Reviewerly/exHarmony huggingface dataset:
MemoryGAN: GAN Generator as Heterogeneous Memory for Compositional Image Synthesis

The Generative Adversarial Network (GAN) has recently experienced great progress in compositional image synthesis. Unfortunately, the models proposed in the literature usually require a set of pre-defined local generators and use a separate generator to model each part object. This makes the model inflexible and also limits its scalability. Inspired by humans’ structured memory system, we propose MemoryGAN to eliminate these disadvantages. MemoryGAN uses a single generator as a shared memory to hold the heterogeneous information of the parts, and it uses a recurrent neural network to model the dependency between the parts and provide the query code for the memory. The shared memory structure and the query and feedback mechanism make MemoryGAN flexible and scalable. Our experiment shows that although MemoryGAN only uses a single generator for all the parts, it achieves comparable performance with the state-of-the-art, which uses multiple generators, in terms of synthesized image quality, compositional ability and disentanglement property. We believe that our result of using the generator of the GAN as a memory model will inspire future work of both bio-friendly models and memory-augmented models.
A Study on Corpus-based Stopword Lists in Indian Language IR

We explore and evaluate the effect of different stopword lists (non-corpus-based and corpus-based) in the information retrieval (IR) tasks with different Indian languages such as Bengali, Marathi, Gujarati, Hindi, and English. The issue was investigated from three viewpoints. Is there any performance difference between non-corpus-based and corpus-based stopword removal in chosen Indian languages? Can corpus-based stopword lists improve performance in Indian languages IR? If yes, to what extent? Among the different corpus-based stopword lists, which stopword list provides the best IR performance? Does the length of a corpus-based stopword list affect the retrieval performance in Indian languages? If yes, to what extent? It was observed that a corpus-based stopword list provides better retrieval performance than a non-corpus-based stopword list in different Indian languages. Among the different corpus-based stopword lists generated and experimented with, Zipf’s law-based stopword list (idf-based one) provides the best retrieval performance in various Indian languages. The aggregation1-based stopword list provides better retrieval than the aggregation2-based list in Indian languages, but in English, the aggregation2-based stopword list performs better than the aggregation1-based list. The best performing idf-based stopword list improves MAP score by 5.43% in Bengali, 1.91% in Marathi, 5.4% in Gujarati, 1.5% in Hindi, and 2.12% in English, respectively, over their baseline counterparts. The probabilistic retrieval models (BM25 and TF-IDF) perform best in different Indian languages. A smaller length of corpus-based stopword lists performs better than a larger length of non-corpus-based stopword lists for all the Indian languages considered. The proposed schemes demonstrate that a stopword list can be heuristically generated in a language-independent statistical method and effectively used for IR tasks with performance comparable, to or even better than non-corpus-based stopword lists.
Multimodal Spatiotemporal Deep Learning Framework to Predict Response of Breast Cancer to Neoadjuvant Systemic Therapy

Current approaches to breast cancer therapy include neoadjuvant systemic therapy (NST). The efficacy of NST is measured by pathologic complete response (pCR). A patient who attains pCR has significantly enhanced disease-free survival progress. The accurate prediction of pCR in response to a given treatment regimen could increase the likelihood of achieving pCR and prevent toxicities caused by treatments that are not effective. Th early prediction of response to NST can increase the likelihood of survival and help with decisions regarding breast-conserving surgery. An automated NST prediction framework that is able to precisely predict which patient undergoing NST will achieve a pathological complete response (pCR) at an early stage of treatment is needed. Here, we propose an end-to-end efficient multimodal spatiotemporal deep learning framework (deep-NST) framework to predict the outcome of NST prior or at an early stage of treatment. The deep-NST model incorporates imaging data captured at different timestamps of NST regimens, a tumor's molecular data, and a patient's demographic data. The efficacy of the proposed work is validated on the publicly available ISPY-1 dataset, in terms of accuracy, area under the curve (AUC), and computational complexity. In addition, seven ablation experiments were carried out to evaluate the impact of each design module in the proposed work. The experimental results show that the proposed framework performs significantly better than other recent methods.
In the melting pot of web‐crawled texts: The challenges of extracting English words from Croatian corpora

