Reviewerly/exHarmony huggingface dataset:
IMPLEMENTASI STRATEGI BAURAN PEMASARAN DALAM MEMASARKAN PRODUK APLIKASI AKUNTANSI BERBASIS DIGITAL EDUKASI: FINANCIAL REPORT ASSISTANT (FIRA)

The accounting system is a field that handles the planning and application of procedures for collecting and reporting financial data—planning a system with elements of checking and matching (checks &amp; balances) to maintain company assets and have an efficient and beneficial information flow for management. The process of making financial statements seems complicated, especially there are still some small businesses that do not understand how to make their financial statements as an opportunity for accounting application providers to help small entrepreneurs by presenting easy and real-time accounting applications, as well as with the support of up-to-date technology. Financial Report Assistant (FIRA) is here to answer the problems of small entrepreneurs as an accounting application solution with service features that integrate all the company's accounting administrative needs in terms of financial records, calculation of income tax liabilities, budgeting, per-project financial statements to financial health analysis whose superior features can be selected and adjusted to consumer needs. With the Ask The Expert feature, customers don't have to worry about the FIRA accounting application not functioning optimally, because consumers will be accompanied by a professional, friendly and ready to help facilitate consumer accounting administration work that can provide insights so that consumer businesses are more profitable, growth and sustained. The purpose of this study is to determine marketing strategies to make the right marketing mix strategy decisions in accordance with company goals. The method used in this study uses qualitative methods with SWOT analysis to find out strengths, weaknesses, opportunities, and threats. After conducting a SWOT analysis can provide an overview of the process needed to develop a strategy for implementing the marketing mix in marketing a product.
Peningkatan Keterampilan Membaca dengan Menggunakan Audiovisual Siswa Kelas VI SDN Sindangsari III Kabupaten Tangerang

This research was motivated by low reading skills in class VI at SDN Sindang Sari III, Tangerang Regency. The formulation of the problem is as follows, can the use of audio-visual media improve the reading skills of students at SDN 04 Kemiri. The purpose of this research is to improve reading skills by using audio-visual media. The type of research used is Classroom Action Research (CAR). The research location was in class VI of SDN Sindang Sari III, Tangerang Regency. The research subjects were 22 class II students. Data collection techniques use test techniques and non-test techniques. The research instrument is a test sheet and non-test sheet. The results of research on reading skills using audio-visual media in class VI of SDN Sindang Sari III, Tangerang Regency.
ADAPTATION OF LAMBDAMART MODEL TO SEMI-SUPERVISED LEARNING

The problem of information searching is very common in the age of the internet and Big Data. Usually, there are huge collections of documents and only multiple percent of them are relevant. In this setup brute-force methods are useless. Search engines help to solve this problem optimally. Most engines are based on learning to rank methods, i.e. first of all algorithm produce scores for documents based on they feature and after that sorts them according to the score in an appropriate order. There are a lot of algorithms in this area, but one of the most fastest and a robust algorithm for ranking is LambdaMART. This algorithm is based on boosting and developed only for supervised learning, where each document in the collection has a rank estimated by an expert. But usually, in this area, collections contain tons of documents and their annotation requires a lot of resources like time, money, experts, etc. In this case, semi-supervised learning is a powerful approach. Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Unlabeled data, when used in combination with a small quantity of labeled data, can produce significant improvement in learning accuracy. This paper is dedicated to the adaptation of LambdaMART to semi-supervised learning. The author proposes to add different weights for labeled and unlabeled data during the training procedure to achieve higher robustness and accuracy. The proposed algorithm was implemented using Python programming language and LightGBM framework that already has supervised the implementation of LambdaMART. For testing purposes, multiple datasets were used. One synthetic 2D dataset for a visual explanation of results and two real-world datasets MSLR-WEB10K by Microsoft and Yahoo LTRC.
A Multisession SLAM Approach for RatSLAM

Abstract To successfully perform autonomous navigation, mobile agents must solve the Simultaneous Localization and Mapping (SLAM) problem. However, acquiring the map in a single SLAM session may not be possible, thus the map may be incrementally built over multiple sessions. Two solutions could be considered to solve the multisession SLAM problem: (i) the robot must localize itself in the previously stored map before the new session starts; (ii) it can start a new map and merge it with the map from the previous sessions. To date, only scenario (i) has been addressed by RatSLAM, an algorithm inspired by the navigation system in rodent brains. Therefore, this work proposes a multisession solution that solves both scenarios. A new mechanism merges the data from the RatSLAM structures of the current mapping session with those previously stored if there are connections between these paths. This approach was tested in four different scenarios, from virtual controlled environments to real-world environments with two, three, and five sessions. The robot started in an unfamiliar location for each mapping session, but it also works if the agent starts in a known place, scenario (ii) and (i), respectively. For all experiments, the entire map was consistently obtained. Furthermore, the proposed approach updates and enhances the previous session’s map in real-world environments. Therefore, the proposed approach may be a multiple SLAM session solution for the RatSLAM algorithm.
Студенты технического университета о роли иностранного языка и проблемах при его изучении

