Reviewerly/exHarmony huggingface dataset:
Cache Antagonists Identification: A Practice from Alibaba Colocation Datacenter

Colocating latency-critical (LC) jobs and best-effort (BE) jobs on a host effectively improve resource efficiency in modern datacenters. But it increases resource contention between jobs, which seriously affects job performance. In Alibaba's real- world LC- BE colocation datacenters, we observed that cache is one of the most contended resources in the CPU. When cache contention occurs, identifying the antagonists that caused cache resource contention is the first step to mitigate cache contention, called cache antagonists identification (CAl). However, it is chal-lenging to identify cache antagonists because cache contention is difficult to observe and quantify. In this paper, we first propose cache usage graph (CUG) to finely characterize cache usage of jobs in the multiple CPU microarchitectural hierarchies and locations, and we provide a monitoring tool to collect “per-container-per-logic CPU” Ll/2/3 cache misses and build CUG. Then we propose a CUG-based CAl approach, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> Tactic. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> Tactic leverages machine learning models to quantify the cache contention on every cache hierarchy, then reasons out the cache antagonists with CUG. Experiments in production datacenters show that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> Tactic has a high precision (85+%) and low cost (32 ms), which are better than state-of-the-art approaches.
An Improved Top-Eye Trajectory Anomaly Detection Method Integrating Semantic Features

Anomalous trajectory detection is an important research content in the field of trajectory data mining. The anomaly detection method based on Top-k evolving trajectory outlier detection (TOP-EYE) is an effective anomaly detection method for moving objects that adopts evolutionary computation to detect anomalies from two perspectives of density and direction. However, the TOP-EYE method only considers two factors of density and direction, ignoring the multi-dimensional semantic features of trajectory data. In order to improve the accuracy of anomaly detection of trajectory data, this paper proposes an improved TOP-EYE method integrating semantic features, which adds trajectory semantic features for anomaly trajectory detection based on TOP-EYE. Experiments show that the proposed improved method integrating semantic features can more accurately identify anomalous trajectories than TOP-EYE, which is helpful for mining potential trajectory outliers with anomalous semantic features.
Brahmi Script Classification using VGG16 Architecture Convolutional Neural Network

Many Indonesians have difficulty reading and learning the Brahmi script. Solving these problems can be done by developing software. Previous research has classified the Brahmi script but has not had an output that matches the letter. Therefore, letter classification is carried out as part of the process of recognizing Brahmi script. This study uses the Convolutional Neural Network (CNN) method with the VGG16 architecture for classifying Brahmi script writing. Training results from various amounts of image data. Smooth model. The requested image data is a 224x224 binary image. This study has the highest quality, accuracy is 96%, highest recall is 98% and highest precision is 98%.
AN ANALYSIS OF THE VALIDITY OF ENGLISH TEST MADE BY THE TEACHER IN SMP NEGERI 1 BONTOMARANNU

This study aims to determine whether the English test made by the teacher is in accordance with the content validity. To determine the content validity, the researcher compare the English test with the syllabus, lesson plan, and the material that has been taught. This study uses a qualitative descriptive method by using document analysis in the form of mid- semester test and interview from the teacher. This research collects data from English teacher who teaches in seventh grade in SMP Negeri 1 Bontomarannu.The results obtained after analysis the test made by the teacher, a total of 15 questions were made by the teacher who teaches in seventh grade in the mid- semester test. There are 13 questions that are categories accordance with the content validity, namely according to the syllabus, lesson plan, and the material that has been taught. While there are 2 questions that are categories as not accordance with the content validity. And from the results of interview, it can be concluded that the teacher made a grid of questions before making test. Test made by the teacher must be in accordance with the syllabus, lesson plan, and the material that has been taught and according to the level of student ability.
Semantic Line Detection: A Survey

Semantic line detection plays a specific guiding role in aesthetic composition, photography, and so on. This paper introduces the method of semantic line detection and summarizes its present situation. The existing semantic line detection techniques are summarized from the traditional and deep learning-based methods, and the corresponding advantages and disadvantages are pointed out. Based on some difficulties existing in the semantic line detection method, this paper puts forward its own prospects for the future development direction of semantic line detection.
Information Literacy Needs in Remote Areas (Case Study on The Island of Aceh)

