Reviewerly/exHarmony huggingface dataset:
Usage and efficiency analysis of electricity generated by PV power plant in a marina or industrial facility

In this study, the analysis of the usage efficiency and the time allocation to cover the initial investment cost of a PV power plant to be installed in an industrial facility having constant electricity consumption monthly and a yacht marina having fluctuating electricity consumption in a year (equal yearly electricity in each in total) within the installation assumption of same conditions of PV plant has been done. The PV power plant data with 680 kWp power and located at the same latitude as the yacht marina in the center of Mediterranean were used. For this purpose, it has been calculated that the PV power plant installed power required for the annual electricity need of the yacht marina will be 1357 kWp. With this power, 2,141,117 kWh of electricity has been produced annually and the yacht marina/industrial facility need has been supplied. In addition, the Net Present Value (NPV) was used to calculate the coverage period for the initial investment cost of the PV plant. In the calculations, it has been determined that the time to cover the initial investment cost is 4.31 years in the yacht marina and 4.37 years in the industrial facility. This result showed that installing PV power plants in marinas would be 1.5% more advantageous than other industrial facilities.
Self-concept of students of additional training programs in the field of management

The paper deals with the key psychological factors of professional realization and social success of people participating in executive training programs in the field of management in the adulthood. The modern concept of continuing education needs to investigate the psychological and economic problems of adult learning. This research is devoted to the study of the self-concept as an important and insufficiently studied mechanism for regulating human behavior. The choice of training in MBA programs is considered as a manifestation of an active strategy of human adaptation to dynamic social and economic conditions. The study centers on the personal-activity and competenced-based approaches. The purpose of the study is to analyze the features of selfesteem and its role in the professional adaptation of adult students of executive programs (Master of Business Administration). The study used well-known psychological questionnaires and methods of descriptive and multidimensional analysis of data processing. The study revealed that the self-concept of students of Master of Business Administration programs plays a significant role in the choice of training as a way of professional development of managers. The inclusion of the emotional-evaluative component of the self-concept in the personal potential of students was statistically confirmed. The results indicate possible intrapersonal conflicts of students in the process of learning management as a significant field of professional activity. The study identified such factors as “problematic introversion”, “problematic creativity”, “problematic adaptability” and “self-critical leadership” which show the importance of accuracy of self-assessment for the choice of the content of training and the direction of self-development. The results of the study are important for understanding of the personal mechanisms of professional and career selfrealization of adults in the context of continuing education as well as for assessment of the effectiveness of receiving executive education.
End-to-End Kurdish Speech Synthesis Based on Transfer Learning

A text-to-speech (TTS) system converts the texts into speech in a specific language. Several TTS systems generate natural-like speech signals in numerous languages, such as English. On the other hand, the Kurdish language has just been examined. Existing preliminary research on Kurdish speech synthesis has utilized old methods and has generated low-quality speech. They also lack important aspects of speech, including intonation, emphasis, and rhythm. Some approaches were presented to address these challenges, including the use of concatenative systems. For example, the unit selection or statistical parametric methods. On the other hand, they need a great deal of time, effort, and domain knowledge. An additional factor for Kurdish speech synthesizers' low performance is the absence of publicly available speech corpora, unlike English, which has many freely-available corpora and audiobooks. The motivation of this paper is to create a Central Kurdish speech corpus and generate a human-like speech from the Kurdish text. This paper explains how to utilize Tacotron 2, an end-to-end neural network architecture and HiFi-GAN vocoder, to produce a high-quality, realistic, and human-like Kurdish voice. This work utilizes "text, audio" pairings, which contain 10 hours of recorded audio samples and texts collected from the Internet and textbooks. It shows how to use English character embedding as the pre-trained knowledge with Kurdish characters as input and how to preprocess these audio examples to get a great outcome. Our evaluations for various types of texts show a mean opinion score of 4.1, comparable with state-of-the-art synthesizers in other languages.
Question Answering with Additive Restrictive Training (QuAART): Question Answering for the Rapid Development of New Knowledge Extraction Pipelines