On the base of a sociological survey (questionnaire method) of students of the Perm National Research Polytechnic University (PNRPU), the motivation and demand for learning a foreign language for the future engineering profession is ascertained. The paper shows the difficulties that students face in the process of studying and their satisfaction with the knowledge received at school. The authors consider obstacles of an organizational nature in the classroom and clarify the most effective methods of educational activity in the process of teaching this compulsory course.
Limit theorems for the Markov random walks describes by the generalization of autoregressive process of order one

In this paper are proved the strong law of large numbers and the central limit theorem for the Markov random walks describes by the generalization autoregressive process of order one.
Developing the Security for Cloud Information Via Alexnet Learning Model versus the accuracy of Artificial Neural Network

The main objective of the study is to protect cloud from different types of attacks by using AlexNet classifier compared accuracy with Artificial Neural Network and user’s data to be stored in the cloud safely by Advanced Encryption Standard. Materials and Methods: This research examines two groups AlexNet withArtificial Neural Network. Statistical study used 1300 training and 403 testing datasets from UNSW-NB15 dataset. ClinCalc programme utilised N=10, 0.05 is alpha value, 0.8% is G-Power, and 95% confidence interval.Result and Discussion: Novel Alexnet (91.081%) has an increased precision over ANN (90.075%) with the P value is 0.012(p&lt;0.05) from the results of Independent samples t-test. There is a statistical significant difference between these two algorithms.Conclusion: This study concludes that the Novel AlexNet classifier algorithm seems fundamentally better than the ANN in terms of increasing the accuracy of secure cloud data and holding sensitive data with the dataset of UNSW-NB15.
Relation-Aware Multi-Positive Contrastive Knowledge Graph Completion with Embedding Dimension Scaling

Recently, a large amount of work has emerged for knowledge graph completion (KGC), which aims to reason over known facts and to infer the missing links. Meanwhile, contrastive learning has been applied to the KGC tasks, which can improve the representation quality of entities and relations. However, existing KGC approaches tend to improve their performance with high-dimensional embeddings and complex models, which make them suffer from large storage space and high training costs. Furthermore, contrastive loss with single positive sample learns little structural and semantic information in knowledge graphs due to the complex relation types. To address these challenges, we propose a novel knowledge graph completion model named ConKGC with the embedding dimension scaling and a relation-aware multi-positive contrastive loss. In order to achieve both space consumption reduction and model performance improvement, a new scoring function is proposed to map the raw low-dimensional embeddings of entities and relations to high-dimensional embedding space, and predict low-dimensional tail entities with latent semantic information of high-dimensional embeddings. In addition, ConKGC designs a multiple weak positive samples based contrastive loss under different relation types to maintain two important training targets, Alignment and Uniformity. This loss function and few parameters of the model ensure that ConKGC performs best and has fast convergence speed. Extensive experiments on three standard datasets confirm the effectiveness of our innovations, and the performance of ConKGC is significantly improved compared to the state-of-the-art methods.
Trace-based cryptanalysis of cyclotomic $R_{q,0}\times R_q$-PLWE for the non-split case

We describe a decisional attack against a version of the PLWE problem in which the samples are taken from a certain proper subring of large dimension of the cyclotomic ring $\mathbb{F}_q[x]/(\Phi_{p^k}(x))$ with $k&gt;1$ in the case where $q\equiv 1\pmod{p}$ but $\Phi_{p^k}(x)$ is not totally split over $\mathbb{F}_q$. Our attack uses the fact that the roots of $\Phi_{p^k}(x)$ over suitable extensions of $\mathbb{F}_q$ have zero-trace and has overwhelming success probability as a function of the number of input samples. An implementation in Maple and some examples of our attack are also provided.
Defining and Measuring Cost, Effort, and Load in Information Retrieval

The demands imposed on the user during the Information Retrieval (IR) process are well-recognised, with numerous studies highlighting the influence and importance of cost, effort, and load in explaining user search behaviour and experience. Despite this understanding, there exists no universal definitions of these constructs or standardised methods of measuring them within the field of IR. As a result, an open research problem has emerged in relation to how these constructs have been defined, interpreted, and subsequently measured. To address this research problem, the aim of this research is two-fold. Firstly, to establish working definitions and a conceptual framework for defining cost, effort, and load; and secondly, to examine and evaluate existing measures of effort and load within an IR context.
Investigating English Vocabulary Difficulties and Its Learning Strategies of Lower Secondary School Students

