Reviewerly/exHarmony huggingface dataset:
Books Received

Journal of Analytical PsychologyVolume 68, Issue 5 p. 938-941 Books Received Books Received Stephen Garratt, Corresponding Author Stephen Garratt [email protected] orcid.org/0009-0002-9756-0014 London, UKSearch for more papers by this author Stephen Garratt, Corresponding Author Stephen Garratt [email protected] orcid.org/0009-0002-9756-0014 London, UKSearch for more papers by this author First published: 18 September 2023 https://doi.org/10.1111/1468-5922.12955Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume68, Issue5November 2023Pages 938-941 RelatedInformation
Machine learning model for identification of frontend and backend repositories in Github

Frontend and Backend repositories are the main components of the multi-repository structure. The developers or team managers upfront mostly do the identification of these repositories, but in Github, there is almost no tool or third party identification to identify these repositories. Since Github is the largest open source platform and is used by, many researchers and identification of these two repositories are crucial. This paper shows how this problem can be solved by building a machine learning model based on the file structure of the repository. The model has proven to be very successful with an accuracy of over 90% and can be used not only to identify the repository type, but also to identify the development language and environment.
Migrating data from document-oriented database to graph-oriented database

In data migration between different types of NoSQL databases, data may not be directly transferred to the targeted database compared to migration of data between the same types of database. This is due to the heterogeneity of the storage paradigm of the NoSQL databases. For example, when migrating data from a document-oriented database such as MongoDB, which stores data in JSON (Java Object Notation) format to Neo4j, a graph-oriented database stores data in nodes, the differences among these databases’ storage paradigms require different representations of the data model in the targeted graph-oriented database. This paper proposed a sequential approach to migrate data from MongoDB to Neo4j. The approach migrates MongoDB data to Neo4j and verifies the migrated data using a comparative method. The paper discusses the migration algorithm and how complex fields in MongoDB, such as nested documents, are presented in Neo4j.
Rational planning of geological exploration for porphyry copper objects within the Meldek intrusive-dome structure in the Magadan Region, based on application of 3D geological and geophysical modeling and neural networks

The article describes a method that makes it possible to decide, based on the 3D mathematical modeling, statistical methods, and artificial intelligence, on the effectiveness of application of various geophysical measurements. Several well-studied porphyry copper prospects were used as reference objects, for which regular relationships were calculated between the petrophysical parameters, determined using geophysical techniques (magnetic permeability, electrical resistivity, induced polarization, and others), and the geological and geochemical parameters. Verification of the method was carried out for the region of the Meldek dome structure, where ore occurrences of various types were concentrated, the area assessed by specialists as poorly studied in terms of geology. The detailed studies were carried out at the Lora and Talnikovy prospects. A priori reference models were developed for them. Results of the modeling of the characteristics were applied to the planning of a field experiment for a new site, namely the Shkhiperskaya area. Objects at this area are currently at the exploration and assessment phase.
My Life with the R-universe

&lt;strong&gt;Summary&lt;/strong&gt; Installing a package that has &lt;em&gt;just&lt;/em&gt; been released to CRAN is painful for many users on Mac and Windows because often the difference between a ‘binary’ and a ‘source’ version is not immediately clear and they end up trying to install the source version, which leads to errors and heartbreak.When I was designing The Carpentries Workbench, I needed to make sure that people could reliably install R packages at &lt;em&gt;any&lt;/em&gt;
Complementary label learning based on knowledge distillation

Complementary label learning (CLL) is a type of weakly supervised learning method that utilizes the category of samples that do not belong to a certain class to learn their true category. However, current CLL methods mainly rely on rewriting classification losses without fully leveraging the supervisory information in complementary labels. Therefore, enhancing the supervised information in complementary labels is a promising approach to improve the performance of CLL. In this paper, we propose a novel framework called Complementary Label Enhancement based on Knowledge Distillation (KDCL) to address the lack of attention given to complementary labels. KDCL consists of two deep neural networks: a teacher model and a student model. The teacher model focuses on softening complementary labels to enrich the supervision information in them, while the student model learns from the complementary labels that have been softened by the teacher model. Both the teacher and student models are trained on the dataset that contains only complementary labels. To evaluate the effectiveness of KDCL, we conducted experiments on four datasets, namely MNIST, F-MNIST, K-MNIST and CIFAR-10, using two sets of teacher-student models (Lenet-5+MLP and DenseNet-121+ResNet-18) and three CLL algorithms (PC, FWD and SCL-NL). Our experimental results demonstrate that models optimized by KDCL outperform those trained only with complementary labels in terms of accuracy.
A Modified Siamese Network for Facial Assimilation