The purpose of this research is to know the needs of information literacy in remote areas of Aceh Island. The method in this study is a qualitative research method with a direct approach to the source of research informant namely Pulo Aceh community, long time this research was conducted for four months in Pulo Aceh. The result of this research is that the people of Pulo Aceh should get the same rights in fulfilling the needs of information, they also have the same needs of information literacy as other communities. Therefore, libraries need to pay attention to areas that are left behind in the dissemination of information. The library as an information center should have an effort to reach isolated areas such as Pulo Aceh. Libraries must provide service information regardless of ethnic, racial, geographic location, and other backgrounds. The role of the library in fulfilling the information needs to Pulo Aceh community is more noted and prioritized as an isolated area. With the fulfillment of information needs, they are able to develop competitive with other communities to improve information literacy, but more advanced technological development in order to provide comprehensive information services to remote areas. So they can get information quickly, and relevant and adapted to the needs of the community Pulo Aceh.
IVRMaker, An Interactive and Customizable Telephone Chatbot Services Platform

Chatbots are systems that imitate human conversation by using Artificial Intelligence. They have been designed to interact with users by using natural language in a way that they think they are having a dialogue with a human. The relevance of these is gaining impact in our society, being widely applied to numerous fields, from Health Care to Education. Although their usage is associated with different purposes such as virtual assistant, entertainment, domotic, and routing, they are becoming increasingly popular in business domains, managing the customer services since they can automate, optimize and manage business processes and marketing campaigns. However, it is an arduous task to integrate them into the business data flows to take advantage of their potential and stand out from the competence. Therefore, in this work, we have described IVRMaker, an interactive and customizable telephone chatbot services platform. The target behind the IVRMaker is to help companies to integrate a conversational assistant into their business process. The platform is mainly based on cutting-edge research areas in Natural Language Processing to facilitate easy integration into the business data model. The evaluation of the platform was carried out in two use cases relating to disparate domains. The results obtained were interesting in demonstrating the applicability and adaptability of these assistants and their direct impact on the automatization of customer services and marketing campaigns.
Frequency of the misuse of prepositions by Albanian students: A case study with first year students studying Greek, Italian and German at the Faculty of Foreign Languages

This study looks at English prepositions and the challenges that students learning English as a Foreign Language (EFL) encounter in mastering them. The purpose of this study is to describe the problems Albanian students studying English have in discovering out how to use prepositions correctly. Examples of preposition misuse are taken from the descriptive essays submitted by Albanian students at the University of Tirana, Faculty of Foreign Languages. We acknowledge that inappropriate use of prepositions was one of the ten most common errors observed in the collected corpus of essays based on the findings of the study conducted with students of the Faculty of Foreign Languages. The findings suggest that Albanian students studying English as a foreign language have difficulty using prepositions correctly. Prepositions are the most frequently misused part of speech. By, at, in, on, to, of, and for were the most often misused prepositions.
Research on the Construction of the Advanced Nuclear Power Standards System Platform

Abstract Advanced nuclear power standards system is a new type of nuclear power standards system composed of all kinds of standards in the nuclear energy field. At present, the research results of the two stages of the subject and the achievements of nuclear power standard construction in recent years are scattered. It is necessary to establish a single nuclear power standard database to process and develop the standards in the field of nuclear power and various research results, reports, process documents, data and other databases, so as to provide a good data environment for the majority of nuclear engineering scientists and technicians. In order to solidify the research results of advanced nuclear power standard system and nuclear power standard data, the application support platform of nuclear power standard system adopts innovative SaaS cloud service mode to build an Internet-based cloud service platform with process engine and knowledge map technology. Through the integration of informatization and standardization, standardize the standardization process, we can provide convenient and fast information services, vigorously expand the scope of participants in nuclear power standardization and the depth of participation of all parties, and realize the standard system management mode of autonomy, industry participation and dynamic updating.
Hestia: A Cost-Effective Multi-dimensional Resource Utilization for Microservices Execution in the Cloud

