Reviewerly/exHarmony huggingface dataset:
Multi-View Stereo Matching Method Based on Recursive Neural Networks

This article proposes a recursive layered network reconstruction method with pixel-wise attention cost aggregation to address the problems of textureless areas and poor reconstruction results at scene edges in multi-view stereo matching methods. First, multi-scale features of multiple images are extracted through downsampling and transformed into a cost volume using three-dimensional differentiable homography. Then, a pixel-wise attention aggregation module is added to the cost volume aggregation stage to reweight different pixels and generate a new cost volume. Next, a network with recursive layers is used to regularize the cost volume, replacing the traditional 3D CNN network, and an initial depth map is generated. Finally, the filtered and refined depth maps are merged to generate a three-dimensional dense point cloud. Experimental results show that the proposed network model improves completeness, accuracy, and overall quality by 0.377, 0.363, and 0.370, respectively, compared to other network models, and produces more complete point cloud reconstructions in weak texture areas and scene edges.
Syndicom: Improving Conversational Commonsense with Error-Injection and Natural Language Feedback

Commonsense reasoning is a critical aspect of human communication. Despite recent advances in conversational AI driven by large language models, commonsense reasoning remains a challenging task. In this work, we introduce Syndicom - a method for improving commonsense in dialogue response generation. Syndicom consists of two components. The first component is a dataset composed of commonsense dialogues created from a knowledge graph and synthesized into natural language. This dataset includes both valid and invalid responses to dialogue contexts, along with natural language feedback (NLF) for the invalid responses. The second contribution is a two-step procedure: training a model to predict natural language feedback (NLF) for invalid responses, and then training a response generation model conditioned on the predicted NLF, the invalid response, and the dialogue. Syndicom is scalable and does not require reinforcement learning. Empirical results on three tasks are evaluated using a broad range of metrics. Syndicom achieves a relative improvement of 53% over ChatGPT on ROUGE-1, and human evaluators prefer Syndicom over ChatGPT 57% of the time. We will publicly release the code and the full dataset.
Towards Multilingual Automatic Open-Domain Dialogue Evaluation

The main limiting factor in the development of robust multilingual open-domain dialogue evaluation metrics is the lack of multilingual data and the limited availability of open-sourced multilingual dialogue systems. In this work, we propose a workaround for this lack of data by leveraging a strong multilingual pretrained encoder-based Language Model and augmenting existing English dialogue data using Machine Translation. We empirically show that the naive approach of finetuning a pretrained multilingual encoder model with translated data is insufficient to outperform the strong baseline of finetuning a multilingual model with only source data. Instead, the best approach consists in the careful curation of translated data using MT Quality Estimation metrics, excluding low quality translations that hinder its performance.
Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns

Schema-guided dialogue state trackers can generalise to new domains without further training, yet they are sensitive to the writing style of the schemata. Augmenting the training set with human or synthetic schema paraphrases improves the model robustness to these variations but can be either costly or difficult to control. We propose to circumvent these issues by grounding the state tracking model in knowledge-seeking turns collected from the dialogue corpus as well as the schema. Including these turns in prompts during finetuning and inference leads to marked improvements in model robustness, as demonstrated by large average joint goal accuracy and schema sensitivity improvements on SGD and SGD-X.
Bipartite Graph Coarsening for Text Classification Using Graph Neural Networks

Text classification is a fundamental task in Text Mining (TM) with applications ranging from spam detection to sentiment analysis. One of the current approaches to this task is Graph Neural Network (GNN), primarily used to deal with complex and unstructured data. However, the scalability of GNNs is a significant challenge when dealing with large-scale graphs. Multilevel optimization is prominent among the methods proposed to tackle the issues that arise in such a scenario. This approach uses a hierarchical coarsening technique to reduce a graph, then applies a target algorithm to the coarsest graph and projects the output back to the original graph. Here, we propose a novel approach for text classification using GNN. We build a bipartite graph from the input corpus and then apply the coarsening technique of the multilevel optimization to generate ten contracted graphs to analyze the GNN’s performance, training time, and memory consumption as the graph is gradually reduced. Although we conducted experiments on text classification, we emphasize that the proposed method is not bound to a specific task and, thus, can be generalized to different problems modeled as bipartite graphs. Experiments on datasets from various domains and sizes show that our approach reduces memory consumption and training time without significantly losing performance.
PTCP: Alleviate Layer Collapse in Pruning at Initialization via Parameter Threshold Compensation and Preservation