We have proposed a face recognition model that uses modified Siamese Networks to give us a distance value that indicates whether 2 images are the same or different. We have used a pre-trained Xception CNN model based on InceptionV3 for Encoder. The Siamese networks take 3 input images (anchor, positive and negative), and Encoder encodes images to their feature vectors. The main objective of this research is to propose a model for face recognition with high accuracy and low classification process time, that is why we have implemented the model using a custom training and testing loop and loss function to be able to compute the triplet loss using three embeddings produced by Siamese Network. The model is trained using batches of triplets, and testing is performed using test triplets. The performance of the proposed model shows high accuracy. Also, the custom loop lowers the computational time during training and testing.
Role of Shoulder Surfing in Cyber Security (Experimental Study to the Comparative Framework)

Shoulder surfing attacks pose a significant threat to the security of sensitive information, such as passwords, social security numbers, and credit card details. In these attacks, malicious individuals strategically position themselves to observe a victim's screen and keyboard inputs covertly. As the security landscape evolves, researchers are actively exploring alternative authentication methods to replace traditional textual passwords. However, evaluating the resilience of these authentication systems against shoulder surfing attacks has been a complex task. This research aims to provide a comprehensive framework for objectively assessing the vulnerability of authentication mechanisms to shoulder surfing attacks. Through a systematic analysis, our study reveals intriguing insights. Notably, it demonstrates that pictorial passwords are more susceptible to shoulder surfing than their textual counterparts. This susceptibility arises from the ease with which attackers can visually capture and recall graphical representations. However, our research also highlights the potential for designing graphical authentication schemes that can resist shoulder surfing attempts effectively. While visual passwords exhibit inherent vulnerability due to their visibility, creative design choices can mitigate these risks. Furthermore, we found that textual passwords, while less susceptible to shoulder surfing, face limitations due to their smaller character pool size. In conclusion, this study sheds light on the nuanced landscape of authentication mechanisms and their susceptibility to shoulder surfing attacks. By providing a robust set of measures for objective analysis, our research serves as a valuable resource for developing and implementing secure authentication solutions. It emphasizes the importance of considering both usability and security factors when designing authentication systems to combat the persistent challenge of shoulder surfing attacks.
“How do you do, fellow kids?”: staying relevant with college students on your academic library’s social media

AbstractKeeping up with the cultural references, slang, and habits of college students can feel like a daunting task, but doing so is vital for any library to reach and engage with this audience. When done well in academic libraries, this practice humanizes the library, mitigates library anxiety, increases buzz about the library, and illustrates the library as a welcoming space for all. Most important, actively working to remain relevant with the current and upcoming generations of students helps libraries meet their students where they are, supporting them holistically to ensure their success in and beyond college.Keywords: Social medialibrary marketingengagementmarketingmarketing strategies Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsLewey TannerTanner Lewey is the Outreach and Student Engagement Librarian for the University Library at California State University, Stanislaus (a.k.a. Stanislaus State). In this role, Tanner plans and promotes library events and programming and oversees all communication and marketing efforts for the University Library, including being the solo manager of the library’s social media accounts. She previously held the position of Outreach and Evening Librarian at McDaniel College’s Hoover Library. Tanner earned an MSLIS from the University of Illinois at Urbana–Champaign and a BS in English and history from Northwest Missouri State University.
Results of Micro Biopsy (Tru-Cut) of Breast Cancer in Guinea

<i>Objective</i>: The aim was to determine the contribution of Micro-biopsy to the diagnosis of patients with breast cancer. <i>Material and methods</i>: This was a 2-year retrospective observational study from January 1, 2019, to December 31, 2020. It concerned patients with breast pathologies who underwent Micro-biopsy. <i>Results</i>: We enrolled 360 patients with breast pathologies, 47 of whom underwent Micro-biopsy. The mean age of the patients was 43.0 years, with a predominance of females in 45 (95.7%) cases. The primary tumor was classified as T4 in 24 (51.2%) cases. Micro-biopsy yielded an average of 4 cores (4 and 10). Immunohistochemistry was performed abroad (cerba laboratory) in 40 (85.1%) patients. Hormone receptors were positive in 09 patients, including 6 (2.6%) with positive estrogen receptors and 3 (1.3%) with positive progesterone receptors. Her2-positive breast cancers accounted for 3 (1.3%) cases. The Ki67 proliferation index was ≥ 15% in 24 (40.4%) cases. According to the nature of the fine-needle biopsy specimen result, we notified 39 (83.0%) cases of cancer and 7 (15.0%) cases of benign tumor. <i>Conclusion</i>: This preliminary study enabled us to assess the contribution and reliability of Micro-biopsy in the diagnostic arsenal of breast cancer. Micro-biopsy should be performed for all breast nodules. Screening and awareness campaigns will make a considerable contribution to reducing the delay in diagnosis and the morbidity and mortality associated with breast cancer.
The Effect of Using Anagram Games of Students’ English Vocabulary Mastery