Abstract The focus of this paper are English words and phrases used in Croatian which, unlike loanwords, have not undergone major adaptations at the orthographic, phonetic, or other levels apart from being influenced by the inflectional system of the recipient language. A list of English words in Croatian corpora was compiled using automatic algorithm extraction, corpus query language in Sketch Engine , and manual word list evaluation with the end goal of publishing the first comprehensive online database of English words in Croatian. The ENGRI corpus of Croatian was created by web crawling procedure and used together with the existing Croatian hrWaC 2.2 RFTagger corpus to produce a list of English words and phrases. In this paper, word list compilation issues are discussed in relation to both general issues encountered in the study of interlingual lexical types (such as false cognates, antonomasia, and polysemy) as well as Croatian‐specific language properties such as its inflectional system and diacritical marks. In conclusion, we propose that manual evaluation is an indispensable method and a necessary complement to computational linguistic tools in the creation of word lists and databases of foreign words in other languages.
WRF Parameterizations of Short-Term Solar Radiation Forecasts for Cold Fronts in Central and Eastern Europe

The solar power industry is a rapidly growing sector of renewable energy, and it is crucial that the available energy is accurately forecast. Using numerical weather prediction models, we can forecast the global horizontal irradiance on which the amount of energy produced by photovoltaic systems depends. This study presents the forecast effects for one of the most challenging weather conditions in modelling, occurring in central and eastern Europe. The dates of the synoptic situations were selected from 2021 and 2022. Simulations were carried out for 18 days with a cold front and, in order to verify the model configuration, for 2 days with a warm front, 2 days with an occlusion front and 2 days with a high pressure situation. Overall, 24 forecasts were made for each of the three parameterizations of the Weather Research and Forecasting model. The data were compared with the values measured in situ at the station performing the actinometric measurements belonging to Germany’s National Meteorological Service. This paper presents the spatial distribution of the global horizontal irradiance parameters for several terms to explain the differences between the results of the different simulations.
Integrating Artificial Intelligence in English Literature: Exploring Applications, Implications, and Ethical Considerations

This paper explores the integration of Artificial Intelligence (AI) in English literature. AI is a rapidly growing field that has significant implications for the literary world. The paper analyzes the ways in which AI has been used in the creation, analysis, and interpretation of literature. The study also examines the ethical implications of the use of AI in literature and its impact on the role of the author and reader. The research concludes that AI has the potential to revolutionize the literary world and enhance our understanding of literature. The integration of artificial intelligence (AI) into various fields has been rapidly increasing in recent years. One such field is English literature, where AI is being utilized to analyze and even create literary works. This paper discusses the impact of AI on English literature, exploring its various applications and analyzing the advantages and disadvantages of such integration. It also highlights the ethical concerns associated with AI-generated literature and the need for a balance between human creativity and technological advancements. This article analyzes the multifaceted role of AI in English literature, including its use in literary creation, analysis, interpretation, and even reader engagement. Additionally, it examines the ethical concerns associated with AI-generated literature and emphasizes the need for a balanced approach that preserves human creativity while embracing technological advancements. By exploring these aspects, this research article contributes to a deeper understanding of the potential transformative effects of AI on English literature
PROFESSIONAL MENTALITY AS A COMPONENT OF HUMAN DEVELOPMENT IN THE DIGITAL ECONOMY

The introduction of digital technologies into the activities of modern organizations has an unprecedented impact on the sphere of work. New requirements are being imposed on the competencies and motivation of employees, as well as the development of human potential. In this connection, the research of professional mentality is becoming relevant in the digital economy. The article presents the results of a desk study of the essential understanding of professional mentality in the scientific literature. The method of bibliographic analysis was used as a research method. The article presents the results of a desk research of the essential understanding of professional mentality in the scientific literature. The method of bibliographic analysis was used as a research method. The results of the research showed that the topic of professional mentality development has been the subject of scientific discussions in various scientific fields and disciplines for several decades. However, aspects of the impact of digitalization of labor on the professional mentality are not given enough attention. In addition, the widespread introduction of digital technologies leads to the transformation of the mentality of representatives of various professions, which makes it possible to introduce the concept of "digital professional mentality" into scientific circulation. The results of the research have theoretical significance and necessitate further in-depth study of the impact of digitalization on the formation of various components of professional mentality.
Dynamic weighted ensemble learning for sequential recommendation systems: The AIRE model