It is well-known that effective resource utilization is a critical factor in providing high quality microservicess in cloud computing. In a large-scale cluster, if each machine can save a small amount of resources, a huge effect could be made to significantly reduce the overall computing cost as the saved resources across the cluster can be gathered into a large resource pool to facilitate the computation as a whole. As such, how to effectively allocate the resources in a single host is critical to the success of this saving strategy. To this end, we propose a multi-dimensional resource allocation algorithm, called Hestia, for a single machine in a stand-alone environment with each dimension having limited resources. The algorithm is designed by leveraging dynamic programming (DP) techniques to squeeze the occupied resources of the existing microservices without compromising their performance, and leave the saved resources for other newly deployed microservices. Our experimental results show that compared with the default case, this method can save up to $$15\%$$ of the resources for a single machine while ensuring the stability of online microservices.
CobNet: Cross Attention on Object and Background for Few-Shot Segmentation

Few-shot segmentation aims to segment images containing objects from previously unseen classes using only a few annotated samples. Most current methods focus on using object information extracted, with the aid of human annotations, from support images to identify the same objects in new query images. However, background information can also be useful to distinguish objects from their surroundings. Hence, some previous methods also extract background information from the support images. In this paper, we argue that such information is of limited utility, as the background in different images can vary widely. To overcome this issue, we propose CobNet which utilises information about the background that is extracted from the query images without annotations of those images. Experiments show that our method achieves a mean Intersection-over-Union score of 61.4% and 37.8% for 1-shot segmentation on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> respectively, outperforming previous methods. It is also shown to produce state-of-the-art performances of 53.7% for weakly-supervised few-shot segmentation, where no annotations are provided for the support images.
Data protection management process during remote biometric authentication

Remote biometric authentication systems have recently become widespread due to the need to use common devices and make payments over the Internet. Because biometric methods are more user-friendly and now quickly replace passwords, the task of transmitting biometric information over an open network without compromising it is becoming urgent. This work aims to upgrade the remote authentication system to increase the secrecy and security of user biometric data. In order to achieve this goal, it is proposed to use the best security methods for forming biometric templates, network steganography to increase secrecy, and the introduction of an intelligent decision-making system. These improvements will increase the security and privacy of data during the remote authentication process.
Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles

Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of en-semble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.
Penerapan Algoritma Xtea Dengan Metode Pembangkitan Kunci Linear Congruential Generator Untuk Pengamanan Teks Rahasia

Confidential text data is very vulnerable to wiretapping by irresponsible parties. The text is still plaintext, making it easier for the attacker to make use of it. Text data can be secured by several techniques. One of them is cryptographic techniques. Cryptographic techniques can secure secret text data by randomizing the hex value of the data into a data that cannot be recognized and read. One of the reliable algorithms is the eXtended Tiny Encryption Algorithm (XTEA) symmetry algorithm. The XTEA algorithm is a simple algorithm that is lightweight and fast in the encryption and decryption process. However, the concept of the symmetry algorithm is the encryption and decryption process using the same key. The security of this concept is very vulnerable if the key is leaked and is public. So that this can be optimized by generating keys using the Linear Congruential Generator (LCG) method. Key generation is used with prime numbers. The key sent to an eligible recipient is only the initial prime number. So that the recipient has to do key assembly first to carry out the decryption process. This can optimize the encrypted ciphertext using the XTEA algorithm.
Identifying the Characters of Lion and Fox in the Aesop’s Fables using Transitivity System

This research investigates the Aesop Fables characters, the Lion and Fox, characterization analysis using transitivity system. This research aims to identify the characterization of Lion and Fox based on transitivity system using types of processes. This research using descriptive qualitative methods to describe the prominent clause that represents the Lion and Fox characterization. The non-participant observation methods were used to collect data and referential identity methods to analyze the data. The results show that material and verbal processes are discovered as the prominent process to represent the Lion and Fox's characterization. Through material and verbal processes, the Lion and Fox appeared in different characterizations. The Lion is discovered in manipulative, wise, powerful, and foolish characterizations. Meanwhile, the Fox found in cunning and talkative characterization. The finding also shows that the author constructed material and verbal processes because they are suitable for the children's limited knowledge. After all, material and verbal clause can be understood by the children easily.Keywords: transitivity system, characterization, narrative-stylistics, Aesop’s fables
Sharing Pesan Gizi Seimbang dan Monitoring Pertumbuhan Anak Usia Dini di TK Pertiwi Singodutan II