Abstract Numerous studies have explored the use of language models and question answering techniques for knowledge extraction. In most cases, these models are trained on data specific to the new task at hand. We hypothesize that using models trained only on generic question answering data (e.g. SQuAD) is a good starting point for domain specific entity extraction. We test this hypothesis, and explore whether the addition of small amounts of training data can help lift model performance. We pay special attention to the use of null answers and unanswerable questions to optimize performance. To our knowledge, no studies have been done to evaluate the effectiveness of this technique. We do so for an end-to-end entity mention detection and entity typing task on HAnDS and FIGER, two common evaluation datasets for fine grained entity recognition. We focus on fine-grained entity recognition because it is challenging scenario, and because the long tail of types in this task highlights the need for entity extraction systems that can deal with new domains and types. To our knowledge, we are the first system beyond those presented in the original FIGER and HAnDS papers to tackle the task in an end-to-end fashion. Using an extremely small sample from the distantly-supervised HAnDS training data – 0.0015%, or less than 500 passages randomly chosen out of 31 million – we produce a CoNNL F1 score of 73.72 for entity detection on FIGER. Our end-to-end detection and typing evaluation produces macro and micro F1s of 45.11 and 54.75, based on the FIGER evaluation metrics. This work provides a foundation for the rapid development of new knowledge extraction pipelines.
CienTec Guide: Application and Online Evaluation of a Context-Based Recommender System in Cultural Heritage

A Recommender System (RS) is best applied in situations where users have to decide to choose among a list of usually many options and visits in cultural heritage sites are an example of that. Visitors may also face problems in finding how to reach their options. This research addresses both problems with a mobile app consisting of a hybrid context-based RS that suggests personalized visiting routes with the goal to maximize user satisfaction and minimize the length of the recommended route. Unlike most published RS papers related to cultural heritage, the system in this research was built for the mobile platform and its effectiveness was evaluated with actual visitors of a museum. The results were consistent in indicating the improved system achieved high user satisfaction, with all the recommender attributes average ratings between 4.3 and 4.7 (in a scale of 1 to 5), and accuracy, with a Mean Average Error (MAE) of 0.69.
Projeto de Bancos de Dados NoSQL

This book of the XXXVII Brazilian Symposium on Databases (SBBD 2022) includes three book chapters written by the authors of the selected short courses and presented in the edition of the event held from September 19 to 23, 2022. The chapters address issues related to Natural Language Processing, NoSQL Database Design, and Using Meta-Learning in Deep Learning tasks. The short course program committee was composed of professors Ticiana L. Coelho da Silva (UFC), Damires Yluska de Souza Fernandes (IFPB), and Anne Magály de Paula Canuto (UFRN), under the coordination of the former. The richness of this issue can be mainly credited to the authors and reviewers. We thank them a lot for their insightful contributions and discussions during SBBD 2022.
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation

Large pretrained generative models like GPT-3 often suffer from hallucinating non-existent or incorrect content, which undermines their potential merits in real applications. Existing work usually attempts to detect these hallucinations based on a corresponding oracle reference at a sentence or document level. However ground-truth references may not be readily available for many free-form text generation applications, and sentence- or document-level detection may fail to provide the fine-grained signals that would prevent fallacious content in real time. As a first step to addressing these issues, we propose a novel token-level, reference-free hallucination detection task and an associated annotated dataset named HaDes (HAllucination DEtection dataSet). To create this dataset, we first perturb a large number of text segments extracted from English language Wikipedia, and then verify these with crowd-sourced annotations. To mitigate label imbalance during annotation, we utilize an iterative model-in-loop strategy. We conduct comprehensive data analyses and create multiple baseline models.
FedRecAttack: Model Poisoning Attack to Federated Recommendation