The traditional ensemble-based recommendation systems rely on user meta-data to determine the weight distribution of base recommenders in generating recommendations. However, this approach may not be feasible in real-world scenarios where user information is limited, such as in ad-hoc searching. To address this challenge, we propose AIRE, a user-agnostic ensemble model that learns the representation of base recommenders from the history of interactions and memorizes the patterns and characteristics within these representations. Our proposed model takes the advantage of few-shot learning and learns the base recommender representation through a prototype network. The results of experiments demonstrate the effectiveness of AIRE in outperforming single state-of-the-art session-based recommenders. AIRE also proves to be able to distinguish input sequences and base recommenders effectively. The superior performance of AIRE highlights the importance of building representations for base recommenders in ensemble-based recommendation systems for ad-hoc inquiries. This work sheds light on the potential of ensemble learning for improving session-based recommendations and opens up new avenues for future research in this field.
EVALUATION OF THE PERSONNEL MOTIVATION SYSTEM OF A CONSTRUCTION ORGANIZATION IN THE CONTEXT OF THE THEORY OF GENERATIONS

Modern conditions for the functioning of organizations require constant updating of approaches to personnel management, including new approaches are being formed in relation to the management of personnel motivation. Despite the unification of job duties by professional affiliation, each employee is individual and his needs, principles, beliefs are individual, and this should be considered when working with personnel. When forming a system of motivation and incentives, one should consider the specifics of each working generation in the organization. Theories of generations substantiate the important characterizing qualities of generations, their features, and the influence of historical and socio-economic factors during the formation of each generation separately. The authors, when assessing the existing system of motivation of the personnel of a construction organization, took the theory of generations as a criterion basis. Improving the system of motivation and incentives in the context of the theory of generations makes it possible for the organization to contribute to the growth of the efficiency of the work of personnel, reduce turnover and reduce the costs associated with training and adaptation of personnel. The results of the work can be used as the basis for improving the systems of motivation of the personnel of construction organizations. It is possible to use the results of the study in the development of motivation systems for business entities, departments for personnel management of enterprises and organizations.
Supervised ranking approach to identify infLuential websites in the darknet

Abstract The anonymity and high security of the Tor network allow it to host a significant amount of criminal activities. Some Tor domains attract more traffic than others, as they offer better products or services to their customers. Detecting the most influential domains in Tor can help detect serious criminal activities. Therefore, in this paper, we present a novel supervised ranking framework for detecting the most influential domains. Our approach represents each domain with 40 features extracted from five sources: text, named entities, HTML markup, network topology, and visual content to train the learning-to-rank (LtR) scheme to sort the domains based on user-defined criteria. We experimented on a subset of 290 manually ranked drug-related websites from Tor and obtained the following results. First, among the explored LtR schemes, the listwise approach outperforms the benchmarked methods with an NDCG of 0.93 for the top-10 ranked domains. Second, we quantitatively proved that our framework surpasses the link-based ranking techniques. Third, we observed that using the user-visible text feature can obtain comparable performance to all the features with a decrease of 0.02 at NDCG@5. The proposed framework might support law enforcement agencies in detecting the most influential domains related to possible suspicious activities.
A Study on the Reading Program in the Library of College －Focusing on G College Library Book Club ‘GAYA’

Under the task of cultivating professionals, the imbalance between vocational education and liberal arts education is deepening. In addition, compared to university libraries, the number of college libraries operating reading programs is small, and related research is lacking. Therefore, this study presents a library-centered reading program model for junior colleges and examines it through actual cases.The environment, readers, and cultural characteristics of junior colleges were examined, and a reading program that integrated reading and writing was constructed based on Mikhail Bakhtin's “dialogueism”. The reading program was designed according to the pre-, middle-, and post-reading stages, and was applied in the library of G College, after which the case studies were analyzed. The differences and significance of this study are as follows.First, a library-centered liberal arts reading program was presented at a junior college where vocational education was the main focus. Second, a reference model for the library reading program of colleges was presented. Lastly, the need for competence among college librarians tasked with educating students in reading was presented.
Research on the Zero Trust Trend Analysis and Enterprise Security Enhancement

