Reviewerly/exHarmony huggingface dataset:
<i>iFUNDit</i>: Visual Profiling of Fund Investment Styles

Abstract Mutual funds are becoming increasingly popular with the emergence of Internet finance. Clear profiling of a fund's investment style is crucial for fund managers to evaluate their investment strategies, and for investors to understand their investment. However, it is challenging to profile a fund's investment style as it requires a comprehensive analysis of complex multi‐dimensional temporal data. In addition, different fund managers and investors have different focuses when analysing a fund's investment style. To address the issue, we propose iFUNDit , an interactive visual analytic system for fund investment style analysis. The system decomposes a fund's critical features into performance attributes and investment style factors, and visualizes them in a set of coupled views: a fund and manager view, to delineate the distribution of funds' and managers' critical attributes on the market; a cluster view, to show the similarity of investment styles between different funds; and a detail view, to analyse the evolution of fund investment style. The system provides a holistic overview of fund data and facilitates a streamlined analysis of investment style at both the fund and the manager level. The effectiveness and usability of the system are demonstrated through domain expert interviews and case studies by using a real mutual fund dataset.
Literature Analysis on Supply Chain and Blockchain Keywords Using Text Mining: Focusing on Studies Prior to 2020

This study examines the initial research trends related to the supply chain for about ten years from right after 2008 when the concept of blockchain emerged. To this end, using text mining, English literature, including blockchain and supply chain as keywords corresponding to the 2010-2020 period, was collected from major academic databases. Finally, 2,185 papers and conference proceedings were collected, and the main topics of the literature were examined by analyzing words vectorized with information such as authors, titles, and abstracts. As a result, the ‘mainstream’ appeared as blockchain technology, such as RFID, security, and digital ledgers, and the ‘emerging stream’ appeared as blockchain applications to various fields like education, government, trading, and management. Furthermore, six research trends were derived from five clusters derived through topic modeling.
Compressing fully connected layers of deep neural networks using permuted features

Abstract Modern deep neural networks typically have some fully connected layers at the final classification stages. These stages have large memory requirements that can be expensive on resource‐constrained embedded devices and also consume significant energy just to read the parameters from external memory into the processing chip. The authors show that the weights in such layers can be modelled as permutations of a common sequence with minimal impact on recognition accuracy. This allows the storage requirements of FC layer(s) to be significantly reduced, which reflects in the reduction of total network parameters from 1.3× to 36× with a median of 4.45× on several benchmark networks. The authors compare the results with existing pruning, bitwidth reduction, and deep compression techniques and show the superior compression that can be achieved with this method. The authors also showed 7× reduction of parameters on VGG16 architecture with ImageNet dataset. The authors also showed that the proposed method can be used in the classification stage of the transfer learning networks.
Long-term didactic innovations in higher education teaching caused by the coronavirus pandemic?

During the coronavirus pandemic, many universities worldwide were closed and lecturers had to switch from face-to-face teaching to online distance education. Consequently, questions arose which routines in teaching the lecturers were able to use despite this change in teaching environments, and which didactic innovations they implemented in their courses. Lecturers’ evaluation of these innovations and any aspects they intend to continue using after the pandemic and the return to face-to-face teaching were examined. Through a qualitative survey of 24 university lecturers in Germany and Panama, the long-term effects of the pandemic on teaching were investigated. It was found that the creation of new digital media for teaching, the use of new digital tools to ensure interaction and scientific exchange, the finding of new forms of organization, and combinations of synchronous and asynchronous teaching were all considered to be positive didactic innovations that should be maintained after the pandemic.
Utilization of Reading Corners in Literacy Activities to Improve Likes to Read Character and Reading Ability of Elementary School Students

Literacy activities are carried out through habituation activities for all students in the school environment. The lack of use of reading corners makes literacy learning achievements not as expected. This study aims to explore the implementation of the reading corner program, literacy activities, reading skills, and the character of fond of reading elementary school students. This type of research is descriptive explorative with a qualitative approach. Data was collected through interviews, observation, and documentation. The results of the study show that the reading corner program is part of the Bali Danu program, which is an attempt to update the previous program. The collection of books in the reading corner is used as reading material for school literacy activities, and educators use the reading corner as a place for discussion and self-development for students, especially in reading and storytelling activities. Through regular literacy activities and reading corners, students' reading skills and liking to read can be improved. In addition to using the reading corner as a place to read books, the reading corner becomes their class identity; there is a sense of belonging to the reading corner because students are directly involved in creating and maintaining the reading corner.
Novel multi-media steganography model using meta-heuristic and deep learning assisted adaptive lifting wavelet transform