Federated Recommendation (FR) has received considerable popularity and attention in the past few years. In FR, for each user, its feature vector and interaction data are kept locally on its own client thus are private to others. Without the access to above information, most existing poisoning attacks against recommender systems or federated learning lose validity. Benifiting from this characteristic, FR is commonly considered fairly secured. However, we argue that there is still possible and necessary security improvement could be made in FR. To prove our opinion, in this paper we present FedRecAttack, a model poisoning attack to FR aiming to raise the exposure ratio of target items. In most recommendation scenarios, apart from private user-item interactions (e.g., clicks, watches and purchases), some interactions are public (e.g., likes, follows and comments). Motivated by this point, in FedRecAttack we make use of the public interactions to approximate users' feature vectors, thereby attacker can generate poisoned gradients accordingly and control malicious users to upload the poisoned gradients in a well-designed way. To evaluate the effectiveness and side effects of FedRecAttack, we conduct extensive experiments on three real-world datasets of different sizes from two completely different scenarios. Experimental results demonstrate that our proposed FedRecAttack achieves the state-of-the-art effectiveness while its side effects are negligible. Moreover, even with small proportion (3%) of malicious users and small proportion (1%) of public interactions, FedRecAttack remains highly effective, which reveals that FR is more vulnerable to attack than people commonly considered.
Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data

In many real-world machine learning applications, samples belong to a set of domains e.g., for product reviews each review belongs to a product category. In this paper, we study multi-domain imbalanced learning (MIL), the scenario that there is imbalance not only in classes but also in domains. In the MIL setting, different domains exhibit different patterns and there is a varying degree of similarity and divergence among domains posing opportunities and challenges for transfer learning especially when faced with limited or insufficient training data. We propose a novel domain-aware contrastive knowledge transfer method called DCMI to (1) identify the shared domain knowledge to encourage positive transfer among similar domains (in particular from head domains to tail domains); (2) isolate the domain-specific knowledge to minimize the negative transfer from dissimilar domains. We evaluated the performance of DCMI on three different datasets showing significant improvements in different MIL scenarios.
IDENTIFICATION AND ANALYSIS OF STRUCTURAL-TECTONIC FEATURES OF GEOLOGICAL TERRAINS USING LINEAMENT ANALYSIS: EXAMPLES OF GEOMODELLING FOR CANADIAN AND UKRAINIAN SHIELDS

Nowadays, rare earth elements (REEs), which belong to the group of rare metals, are considered worldwide to be strategic critical raw materials and are extremely important for the economic development of any country. Various methods and approaches are used for prospecting and exploration of deposits of these critical metals; among which the methods of 3D geological modeling are currently prioritized, which allow a comprehensive analysis of the structural features of potentially promising areas as well as individual deposits. One of the methods used for REE exploration is structural mapping combined with geological terrain analysis, including structural lineament analysis. The latter is considered an important geological tool for identifying the primary and secondary structural and tectonic features of our study areas of investigation. The objectives of the present research work are: 1) to identify structural lineaments within two studied areas – the Alces Lake area (Northern Saskatchewan, Canadian Shield) and the Western Azov region (Azov block of the Ukrainian Shield) using automated and manual approaches, 2) to compare the results obtained for both areas, and 3) to discuss interpretation/conclusions over the overall suitability of the method for the exploration purposes. In the current research, we conducted the extraction and geospatial analysis of linear features and their tectonic interpretation. During the modeling process, remote sensing and geostatistical methods were used to analyze topographic, geological and geophysical data. As a result, the main structural lineament trends for the two studied areas were identified and structural-tectonic criteria for the formation and localization of deposits of rare earth elements were determined/proposed.
Mobile Learning Model for Children with Special Learning Needs

The mobile learning developed for children with special learning needs have limited users even within the same learning disability because severity in learning disability varies greatly even within a specific learning disability. Mobile learning developed for these children currently uses the same model used by any mobile application development where it caters for the masses rather than focusing on personalisation. The objective of this paper is to propose a mobile learning model for children with special learning needs, named APIE. ADDIE model is a generic instructional system design model that consists of processes: Analysis, Design, Develop, Implement and Evaluate. APIE is a modification from the ADDIE model, which makes APIE a mobile learning application development model. APIE iterates the design and develop processes under the Personalisation stage to explore the most suitable way for customisation for individual learning. APIE is applied to the development of a mobile learning for Dyslexic Children to assist the children to read and spell. Through APIE, a mobile learning application, DysleRead is produced. DysleRead integrates two classroom approaches: sight word and phonic where teachers can set and monitor individual learning needs of the children. The teachers find DysleRead helpful to teach the children. This shows that the mobile learning application produced using APIE meets the objective to cater for personalisation within a specific learning need without undermining the role of the teachers. Hypothetically, APIE model is applicable to other special learning needs too.
Technology Review: The Perfect Accent App