As the COVID-19 pandemic and the development of IT technology have led to the gradual popularization of remote and telecommuting, cloud computing technology is advancing, and cyber attack techniques are becoming more sophisticated and advanced. In response to these trends, companies are increasingly moving away from traditional perimeter-based security and adopting Zero Trust to strengthen their security. Zero Trust, based on the core principle of doubting and not trusting everything, identifies all traffic and grants access permissions through a strict authentication process to enhance security. In this paper, we analyze the background of Zero Trust adoption and the adoption policies and trends of countries that are proactively promoting its implementation. Additionally, we propose necessary efforts from governments and organizations to strengthen corporate security and considerations for companies when applying Zero Trust.
Time Series Regression Modelling: Replication, Estimation and Aggregation through Maximum Entropy

In today’s world of large volumes of data, where the usual statistical estimation methods are commonly inefficient or, more often, impossible to use, aggregation methodologies have emerged as a solution for statistical inference. This work proposes a novel procedure for time series regression modelling, in which maximum entropy and information theory play central roles in the replication of time series, estimation of parameters, and aggregation of estimates. The preliminary results reveal that this three-stage maximum entropy approach is a promising procedure for time series regression modelling in big data contexts.
A Typology Study of Pre-service Teachers&amp;apos; Perceptions of the Arts Writing Courses as Part of the Liberal Arts Education in University －Focusing on ‘Creative Writing’ of K University’

This study used the Q-methodology to categorize and examine the characteristics of pre-service teachers' perceptions of the writing course called ‘Creative Writing,’ a liberal arts writing course at K University, in order to identify the types of class perceptions of students in the Korean Language Education Department and the English Language Education Department.Based on the results of previous studies, a Q-sample was constructed and reviewed by 33 experts. The sample involved in-depth interviews with students, as well as questionnaires related to university liberal arts writing courses. 28 students of ‘Creative Writing’ were selected as a P-sample and analyzed using PQ method 2.35, and the results of the study were interpreted based on strong positive, strong negative, and identification questions in order to derive five types of perceptions related to creative writing. The fourth type of writer is the “discovery writer,” who has discovered the joy of writing itself. The fifth category is “collaborative writing strategy discovery,” where students are intrigued by the collaborative process and discover different strategies for good writing.It is hoped that these findings will serve as a foundation for the development and improvement of college liberal arts writing courses and as a way to increase the awareness of pre-service teachers when it comes to writing instruction.
Stakeholder Collaboration in Flood Disaster Management in Labansari Village

This study aims to describe stakeholder collaboration in flood disaster management in Labansari Village. The research method is a qualitative method, with data collection techniques, interview, observation, and documentation. Labansari Village is a village located in East Cikarang District, Bekasi Regency. Geographically, Labansari Village is a meeting point for 2 rivers, the Citarum River and Cibeet River. So that at the meeting point of the two rivers there is an increase in water level which makes the river overflow. This resulted in Labansari Village being flood, even though there was no rain in the village. This event often occurs every year. Therefore, people are used to floods. However, the frequent occurrence of floods does not increase public awareness. On this basis, there is a need for collaboration stakeholder, from the community, the government, to companies in efforts to deal with flood disaster. Labansari village community has become aware of flood disaster management through this collaboration. Then formed Tim Kerja Masyarakat Siaga Bencana (TKMSB). TKSMB has 6 functions, Tim Reaksi Cepat (TRC) &amp; Informasi, Shelter, Logistik/Dapur Umum, Evakuasi, Medis, Keamanan. The existence of this collaboration has produced real results with the existence of an early warning system, vlllage regulations, and the role of TKMSB. Flood disasters that occur repeatedly can be minimized by collaboration stakeholders.
Few-shot temporal knowledge graph completion based on meta-optimization

Abstract Knowledge Graphs (KGs) have become an increasingly important part of artificial intelligence, and KGs have been widely used in artificial intelligence fields such as intelligent answering questions and personalized recommendation. Previous knowledge graph completion methods require a large number of samples for each relation. But in fact, in KGs, many relationships are long-tail relationships, and the existing researches on few-shot completion mainly focus on static knowledge graphs. In this paper, we consider few-shot completion in Temporal Knowledge Graphs (TKGs) where the event may only hold for a specific timestamp, and propose a model abbreviated as FTMO based on meta-optimization. In this model, we combine the time-based relational-aware heterogeneous neighbor encoder, the cyclic automatic aggregation network, and the matching network to complete the few-shot temporal knowledge graph. We compare our model with the baseline models, and the experimental results demostrate the performance advantages of our model.