, seeking to help individual researchers make an intentional and well-argued choice for their research. The presenters have over fifty years of combined experience working in interactive information retrieval, and information interaction in general.
Accounting Information System Reporting the Use of School Operational Costs (Case Study: SDIT Daar Ilmi Waykanan)

Every school that operates always issues and records the use of operational costs. Operational costs are commercial costs incurred by companies/agencies to support or support the operating activities of companies/agencies. These expenditures usually use the existing cash funds in the company/institution with a relatively small nominal. Similar to the process of managing school operational activities in general, SDIT Daar Ilmi Waykanan has used computers and manual bookkeeping in managing all financial recording activities. The computer application used is Microsoft Excel. Even though they already use a computer, there are still very frequent calculation errors and difficulties in manual recording. Because reporting on the use of operational costs must be done every month the school also needs time in conducting reports. There are frequent loss of documents and miscalculations and very wasteful of books/papers.Therefore, as a solution to the problems above, the authors build a web-based reporting system for the use of school operational costs, so that recording and reporting can be timely and more efficient. The recording method used in the development of the accounting information system for reporting the use of operational costs is the Imprest method where monthly recording is carried out repeatedly with the same nominal and time period. The method in developing this system uses the Prototype method, the prototype method itself is a software development method that intermediaries between developers and potential users, also described as an early version of a detailed description of a system desired by potential users. The results of this study indicate that the accounting information system for reporting the use of operating costs is very helpful for the school. Documents in reporting have been developed by adding several types of documents including general journals, cash receipts journals, cash disbursements journals and ledgers. This system can also generate reports of all incoming and outgoing funds.
PESrank: An Explainable online password strength estimator1

Human-chosen passwords are the dominant form of authentication systems. Passwords strength estimators are used to help users avoid picking weak passwords by predicting how many attempts a password cracker would need until it finds a given password. In this paper we propose a novel password strength estimator, called PESrank, which accurately models the behavior of a powerful password cracker. PESrank calculates the rank of a given password in an optimal descending order of likelihood. PESrank estimates a given password’s rank in fractions of a second – without actually enumerating the passwords – so it is practical for online use. It also has a training time that is drastically shorter than previous methods. Moreover, PESrank is efficiently tweakable to allow model personalization in fractions of a second, without the need to retrain the model; and it is explainable: it is able to provide information on why the password has its calculated rank, and gives the user insight on how to pick a better password. We implemented PESrank in Python and conducted an extensive evaluation study of it. We also integrated it into the registration page of a course at our university. Even with a model based on 905 million passwords, the response time was well under 1 second, with up to a 1-bit accuracy margin between the upper bound and the lower bound on the rank.
Recognition of Problematic Educational Situations in Computer Modeling Training

The article presents the results of the training in Computer Modeling in the third and fourth grades. Through the methods of participatory observation, interviews, discussions, surveys, and content analysis, data was collected, analyzed and systematized by teachers, principals, parents, university methodologists and IT administrators for problematic educational situations arising from technical reasons. An identification system was developed for their establishment. It is used to assess and measure changes in the complexity of the learning process and the workload of the teachers in the Computer Modeling subject in the event of problematic educational situations. In conclusion, guidelines for overcoming and resolving the identified unforeseen situations are provided.
Динамичният софтуер в помощ на изследване на задачи от практиката

The article describes some mathematical problems that arise in everyday life. Using GeoGebra non-trivial dynamic models describing such problems are constructed. GeoGebra is an interactive geometry, algebra, statistics and calculus application, intended for learning and teaching mathematics and science from primary school to university level. GeoGebra is available on multiple platforms and operating systems.
Converting Numeral Text in Bulgarian into Digit Number Using GATE

The Internet serves billions of users providing a variety of information resources whereby a lot of the information is presented in natural human language and needs an efficient approach to be processed. Natural language processing (NLP) refers to the ability of computers to analyze and understand the structure of human language. By utilizing NLP this linguistic knowledge is transformed into algorithms for solving specific problems. GATE is widely used, open-source software infrastructure that provides a framework and components for solving NLP tasks. The available GATE tools can be adapted to other languages and text processing tasks. This article will present an approach for converting numeric data, written as words in Bulgarian, into digit numbers. For this case, a relevant conﬁguration ﬁle for Bulgarian has been integrated into the general tool set in the open source software for natural language processing GATE. The aim of this survey is to determine the exact numeric value of Bulgarian text numeric data, which can be used as a starting point for producing more complex annotations, such as monetary measurement units, etc.
Reading Activities at Rumah Langit Foundation as Implications of Indonesian Learning

Reading is an activity that is very beneficial for everyone. Many things can be obtained in life, if someone is diligent in reading. In the world of education, making reading one of the students' habits is a hope for all parents and teaching staff in schools. The development of this reading activity cannot be separated from the great interest of the students to want to do it and the Indonesian language learning which is well managed by the teachers at the school. Reading, which essentially includes the study of the Indonesian language and is used as one of the important teaching materials, is expected to be able to arouse students' interest in reading. Language teachers are expected to be able to help students overcome reading difficulties by teaching good reading techniques and utilizing existing facilities so that reading culture can be realized in schools
Multi-Agent RL-based Information Selection Model for Sequential Recommendation

For sequential recommender, the coarse-grained yet sparse sequential signals mined from massive user-item interactions have become the bottleneck to further improve the recommendation performance. To alleviate the spareness problem, exploiting auxiliary semantic features (\eg textual descriptions, visual images and knowledge graph) to enrich contextual information then turns into a mainstream methodology. Though effective, we argue that these different heterogeneous features certainly include much noise which may overwhelm the valuable sequential signals, and therefore easily reach the phenomenon of negative collaboration (ie 1 + 1 > 2). How to design a flexible strategy to select proper auxiliary information and alleviate the negative collaboration towards a better recommendation is still an interesting and open question. Unfortunately, few works have addressed this challenge in sequential recommendation.
Knowledge management: a practical guide for librarians (Practical guides for librarians, 73)

"Knowledge management: a practical guide for librarians (Practical guides for librarians, 73)." Journal of Electronic Resources Librarianship, 34(2), pp. 158–159