AbstractThe major goal of this proposal is to implement a new steganography model with multi-media data. Initially, in video steganography and frames are extracted and then, the keyframes with texture and motion features of gathered video are selected through Deep Convolutional Neural Networks (DCNN). Then, the optimal region selection for embedding is carried out using the Improved Artificial Gorilla Troops Optimizer (IAGTO). Secondly, image steganography is performed by collecting the images, where the color channel selection is done through DCNN. Thirdly, audio steganography is performed by gathering the input audios from the standard datasets. Further, the optimal time instant selection is performed by the same IAGTO algorithm, which will be fed to the next phase. Finally, the audio embedding is done by ALWT, and then, the stego audios are extracted by taking inverse ALWT. From the experimental analysis, successful embedding over multi-media data is achieved..KEYWORDS: Multi-media steganography modelimage steganographyvideo steganographyaudio steganographyadaptive lifting wavelet transformdeep convolutional neural networksimproved artificial gorilla troops optimizer Disclosure statementNo potential conflict of interest was reported by the author(s).
Performance Analysis of XGBoost Algorithm to Determine the Most Optimal Parameters and Features in Predicting Stock Price Movement

Purpose: The research aims to find the best parameters and features for predicting stock price movement using the XGBoost algorithm. The parameters are searched using the RMSE value, and the features are searched using the importance value.Design/methodology/approach: The research data is the stock data of Amazon.com company (AMZN). The dataset contains the Date, Low, Open, Volume, High, Close, and Adjusted Close features. The dataset is ensured to have no missing data by handling missing values. The input feature is selected using the Pearson Correlation feature selection method. To prevent the difference between the highest and lowest stock price from being too far apart, the data is scaled using the scaling method. To avoid bias that may appear in the prediction result, cross-validation is used with the Min Max Scaling method, which will devide the dataset into training data and testing data within a range of 30 days after the training data. The parameters to be tested include n_estimator = 500, early stopping round = 3, learning rate = 0.01, 0.05, 0.1, and max_depth (tree depth) = 3, 4, 5.Findings/result: The result of the research that a learning rate of 0.05 and a tree depth of 5 obtained the lowest RMSE result compared to other models, with an RMSE of 0.009437. The Low feature obtained the highest importance value among all the models built.Originality/value/state of the art: This study used testing data within a range of 30 days after the training data and used a combination of parameters, including n_estimator = 500, early stopping round = 3, learning rate = 0.01, 0.05, 0.1, amd max_depth (tree depth) = 3, 4, 5.
A Case Study on the Development of Digital Competences of Teachers at the University of Ljubljana

Abstract The renewed 2017 EU Higher Education Agenda expresses the intention to “develop and implement a digital readiness model” to assist higher education institutions, their staff, and students in implementing digital learning strategies and maximising the potential of cutting‐edge technologies such as learning analytics. The anticipated digital transformation will only be successful if higher education institutions and teachers strengthen their digital competences and skills and “become” digitally competent. Many of the incentives for these processes were prompted by the unexpected Covid‐19 crisis, which highlighted the importance of higher education teachers’ digital skills in the need to digitise the higher education environment. The Covid‐19 crisis experience and the accelerating development of digitalisation are changing both the conditions for education and education itself, which is why higher education teachers face the challenging task of lifelong development of digital competences. To complete this task, they must learn about information and communication technology (ICT)/digital technologies and how they can be integrated into the pedagogical process. The challenge for higher education teachers is to develop ICT‐based teaching. This is not about how higher education teachers (and students) master ICT, but about how to make ICT one of the tools for carrying out pedagogical activities in general. The article addresses the development of digital competences among higher education teachers as a critical issue in the renovation of higher education didactics. To that end, various digital competence models and concepts are presented. Among other things, the European Digital Competence Framework for Teachers is highlighted. The case study of activities aimed at developing digital competences of higher education teachers at the University of Ljubljana (Slovenia), and thus higher education teachers working in the field of education for public administration. Based on the analysis of activities, we discover an increased interest in the development of digital competences, which is reflected in various forms of institutional (university) support for educators (e.g. training), as well as an increased interest in the development of digital competences among higher education teachers.
Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning

Understanding the weathering intensities can provide answers for environmental issues, soil, and geoscience studies. Recently, geophysical approaches and machine learning techniques have been applied in soil science to access weathering. This study aimed to model weathering intensity using combined data from geophysical sensors, satellite images, and morphometry associated to machine learning algorithms. We also we evaluated the efficiency of nested-leave one out cross-validation applicability in a small geodata set evaluated the importance of covariates and the resulting weathering intensity map in relation to pedogeomorphological processes. Our study focused on a 184-ha area in southwest Brazil, where we conducted soil analysis at 71 sites. We applied principal component analysis and determined the ideal number of clusters to determine the classes of weathering intensity. We used six geophysical sensor parameters, including equivalent uranium, equivalent thorium, potassium 40, magnetic susceptibility, and soil apparent electrical conductivity, along with the weathering index to create the clusters. Then, four machine learning algorithms were used to infer different weathering intensities in soils formed from the same parent materials. To validate our results, we used the nested-leave-one-out-cross-validation (“nested-LOOCV”) method, which is suitable for small datasets. Our findings showed that the random forest model performed the best with three clusters as the ideal number. We also found that the geophysical data, clusterization, and machine learning algorithm contributed significantly to identifying different weathering intensities. The results indicated that weathering operated at different intensities on both the diabase/Rhodic-Nitisols and the siltite/metasiltite Rhodic and Xanthic Lixisols areas, with the highest intensities occurring in the west Xanthic Lixisols and the lowest intensities occurring in the Rhodic and Lixisols in the east area. The siltite/metamorphosed siltite and Lixisols areas presented moderate weathering rates. We found that the all-geophysical variables used were related and affected by weathering intensity, which contributed to the modeling and clusterization processes.
FBN: Weakly Supervised Thyroid Nodule Segmentation Optimized by Online Foreground and Background

The main objective of the work described here was to train a semantic segmentation model using classification data for thyroid nodule ultrasound images to reduce the pressure of obtaining pixel-level labeled data sets. Furthermore, we improved the segmentation performance of the model by mining the image information to narrow the gap between weakly supervised semantic segmentation (WSSS) and fully supervised semantic segmentation.Most WSSS methods use a class activation map (CAM) to generate segmentation results. However, the lack of supervision information makes it difficult for a CAM to highlight the object region completely. Therefore, we here propose a novel foreground and background pair (FB-Pair) representation method, which consists of high- and low-response regions highlighted by the original CAM-generated online in the original image. During training, the original CAM is revised using the CAM generated by the FB-Pair. In addition, we design a self-supervised learning pretext task based on FB-Pair, which requires the model to predict whether the pixels in FB-Pair are from the original image during training. After this task, the model will accurately distinguish between different categories of objects.Experiments on the thyroid nodule ultrasound image (TUI) data set revealed that our proposed method outperformed existing methods, with a 5.7% improvement in the mean intersection-over-union (mIoU) performance of segmentation compared with the second-best method and a reduction to 2.9% in the difference between the performance of benign and malignant nodules.Our method trains a well-performing segmentation model on ultrasound images of thyroid nodules using only classification data. In addition, we determined that CAM can take full advantage of the information in the images to highlight the target regions more accurately and thus improve the segmentation performance.
ANALISIS METAFORA KONSEPTUAL DALAM ARTIKEL POLITIK “MENATA ULANG KOALISI”

The aim of this research was to describe the concept of metaphor which associated the source domain to the target domain in a political article as well as to see the scheme of metaphor that might appear in four related topics based on the text; coalition, government, politics and party. Research object was limited to metaphor in the level of single lexical form under qualitative approach and literature methodology by analyzing the written text. Based on the analysis, it was gained that the concept of metaphor leaded to the kind of structural metaphor which applied a certain structure to the target domain as followed “Coalition” and “Government” were “Body” and “Building”; “Policy” was “Building”, “Earthquake, Nobleman, Association, and Deposit”; “Party” was “Enemy, Friend, Tactic, and Stamp”.
Folkloric elements in the repertoire of the instrumental vocal ensemble „Orizont”