Vocabulary plays a crucial role in language proficiency as it serves as the foundation for effective language acquisition. The ability to acquire vocabulary is considered a prerequisite for learning a language. Therefore, the primary objective of this research is to investigate the difficulties encountered by students in their vocabulary learning process as well as the strategies they employ to overcome these obstacles in the context of English vocabulary acquisition. The participants in this study consisted of nine students at various proficiency levels in a lower secondary school, along with one English teacher. Employing a descriptive qualitative method, semi-structured interviews were conducted to gather data for this research. The interview questions focused on investigating the difficulties students faced in learning English vocabulary and the strategies they utilized to address these challenges. To facilitate data analysis, each interview was meticulously recorded and transcribed. The data analysis technique employed in this research entailed descriptive analysis. The findings of this study revealed that a significant number of students encountered difficulties in acquiring vocabulary. Specifically, these challenges manifested in areas such as the correct pronunciation of new words, accurate spelling, proper usage of word meanings, and effective retention or memorization of vocabulary. In order to mitigate these difficulties, students employed various strategies, including utilizing media resources such as smartphones, the internet, and games as well as employing note-taking techniques and consulting dictionaries to aid their vocabulary learning process.
PENGEMBANGAN E-MODUL RESEARCH BASED LEARNING MENGGUNAKAN LECTORA INSPIRE APPLICATION PADA PEMBELAJARAN IPAS UNTUK MENDUKUNG MENDEKA BELAJAR SISWA SEKOLAH DASAR

The research is that students have difficulty understanding the subject matter because of the new curriculum and during the learning process the teacher only uses books as a learning resource so that student learning motivation in science learning does not look good because there are no teaching materials other than books so students feel bored because learning is not interesting and fun. The purpose of this study is to find out how the accuracy of the content, the implementation of the use and how the understanding of science learning after using the E-module. This research is research and development (R&amp;D). The model used, namely the plopm model, this study consists of three stages. The research phase. Introduction (Preliminary research), the prototype stage (development or prototyping) and the assessment phase (assessment phase). The results of the validation assessment by the three validators obtained an average score of 89.9% with a very valid category. the results of the practicality assessment of the e-module conducted by teachers and fourth grade elementary school students obtained an average score of 90.7% in the very practical category. The effectiveness results carried out by all fourth grade elementary school students obtained a percentage of 80% in the effective category. So, the conclusion of this study is that the development of e-modules based on research based learning using the Lectora Inspire Application has valid, practical and effective criteria.
Knowledge-based checkpointing strategy for spot instances in cloud computing

The Amazon EC2 offers spot-priced virtual machines (VMs) at a reduced price compared to on-demand and reserved VMs. However, Amazon EC2 can terminate these VMs anytime due to the spot price and demand fluctuation. Using spot VMs results in a longer execution time and disrupts service availability. Users can use fault-tolerant techniques such as checkpointing, migration, and job duplication to mitigate the unreliability of spot VMs. In this paper, a knowledge-based checkpointing strategy is proposed to minimize the overall checkpointing overhead during the execution of jobs. The proposed scheme uses real-time price history to decide when to take a checkpoint. Results show that the proposed approach can significantly reduce the turnaround time by 18% compared to Hourly Checkpointing Strategy and 9% compared to Rising-Edge Checkpointing Strategy. One can also achieve 54% to 78% reliability with a cost saving of 78% for the workload used with the described approach.
Empirical investigation of factors influencing function as a service performance in different cloud/edge system setups

Experimental data can aid in gaining insights about a system operation, as well as determining critical aspects of a modelling or simulation process. In this paper, we analyze the data acquired from an extensive experimentation process in a serverless Function as a Service system (based on the open source Apache Openwhisk) that has been deployed across 3 available cloud/edge locations with different system setups. Thus, they can be used to model distribution of functions through multi-location aware scheduling mechanisms. The experiments include different traffic arrival rates, different setups for the FaaS system, as well as different configurations for the hardware and platform used. We analyse the acquired data for the three FaaS system setups and discuss their differences presenting interesting conclusions with relation to transient effects of the system, such as the effect on wait and execution time. We also demonstrate interesting trade-offs with relation to system setup and indicate a number of factors that can affect system performance and should be taken under consideration in modelling attempts of such systems.
TRAINING OF HUMAN RESOURCES IN AN EDUCATIONAL INSTITUTION. PROPOSAL FOR THE TRAINING OF VIRTUAL TEACHERS