Over-parameterized neural networks have good performance, but training such networks is computationally expensive. Pruning at initialization (PaI) avoids training a full network, which has attracted intense interest. But at high compression ratios, layer collapse severely compromises the performance of PaI. Existing methods introduce operations such as iterative pruning to alleviate layer collapse. However, these operations require additional computing and memory costs. In this paper, we focus on alleviating layer collapse without increasing cost. Therefore, we propose an efficient strategy called parameter threshold compensation. This strategy constrains the lower limit of network layer parameters and uses parameter transfer to compensate for layers with fewer parameters. To promote a more balanced transfer of parameters, we further propose a parameter preservation strategy, using the average number of preserved parameters to more strongly constrain the layers that reduce parameters. We conduct extensive experiments on five pruning methods on Cifar10 and Cifar100 datasets using VGG16 and ResNet18 architectures, verifying the effectiveness of our strategy. Furthermore, we compare the improved performance with two SOTA methods. The comparison results show that our strategy achieves similar performance, challenging the design of increasingly complex pruning strategies.
Replay-Based Online Adaptation for Unsupervised Deep Visual Odometry

Online adaptation is a promising paradigm that enables dynamic adaptation to new environments. In recent years, there has been a growing interest in exploring online adaptation for various problems, including visual odometry, a crucial task in robotics, autonomous systems, and driver assistance applications. In this work, we leverage experience replay, a potent technique for enhancing online adaptation, to explore the replay-based online adaptation for unsupervised deep visual odometry. Our experiments reveal a remarkable performance boost compared to the non-adapted model. Furthermore, we conduct a comparative analysis against established methods, demonstrating competitive results that showcase the potential of online adaptation in advancing visual odometry.
Randomized Functions with High Round Complexity

Consider two-party secure function evaluation against an honest-but-curious adversary in the information-theoretic plain model. We study the round complexity of securely realizing a given secure function evaluation functionality. Chor-Kushilevitz-Beaver (1989) proved that the round complexity of securely evaluating a deterministic function depends solely on the cardinality of its domain and range. A natural conjecture asserts that this phenomenon extends to functions with randomized output. Our work falsifies this conjecture – revealing intricate subtleties even for this elementary security notion. For every r, we construct a function $$f_r$$ with binary inputs and five output alphabets that has round complexity r. Previously, such a construction was known using $$(r+1)$$ output symbols. Our counter-example is optimal – we prove that any securely realizable function with binary inputs and four output alphabets has round complexity at most four. We work in the geometric framework Basu-Khorasgani-Maji-Nguyen (FOCS–2022) introduced to investigate randomized functions’ round complexity. Our work establishes a connection between secure computation and the lamination hull (geometric object originally motivated by applications in hydrodynamics). Our counterexample constructions are related to the “tartan square” construction in the lamination hull literature.
Multiscale Network with Equivalent Large Kernel Attention for Crowd Counting

Most of the existing crowd counting methods are based on convolutional neural networks (CNN) to solve the crowd scale and background noise problems. These methods can effectively extract local features, but their convolutional kernel sizes are limited so that it is hard to obtain global information which is also crucial for scale awareness and noise discrimination. In this paper, we propose a Multiscale Network with Equivalent Large Kernel Attention for Crowd Counting (MELANet), which can extract both global and local information based on CNN. MELANet is composed of three parts: feature extraction module (FEM) for original feature extraction, multiscale equivalent attention module (MEAM) for global and local information combination, and fusion module (FM) for multiscale feature fusion. In MEAM, by decomposing large convolution kernels into equivalent combinations of small convolution kernels, the model obtains receptive fields equivalent to the large convolutional kernels with lower complexity and less parameters. It enables local and global correlation in the attention mechanism based on CNN, which makes the model focus more on the crowd head region to resist the background noise. Besides, we use a multiscale structure and different convolution kernel sizes to encode contextual information at different scales into the feature maps to deal with head scale transformations. Furthermore, we add gate channel attention units in MEAM to enhance the channel adaptivity of the model. Extensive experiments demonstrate that MELANet can achieve excellent counting performance on three popular crowd counting datasets.
Technologies for The Development of Special Competencies of Future Russian Language Teachers

Article discusses technologies for the development of special competencies of future Russian language teachers, modern approaches in the educational system of the Republic of Uzbekistan; the differences between the competencies and competencies of the teacher are revealed, the formation of the communicative competence of teachers of the Russian language and literature is considered.
Fun with English Activity for Junior High School 8 Palopo