The objective of this research was to find out the effect of using anagram game of students’ english vocabulary mastery at the eighth-grades of SMP Negeri 2 Deli Tua Academic Year 2021/2022. The type of research was quantitative research, and it was conducted by using experimental design with two classes, in experimental class consisted of 30 students and control class consisted of 30 students. In technique of collecting data, the researcher conducted pre-test, treatment and post-test by vocabulary test. The data were analyzed by using t-test formula. The result of the data showed that tobserve (11,2) value was higher than the ttable in which tobserve&gt;ttable (11,2&gt;1,68). The finding showed that the hypothesis was accepted. It can be concluded that there was significant difference score in vocabulary skill for the students who were taught by anagram media and who were not taught by anagram media. Based on the explanation above, the researcher concluded that Anagram Media is effective for teaching students’ understanding in learning vocabulary skill of the eighth-grade students at SMP Negeri 2 Deli Tua Academic Year 2021/2022
Exploring Students’ Difficulties in Writing English Focused on Voabulary

This study aims to determine the difficulties faced by students in understanding English texts, the factors causing the students to have difficulty in understanding English texts and then formulate the most dominant factors influencing the students in understanding English texts. This study used descriptive qualitative methods and the participants of the research was grade 10 students of MAS Al Washliyah 22 Tembung consisting of 4 students. The data were collected through interview, and they were analyzed by performing data reduction, presenting data and drawing conclusions. The results of the study found that students have several difficulties in understanding English texts because they are lack of interes,, lack of concentration and focus, and lack of mastery of strategies and methods of understanding English texts. Finally, it was concluded that the dominant cause of students' difficulty in understanding English texts was the lack of vocabulary mastery. In addition, it was found that some students had difficulty understanding English texts due to not having the right reading strategies, lack of vocabulary mastery, lack of grammar understanding, and they are not having regular reading habit.
EPISTEMIK CALON GURU SD DALAM MEMPERSIAPKAN PEMBELAJARAN KETERAMPILAN MEMBACA PEMAHAMAN

A prospective elementary school teacher needs to know the epistemic beliefs in him in order to prepare for reading comprehension learning well. The purpose of this study is to reveal a map of previous research on epistemic elementary school teachers in preparing for reading comprehension learning, so that further research opportunities can be carried out. In addition, this study also aims to determine the epistemic role of prospective elementary school teachers in preparing to learn reading comprehension skills. This research is a systematic literature review research that has four phases. These four phases are exploration, visualization, identification, and verification. The results showed 1) research on epistemic primary school teacher candidates and reading comprehension is still not carried out in Indonesia, because the research orientation focuses on solving reading comprehension problems; 2) The teacher's epistemic perspective is the basic foundation that can be used to develop effective reading comprehension learning. Theoretically, the implication of this study is that teacher epistemics need to be known from the beginning because mature epistemic primary school teacher candidates will be able to design and use effective learning and text understanding strategies. Teachers' epistemic beliefs need to be known from the beginning even since taking teacher education.
Otsu-Based Differential Evolution Method for Image Segmentation

This paper proposes an OTSU-based differential evolution method for satellite image segmentation and compares it with four other methods as Modified Artificial Bee Colony Optimizer (MABC), Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) using the objective function proposed by Otsu for optimal multilevel thresholding. The experiments conducted and their results illustrate that our proposed DE+OTSU algorithm segmentation can effectively and precisely segment the input image, close to results obtained by the other methods. In the proposed DE+OTSU algorithm, instead of passing the fitness function variables, the entire image is passed as an input to the DE algorithm after obtaining the threshold values for the input number of levels in the OTSU’s algorithm. The image segmentation results are obtained after learning about the image instead of learning about the fitness variables. In comparison to other segmentation methods examined, the proposed DE+OTSU algorithm yields promising results with minimized computational time compared to some algorithms.
Performance Evaluation of ML-Based AWS Security Evaluation Model for Cloud Computing