The virtual teacher in an educational institution is the main human resource that interacts with students; and training it is essential not only so that it performs its functions well from the first assigned course; but also to improve procedures; With this, it is sought that initially the induction to the position is generated, which will allow the teacher to know what is expected of him during the school year and the training that will allow him to master the tools of the virtual classroom and perfect his way of working.The foregoing has an impact on providing a quality service that guarantees a uniform way of working, with evaluation processes for teaching staff and, above all, that guarantees that the academic training of the students complies with the standards and achieves that its graduates are competitive at a national and international level.It is concluded that for the knowledge of teachers to materialize in quality training for students, it is necessary to know the institutional regulations, the way of working of the educational institution, the virtual platform and what will be evaluated to the teacher and for this It is essential that educational institutions have an induction and training program for their teachers.
Content-immersive Subjective Quality Assessment in Long Duration 360-degree Videos

This paper presents a comparison between three content-immersive methodologies to evaluate video quality: Absolute Category Rating (ACR) with unrepeated scenes, Single Stimulus Continuous Quality Evaluation (SSCQE), and Single Stimulus Discrete Quality Evaluation (SSDQE). Three tests were conducted with different participants assigned to each methodology to assess 360-degree video quality in an immersive communications context. Additionally, socioemotional aspects such as presence, attitude, or attention were measured for each condition. All three methodologies offer similar results in terms of video quality evaluation, and they all keep similar levels of social and spatial presence. Both SSCQE and SSDQE allow narrative and context while evaluating video quality, but SSDQE has less impact in participants' attention to the video content. These results suggest that it is possible to extend ITU-T P.919 with the support of long sequences with in-sequence SSDQE video quality evaluation, without significant impact on the socioemotional properties of the experience. This would result on a standard content-immersive method, methodology suitable for 360-degree video content, favoring the ecological validity of the subjective assessment tests.
The Transmission Rating Scale and its Relation to Subjective Scores

Parametric planning models provide an estimation of the perceived Quality of Experience (QoE) for a certain set of values of relevant network Quality of Service (QoS) conditions. Many of those derive from the E-model, ITU-T standard for network planning in voice transmission applications. The E-model maps the values of different network conditions into a common Transmission Rating scale, which is later used to estimate user quality scores. However, the E-model does not provide a method to obtain direct human judgments on such scale, which has also found not applicable to other applications, such as web browsing or video. In this paper, we propose a way to use the Transmission Rating for general use cases, including video quality. Be re-doing the derivation process which led to the E-model scale from the original Bellcore model, it is possible to apply it to any use case with an underlying normal distribution of subjective scores. We validate this approach by analyzing two video quality experiments which mix different judgement conditions (in particular, screen sizes and resolutions). Finally, we provide general recommendations to develop Transmission Rating scales in future parametric models.
DIAGNOSTIC ACCURACY OF MAMMOGRAPHY IN PATIENTS WITH PALPABLE BREAST LUMP BY USING BIRADS SCORE

Background and objectives Various radiographic modalities are readily available to identify lesions that are suspicious for breast cancer. Mammography remains the mainstay of breast cancer screening. This study was aimed to determine the diagnostic accuracy of mammography in evaluating breast lumps. Methodology The present one and half year cross-sectional study was conducted from March 2021 to September 2022 on a total of 58 eligible women presenting with palpable breast lump in the Department of General Surgery, MYSORE MEDICAL COLLEGE AND RESEARCH INSTITUTE, MYSURU. In the present study commonest age group was betw Results een 46 to 60 years with 43.37% of the patients the nipple discharge was present in 12.06% of the cases. The shape of the lesion was found to be globular in 37.93% and tenderness was present in 43.10% of the patients. The nipple examination revealed retracted nipples in 15.51% of the patients. The consistency of the breast lump was rm in 55.17% of the patients. Axillary lymph node was present in 24.13% of the women. Mammography ndings revealed grade 5 breast lump in 27.58% of the patients. Malignant lesions were diagnosed in 60.41% of the patients while 39.65% of the women were diagnosed as having benign lesions. Conclusion and interpretation: Of the 35 patients who had malignant lesions on histopathology/cytology, 32 had malignant lesions on mammography and 3 women had benign lesions. The sensitivity of mammography compared to histopathology/cytology in predicting malignant lesions was 91.42% with specicity of 91.30% and the diagnostic accuracy was 91.37%.
AffectiveTree: Visualizing Collective Stress Amongst Chinese Telecommuters through Dynamic Painting

Under the epidemic situation of Covid-19, many companies are promoting telecommuting to protect employees' health. At the same time, telecommuting created an additional communication barrier for people to communicate with their colleagues or express their feelings. Stress can lead to health problems and burnout. In this paper, we propose AffectiveTree, a dynamic painting that can visualize collective stress among Chinese telecommuters in an abstract way. According to the stress level and duration of the Chinese telecommuters, the shape and color of the trees in the painting will be morphed. We used participatory design to involve the user in the design of the AffectiveTree and to verify its effectiveness and usability. The result showed that this dynamic painting could visualize the stress level of Chinese telecommuters through dynamic abstract art, and it can relieve their stress.