The purpose of this activity is to do community service, especially in the health sector, that can improve the health status of children through monitoring children's growth and sharing balanced nutrition messages in TK Pertiwi Singodutan II. The implementation of this community service activity began with the lecture method and discussion between the Service team and Singodutan II Kindergarten students through sharing messages of balanced nutrition. Then proceed with early detection of growth irregularities through measurements of height, weight, and head circumference. The impact of this community service activity is information on the nutritional status of TK Pertiwi Singodutan II students (based on measurements of height, weight, and head circumference) which can be conveyed to parents as a reference to further improved the health status of early childhood.
A hierarchical model for automatic Neoplasm ICD coding

International Classification of Diseases (ICD) codes are used for different management activities in hospitals. Previous researches employed Machine Learning (ML) models for automatic coding to simplify the disease code assignation process; nevertheless, model performance was compromised due to problems with label imbalance and the high number of labels. In the present research, a Support Vector Machine (SVM) model for Neoplasm ICD coding was trained with a dataset previously treated by applying re-sampling methods to mitigate label imbalance issues and increase the model sensitivity. To mitigate the issue with the high number of labels, human body location information contained in the medical records and ICD code descriptions were employed to build a hierarchical model, which improved the performance of a base non-hierarchical model by up to 15%.
Reinforcement Learning with Success Induced Task Prioritization

Many challenging reinforcement learning (RL) problems require designing a distribution of tasks that can be applied to train effective policies. This distribution of tasks can be specified by the curriculum. A curriculum is meant to improve the results of learning and accelerate it. We introduce Success Induced Task Prioritization (SITP), a framework for automatic curriculum learning, where a task sequence is created based on the success rate of each task. In this setting, each task is an algorithmically created environment instance with a unique configuration. The algorithm selects the order of tasks that provide the fastest learning for agents. The probability of selecting any of the tasks for the next stage of learning is determined by evaluating its performance score in previous stages. Experiments were carried out in the Partially Observable Grid Environment for Multiple Agents (POGEMA) and Procgen benchmark. We demonstrate that SITP matches or surpasses the results of other curriculum design methods. Our method can be implemented with handful of minor modifications to any standard RL framework and provides useful prioritization with minimal computational overhead.
Recursos didácticos de pandemia: ¿innovación docente o espejismo?

This paper analyzes whether the teaching resources used during confinement, due to Covid-19, have represented a teaching innovation for the different groups of the UPC DEM.For this, two surveys have been used: in the first, questions have been asked about the tools used, the difficulties encountered and the interest in participating in a working session to share experiences.In the second, questions were asked about the use of the tools in four periods: before and during confinement, after the return and now.The conclusions are that the use of the different tools reached its peak during confinement; currently, these tools are used more and their possibilities are better exploited since the perception of them has improved.These results indicate that the resources used have served to improve and innovate in some aspects of DEM teaching.
Selective and Representative Sequence Feature Alignment for Domain Adaptive Detection Transformer

Recently, several studies have applied the Unsupervised Domain Adaptation (UDA) method on detection transformers to improve their cross-domain detection performance. However, the majority of them directly apply adversarial alignment on expatiatory token sequences, which will introduce too much background information and disturb the alignment process. To tackle the problem, we propose a domain adaption method focused on the detection transformer named selective and representative sequence feature alignment (SR-SFA). Specifically, our SR-SFA contains two modules: self-guided weight map generation module (SWG) and classification-guided domain query generation module (CQG). The SWG module takes full advantage of transformer detection capability to locate the foreground parts of the token sequences for local alignment. The other CQG module introduces an image-level multi-label classification task as an auxiliary task to capture the representative information of the whole image for global level alignment. Therefore, more effective feature alignment is performed in a local and global fashion. Experiments on two adaptation scenarios demonstrate our method gets better performance compared with other approaches.