The purpose of this article is to identify the methods of interaction of national folklore with the idioms of pop music and jazz in the creation of the vocal-instrumental group „Orizont”, led by Oleg Milstein. Based on the detailed analysis of the compositions „La morișca”, „Leana”, „Moldovan tunes”, etc. the author reveals the most representative methods of involving Romanian folklore in the sound texture of the pieces performed by the band „Orizont”. Among them it is worth mentioning: quoting popular songs, composing contrapuntal lines to folk melodies, combining themes of folkloric origin with stylistic and genre elements of pop, rock and jazz music. Special attention is paid to the treatment of vocal jazz methods used by the members of the „Orizont” band.
Dissipative dynamics in quantum key distribution

Abstract Using the IBM Quantum Experience platform, we simulate the dissipative dynamics in the BB84 quantum key distribution protocol. We employ the Jaynes–Cummings model to simulate the attenuation in an optical fiber during the information transmission process and calculate the quantum bit error rate (QBER). The results of QBER as a function of the distance give a satisfactory agreement with experimental data when the system is in a Markovian regime.
Improvement of Data Mining Models using Forward Selection and Backward Elimination with Cryptocurrency Datasets

Cryptocurrency is a digital currency not managed by a state or central bank, and transactions are peer-to-peer. Cryptocurrency is still considered a speculative asset and its price volatility is relatively high, but it is also expected to become an efficient and secure transaction tool in the future. The purpose of this study is to compare and improve the performance of the Data Mining Algorithm model using the Feature Selection-Wrapper with the Binance Coin (BNB) cryptocurrency dataset. The Feature Selection-Wrapper approach used is Forward Selection and Backward Elimination. The algorithms used are Neural Networks, Deep Learning, Support Vector Machines, and Linear Regression. The methodology used is Knowledge Discovery in Databases. The results showed that from a comparison using K-Fold Cross Validation with a value of K=10, the Neural Network Algorithm has the best Root Mean Square Error value of 10,734 +/- 10,124 (micro average: 14,580 +/- 0,000). Then after improving performance using Forward Selection and Backward Elimination in the Neural Network Algorithm, the best performance improvement results are shown by using Backward Elimination with RMSE 5,302 +/- 2,647 (micro average: 5,805 +/- 0,000).
PEMBUATAN APLIKASI JASA PENYEWAAN TRUCK BERBASIS ANDROID (SELVARU TRUCK)

Logistics is an important part of supply chain management and includes activities such as planning, executingand controlling the process of moving goods, storing goods, and managing transportation. In addition, the wordlogistics also refers to the distribution process which is the process of moving products or materials from oneplace to another. One of the implementation of logistics activities and distribution of goods is the activity ofsending goods via land by using trucks as a means of transporting the movement of these goods. The applicationof goods delivery activities using trucks is the availability of truck rental services that facilitate the delivery ofgoods or cargo from one place to another, which is needed by business owners and individuals who need theseservices. And in today's technological era, one of the realizations that supports the truck rental service is theexistence of an application that can make it easier for people to access and use it. The Selvaru Truck applicationis designed as a solution to the problem of shipping goods, namely as a service that provides a fleet of trucks forshipping goods based on Android applications..
The Relationship Between Word Length and Average Information Content in Japanese

Piantadosi, Tily, and Gibson analyzed a large-scale web-scraping corpus (the Google 1T dataset) and reported that word length is independently predicted from average information content (surprisal) calculated by a 2- to 4-gram model (hereafter, longer-span surprisal) across 11 Indo-European languages, namely, Czech, Dutch, English, French, German, Italian, Polish, Spanish, Portuguese, Romanian, and Swedish. However, a recent article by Meylan and Griffiths suggested the importance of preprocessing for studies with large-scale corpora and reanalyzed the same databases. After their preprocessing, the results in Piantadosi et al. were not replicated in Czech, Romanian, and Swedish. Additionally, a German-specific study by Koplenig, Kupietz, and Wolfer showed that the strict analysis did not replicate the result in Piantadosi et al. for that language with the preprocessing suggested by Meylan and Griffiths in a large-scale but less noisy database. These three studies provide evidence from 11 Indo-European languages and one Afro-Asiatic language, Hebrew, as relevant in this debate. However, we do not have evidence from other linguistic groups. This study provides evidence about Japanese based on a strict preprocessing of Google's web-scraping database. The results show that Japanese word length can be predicted independently by 2- to 4-gram surprisal.