As there is a great increase in demand for cloud services, the number of companies that provide cloud services have been increased in number. Cloud-based web services wholly depends on the service provides by the companies. Hence, it is very essential to choose the cloud service provider for obtaining best services. API consistently gives access to different cloud services. In this paper a quantifiable security evaluation system for various clouds is analyzed. A set of evaluation elements are present in the security evaluation model which compresses of various sectors via storage, computing, maintenance, application security, etc. The major aim of this paper is to deal with the issues related and exhibits the comparison of Amazon Work Space (AWS) and International Business Machines (IBM) cloud. Either of the cloud service platforms are compared with each other under some situations for respective instances. A machine learning-based CNN method is used for developing the proposed cloud computing. This mechanism calculates the score for each field and gives the total score as result in order to create one secured pattern. Dynamic security scanning is done for single or multiple clouds by using visual graphs and cloud users are guided for improvement of cloud operations, changing the configuration and to provide protection against vulnerabilities. This ultimately results in the enhancement of security in cloud resources.
A Multiheaded Attention-Based Model for Generating Hindi Captions

Image captioning has recently attracted attention from the research community, but most of the works done in this domain are based on the English language. Little work has been done in other languages, such as Hindi, which are spoken by a large population across India and the world. Through this work, we proposed a model that combines multiheaded attention with two long short-term memory (LSTM) as a decoder to build caption for a given image in Hindi. We conducted experiments that resulted in Bilingual Evaluation Understudy (BLEU) scores of 62.5, 45.8, 32.8, and 23.2, respectively. Our model outperformed existing approaches on BLEU3 and BLEU4 metrics, demonstrating that the presented model is able to produce better captions.
Image Captioning for Assisting the Visually Impaired

It is an arduous task for the visually impaired to comprehend their surrounding environment. Understanding the road signs while outdoors, knowing what is in their immediate vicinity or even reading and understanding notice boards might be a problem for them. This paper proposes an application mechanism that can assist the visually impaired people in better comprehending their surroundings. It is a process of generating a suitable textual description of the captured image by using the concept of neural networks and NLP. Along with an OCR module for reading text on notice boards and documents, the description that is generated is read out as an audio output to assist the blind and visually handicapped. A camera and a system with a GPU integrated are required. This makes it simpler for those who are blind or visually impaired to be aware of and knowledgeable about what is present in their immediate surroundings. The description's audio output can be produced in a variety of languages, including Telugu, Hindi, and English, so it can be customized to the demands of the local vernacular.
Robotic ARM for Effective Environmental Cleaning

Often workers find it difficult to clean the places where heavy objects are dumped. In the present situation, many dump yards are found with heavy material which humans cannot lift, in such scenarios these robotic arms can be used to clean up the place. These robotic arms are controlled by humans sitting in a central hub station manually. A manual operator in the central hub can view the place through the camera fixed to robotic arm, which helps them to control the arm. With this method, there is no need to go to each place to clean it. From the central station, we can clean up the city. UART communication protocol is used to establish communication between the central hub and robotic arms. By using this protocol wireless communication can be established, where the central hub acts as a server and the robotic arm acts as a client. These robotic arms can be further updated and used for cleaning sewage blocks and underground drainage pipes. As robotic arms can work well under extreme conditions, it helps in reducing manpower and ensures the safety gear of workers.
Meaning-Making Educational Technologies Exploring Knowledge as a World Models

The article substantiates the relevance and significant developmental potential of meaning-making technolo-gies in the study of scientific knowledge as models. The problem is labeled as the need to include students in the learning process as co-creators of their own thoughts and meanings in the studied scientific knowledge, presented in the content of education as a model of the world phenomena. To solve the problem, the study developed technologies for using different ways of presenting knowledge in lectures, identifying and disclosing contradictions in scientific knowledge, solving real-life situations, posing unexpected questions to the essence of the studied scientific knowledge. The results of the study, evaluated according to the criteria of adequacy to the development of thinking processes, showed that students in the conditions of meaning-making technolo-gies become co-participants in the cognitive process of creating scientific knowledge, deeply comprehend the essence of the studied knowledge, learn to think independently, create their own ideas, find individual mean-ings in scientific knowledge and objectively assess the impact of the process of learning cognition on their de-velopment.