This research is to enhance students’ communicative and English skills of students of Junior High School 8 Palopo, Indonesia. The method used is teaching and learning English with providing pre-test, applying fun with English activities, and giving post-test as the report. The procedures of applying the fun with English activity are giving ice breakers, applying games, explaining materials, grouping the students, providing practice, and evaluating the students’ achievement and the process of the activity. There are several games used in the activity, such as whispering, introducing other and total physical response. It shows that students are eager and interested in learning English that can be seen in their full participation in the Fun with English activity. Also, students are eager to communicate with others by providing communicative and fun activities that is emphasized in the grouping activities. This part enables students to communicate with their peers and tutors effectively. The implication of the study can be a reference for other school to apply the same activity and to collaborate with English Education Study Program to apply the similar activity to increase students’ communicative and English skills.
Pengujian Black Box pada Aplikasi E-Commerce OpenCart dengan Metode Equivalence Partitioning dan Boundary Value Analysis

E-commerce applications have become a significant trend in modern trade. One popular open-source e-commerce application is OpenCart. Testing the application is paramount to ensure that OpenCart functions appropriately and meets the required quality and performance. This study aims to conduct Black Box testing on the OpenCart e-commerce platform using the Equivalence Partitioning (EP) and Boundary Value Analysis (BVA) methods. The testing is carried out to ensure the application's functionality directly relates to user satisfaction and trust. EP divides input data into partitions based on valid and invalid values, while BVA tests input values based on specific boundaries. The focus of the testing is on the checkout feature, which is an essential component of any e-commerce application. The testing flow follows the Software Testing Life Cycle (STLC) approach. The test results show a 57.14% level of conformity using EP and 100% using BVA. Based on these two results, the overall conformity level is 78.57%. The testing results indicate that the system performs well within the tested boundary values but may require improvements in the combination of partition usage.STLC (Software Testing Life Cycle). Hasil pengujian menunjukkan tingkat kesesuaian sebanyak 57,14% pada EP dan 100% pada BVA. Berdasarkan dua hasil tersebut, didapatkan tingkat kesesuaian sebesar 78,57%. Hasil pengujian menunjukkan jika sistem dapat berjalan dengan baik pada nilai-nilai batasan yang diuji, tetapi masih perlu perbaikan pada kombinasi penggunaan antara partisi.
Trip Planning and Recommendation System with AI Chatbot

The principle aim of this project is to develop a responsive website, which would comprehend the process of tour planning and management for users. With the increasing popularity of tourism and recreational activities all around the world has increased, there is a need for an advanced and smart tour planning system, which would make it easier for users to plan and execute trips. This proposed website is built in such a way that the user will get to know all aspects related to his trips, and there will be a ChatBot, which would make the booking procedure easier for the user. The Chatbot would suggest the user different destinations based on the user’s area of interest and make the decision-making process more rational. Also, it would help the user to gain more insights about their dream holidays for a smooth trip without any disturbances and mismanagements. Along with the ChatBot, we also integrated a virtual tour feature, which would give users a glance at their dream destinations. The development of the website involves various software integrated using a robust backend system, which will implement and handle the processing and retrieval of travel-related data, ensuring fast and accurate user queries. The front end of the website will be designed to provide an intuitive and visually appealing UI, enhancing the UX. Keywords — Chatbot, virtual tour, user interface, user experience, frontend, backend.
Development of fitness game applications for learning healthy lifestyle using unity game engine

A lot of individuals in today's society have a poor understanding of physical fitness and the benefits of adequate nutrition and a healthy diet. As a result, the researchers decided to take an important step in light of the general absence of information on physical fitness and its related benefits. Researchers decided to create a game application that would be a great instrument to impart information and understanding to individuals. The major purpose of this application is to introduce users to a variety of fitness routines, allowing them to experiment with various physical exercises. By doing so, it aims to not only increase people's familiarization with various forms of exercise but also inform them of the numerous benefits of incorporating regular physical activity into their lives. The project is a proactive effort to minimize the knowledge gap while promoting a healthier lifestyle among the users. While conducting the study the researcher considered the problem of first the engagement and how to sustain the user's interest in the application and second the user's understanding of using the application and its functionality third the lack of information that the game developers provided in creating an application and last is how to identify the things that the researcher can do to make the flow of the gameplay improved. the researchers use some of the sub-characteristic of the ISO25010 that are appropriate for evaluating the gaming application. After evaluating the game application, the sub-characteristics of Functional Suitability garnered an overall weighted mean of 3.94 Performance efficiency and Usability both have a 3.81 Security has a 3.56, and Portability 3.82, the researchers used the confusion matrix with the help of the tool of Weka Software using the scheme of weka.classifiers.bayes.NaiveBayes the evaluation on the training set has a summary of Correctly classified instances of 90% and Incorrectly classified instances of 10% it indicates that the application has an accurate algorithm.
Context of Data and Uncertainty: One of the Challenges for Numeracy Provision in Elementary School Teacher Education