Automated speech recognition (ASR) is a feature of computer-assisted pronunciation training (CAPT) that can provide many benefits such as individualized instruction, a safe environment for pronunciation practice, and increased motivation and engagement (McCrocklin, 2019). Perfect Accent is a language-learning mobile application created by Virtual Immersive Educational Worlds, Inc. that is designed to improve a user’s accent at the individual sound level using sentences from different contexts. Users can choose from over forty different categories, including everyday tasks, various English for Specific Purposes contexts, and TOEFL. The creators claim that using the app improves the users’ pronunciation which can in turn help them get a better job, develop better social relationships, and be more clearly understood.
An Invigorating Perspective of a Practical Aspect of NoSQL Database

The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.
SISTEM PENUNJANG KEPUTUSAN PENERIMAAN KARYAWAN BARU MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) PADA PT CRESTEC INDONESIA CIKARANG

Employees are the most essential part of the company. To obtain qualified and qualified employees, companies usually hold a selection process. The selection process is carried out before the employee is declared accepted. In the process of recruiting prospective employees, PT. Crestec Indonesia is still using the manual method. A large number of applicants makes it very difficult for HRD to choose the right employee candidates who meet the criteria to be accepted to work in the company. The absence of a computerized system and the right method is one of the inhibiting factors in making decisions to accept new employees in the company. The purpose of the selection of recruitment is to obtain the right and qualified employees so that employees can work optimally in the company. To improve the new employee recruitment system that is faster and more precise, a web-based computerized system is needed by applying the SAW (Simple Additive Weighting) method which can facilitate HRD in the decision-making process of hiring new employees. The results of the Decision Support System by applying the SAW method are getting a ranking process so that it becomes the best recommendation in accepting new employees.
MogoDB: A NoSQL Database with Amazing Advantages and Features

The extremely compressed data stores on non-relational database management systems, commonly referred to as NoSQL data stores, which facilitate the management of data for internet user programs, do not now provide this feature. Some of the words that come to mind are data security, encryption methods, MongoDB, and NoSQL. Without negatively compromising the database's performance in terms of speed or memory utilization, the appropriate encryption methods utilized to various essential data fields provide data protection. This is possible because the right encryption methods are being used. Databases are often used to manage different types of data since document-oriented and unstructured data are typically stored there. These databases include Cassandra, MongoDB, CouchDB, Redis, Hyper-table, and others. They must provide strong security and safeguard users' private data when it is in use or at rest because they are open source. There is a need for a single solution that may enhance data transmission security by offering an improved encryption technique that expedites operations and consumes less memory when managing databases.Today's IT growth is attributed to the development of more data-intensive applications. Due to stringent limitations on data structure, data relations, and other factors, relational databases do not allow us to work with huge data sets or maintain high volume databases. Unstructured data from many industries, including a variety of forms, must be processed and stored in databases. Thus, NoSQL types offer solutions to a number of problems associated with huge data bases. Largely because of benefits like flexibility and horizontal scalability, NOSQL (Not Only SQL) is in demand. This essay explores many NoSQL database types, such as MongoDB, CouchDB, HBase, Cassandra, etc.
Komparasi Fungsi Aktivasi Relu Dan Tanh Pada Multilayer Perceptron

Neural network is a popular method used in machine research, and activation functions, especially ReLu and Tanh, have a very important function in neural networks, to minimize the error value between the output layer and the target class. With variations in the number of hidden layers, as well as the number of neurons in each different hidden layer, this study analyzes 8 models to classify the Titanic's Survivor dataset. The result is that the ReLu function has a better performance than the Tanh function, seen from the average value of accuracy and precision which is higher than the Tanh activation function. The addition of the number of hidden layers has no effect on increasing the performance of the classification results, it can be seen from the decrease in the average accuracy and precision of the models that use 3 hidden layers and models that use 4 hidden layers. The highest accuracy value was obtained in the model using the ReLu activation function with 4 hidden layers and 50 neurons in each hidden layer, while the highest precision value was obtained in the model using the ReLu activation function with 4 hidden layers and 100 neurons in each hidden layer