Background : Student numeracy reflects their interaction with the environment, especially with their teacher. Therefore, the formation of teacher numeracy must start from their education when in college. Objectives : This study explores the numeracy, which parts become limited or lacking, and what hinders and supports those skills. Design : This study employs an exploratory descriptive method and involves case studies to achieve the objectives. Setting and Participants : The research participant is 84 students of the department of elementary school education at one of the public universities in Indonesia, consisting of 3 classes: undergraduate, postgraduate, and postgraduate cooperation class. Data collection and analysis : Numeracy tests and semi-structured cognitive interviews are used to collect data. The test results were analyzed using descriptive statistics to see the numeracy profile, and essential parts were clarified in the discussion. Results : Our findings show that for all classes, statistics and probability became the content with the lowest achievement on this test. We also highlight that the undergraduate class become the best class based on the test results, followed by the regular postgraduate class and the postgraduate cooperation class. In other words, this study's teaching experience and level of education are not enough to help students solve numeracy problems. Conclusions : Data and uncertainly (quantitative) literacy must emphasize improvements and potential studies for subsequent researchers. The implications of our research are also focused on teacher education; numeracy provision should not only be noted on in-service teachers but also target their education in college.
FUNCTIONAL LEVEL STRATEGIES AND PERFORMANCE OF SAFARICOM PUBLIC LIMITED COMPANY, KENYA

This study examined the effect of functional-level strategies on the performance of Safaricom PLC, Kenya. The specific objectives of the study were: to determine the effect of marketing strategies on the performance of Safaricom PLC, Kenya ; to establish the effect of human resource strategies on the performance of Safaricom PLC, Kenya; to determine the effect of financial strategies on the performance of Safaricom PLC, Kenya and to establish the effect of operational strategies on the performance of Safaricom PLC, Kenya. The study was anchored on the Balanced Score Card Model, Resource-based View Theory, and Mintzberg’s Strategy. The study adopted a descriptive survey design, with the targeted population as 2262 senior-level employees from Safaricom PLC. The study sample was derived using the systematic sampling technique to obtain 153 employees as respondents. Primary data was collected using structured closed-ended questionnaires, before the actual study a validity and reliability test was be performed during the pilot stage to ascertain that the data collected is valid. After obtaining data, the data was coded and entered into the SPSS version 26 statistics software which was used as the tool of analysis. A descriptive model was used to analyse data and establish the relationship between functional strategies and performance. All ethical measures and guidelines were adhered to throughout the study period. From the study findings, the study independent variables; human resource strategies, marketing strategies, financial strategies and operational strategies were all found to affect performance. The measures that stood out negatively were position of Safaricom PLC in the current market and promotion of employees, although Safaricom seems to be the market leader, competitors have found a way to eat into its market share while most employees seemed to strongly disagree on there being promotional strategies within the organization. The study therefore concluded that all the variables used in the present study are fundamental for organization performance in the telecommunication industry. Further, the study recommended that Safaricom PLC come up with strategies that will ensure it is fully in control of its market share, it should also find a way to implement promotion strategies within the organization. The study recommended that further studies be conducted in other telecommunication companies in Kenya to see whether the variables used in this study similarly affect them. Key Words: Marketing Strategies, Human Resource Strategies, Financial Strategies, Operational Strategies CITATION : Opar, J. A., &amp; Mwasiaji, E. (2023). Functional level strategies and performance of Safaricom Public Limited Company, Kenya. The Strategic Journal of Business &amp; Change Management, 10 (4), 1306 – 1318. http://dx.doi.org/10.61426/sjbcm.v10i4.2819
PERANCANGAN SISTEM INFORMASI AKADEMIK MOBILE DENGAN SCRUM FRAMEWORK

A mobile application-based academic information system is an information system that provides academic information for students and lecturers that can be accessed from mobile devices from anywhere. With the existence of a mobile application-based academic information system, students and lecturers can be given alternatives to carry out academic activities on an online academic information system from mobile devices so as to provide convenience and flexibility. The research method was carried out by conducting interviews with lecturers and several students to obtain information about the needs of the academic information system. Meanwhile, the system designer's method uses the Scrum. Scrum is a framework that is suitable to be implemented in making academic information systems because Scrum is used in a work context that is quite complicated where cases cannot be predicted. The result of the implementation is that some of the information in the academic information system that can be accessed is the siakad login page, the siakad dashboard, and others so that it can maximize academic activities which of course can also make it easier for students and lecturers to get services and information about academic matters.