Reviewerly/exHarmony huggingface dataset:
Revisiting the effects of the Spectre and Meltdown patches using the top-down microarchitectural method and purchasing power parity theory

Software patches are made available to fix security vulnerabilities, enhance performance, and usability. Previous works focused on measuring the performance effect of patches on benchmark runtimes. In this study, we used the Top-Down microarchitecture analysis method to understand how pipeline bottlenecks were affected by the application of the Spectre and Meltdown security patches. Bottleneck analysis makes it possible to better understand how different hardware resources are being utilized, highlighting portions of the pipeline where possible improvements could be achieved. We complement the Top-Down analysis technique with the use a normalization technique from the field of economics, purchasing power parity (PPP), to better understand the relative difference between patched and unpatched runs. In this study, we showed that security patches had an effect that was reflected on the corresponding Top-Down metrics. We showed that recent compilers are not as negatively affected as previously reported. Out of the 14 benchmarks that make up the SPEC OMP2012 suite, three had noticeable slowdowns when the patches were applied. We also found that Top-Down metrics had large relative differences when the security patches were applied, differences that standard techniques based in absolute, non-normalized, metrics failed to highlight.
On the Optimization of Self-Organization and Self-Management Hardware Resource Allocation for Heterogeneous Clouds

There is a tendency, during the last years, to migrate from the traditional homogeneous clouds and centralized provisioning of resources to heterogeneous clouds with specialized hardware governed in a distributed and autonomous manner. The CloudLightning architecture proposed recently introduced a dynamic way to provision heterogeneous cloud resources, by shifting the selection of underlying resources from the end-user to the system in an efficient way. In this work, an optimized Suitability Index and assessment function are proposed, along with their theoretical analysis, for improving the computational efficiency, energy consumption, service delivery and scalability of the distributed orchestration. The effectiveness of the proposed scheme is being evaluated with the use of simulation, by comparing the optimized methods with the original approach and the traditional centralized resource management, on real and synthetic High Performance Computing applications. Finally, numerical results are presented and discussed regarding the improvements over the defined evaluation criteria.
Document AI: Benchmarks, Models and Applications

Document AI, or Document Intelligence, is a relatively new research topic that refers to the techniques for automatically reading, understanding, and analyzing business documents. It is an important research direction for natural language processing and computer vision. In recent years, the popularity of deep learning technology has greatly advanced the development of Document AI, such as document layout analysis, visual information extraction, document visual question answering, document image classification, etc. This paper briefly reviews some of the representative models, tasks, and benchmark datasets. Furthermore, we also introduce early-stage heuristic rule-based document analysis, statistical machine learning algorithms, and deep learning approaches especially pre-training methods. Finally, we look into future directions for Document AI research.
NLP and ML Based Framework for Covid-19 Statistical Analysis

Today internet has a big number of social media sites for online learning, developing new ideas, and sharing their ideas, thoughts, opinions, views, and feelings about anything with a huge amount of people throughout the world. Nowadays it is very free to discuss your ideas and establish own business using social media platforms such as Facebook, Twitter, websites and others, and to raise one's own voice and share their opinions on a regular basis. Sentiment analysis from Twitter has recently among the largest attractive academic disciplines. For the aim of developing such systems, it blends natural language processing techniques with data mining technologies. I presented an effective technique for analysing Twitter sentiment in this research. To compare accuracy, several machine learning classification methods such as naive bayes, SVM, KNN, decision tree, logistic regression were developed for recognising positive and negative tweets.
Retraction Note: Tropical coastal climate change based on image defogging algorithm and swimming rehabilitation training

This paper first designs and implements an image defogging algorithm based on FPGA. The hardware design mainly includes SDRAM control module, SD card control module, data transmission control module, and convolution neural network calculation module. The SDRAM control module controls the working state of SDRAM; the SD card control module controls the working state of SD card; the data transmission control module controls the data transmission of reading and writing SDRAM; and the convolution neural network module is responsible for the image defogging algorithm. Based on this point, this paper uses the medium complex ocean atmosphere combination model to estimate the SST diversity and climate characteristics of the tropical coastal Pacific and Atlantic Ocean in recent years, and studies and analyzes the influencing factors of the prediction. The atmospheric part of the coupled model is the global atmospheric circulation model CCM3, and the oceanic part is a simplified gravity model of Zebiak Cane type. In addition, an air filter is added to the coupled model to test the impact of natural noise during broadcasting. Finally, the article investigated the positive effect of swimming rehabilitation training on chronic neck pain symptoms of office workers; Objectives were to study the effect of various swimming rehabilitation trainings on neck pain of office workers and investigate the effect of different swimming rehabilitation training cycles on chronic neck pain symptoms of office workers. Objective was to provide a theoretical basis for the development of exercise rehabilitation plan to improve the chronic pain symptoms of office workers. The results showed that breaststroke training and freestyle training were effective in treating neck pain of office workers. In this paper, the application of adaptive image defogging algorithm and the correction of coastal climate change in the rehabilitation training research has promoted the healthy development and progress of swimming rehabilitation training.
Pohon Industri (Interaktif) Sebagai Pengembangan Layanan Informasi di Perpustakaan PDDI LIPI

(Objectives) The purpose of writing this paper is to explain the process of making industrial trees (interactive) as an information service development activity carried out by the Center for Scientific Data and Documentation (PDDI) which is useful for the community because it provides an overview and stimulates entrepreneurs to carry out and develop product diversification with economic value through the schematic presented . (Method) The stages of making an industrial tree start from topic selection, literature search, article selection and analysis, then proceed with visualization which is then posted on the web. . (Findings) The result of this research showed that audit experience, audit expertise, and locus of control significantly positive affect audit judgment taken by the auditor. Keywords: Information service s , Industrial tree (interactive), PDDI LIPI .
VLSI Architecture for Smart and Precision Agriculture Using Sensors

World population has almost doubled during the last century increasing dramatically the need for food to support the population over 7 billion persons. Precision agriculture is a type of innovative agriculture, based on new technologies, which aims to streamline the agricultural process. Parameters such as temperature, humidity, soil characteristics, and nutrients all play a role in plant development. Numerous sensors are available to measure the environmental factors and depending on datum, the devices monitor and control the parameter changes for each crop. Some existing technologies are more complex and very complicated for farmers to implement. The main goal of this project is to produce precision agriculture-based grid with high performance and low energy consumption.
Sistem Informasi Beladiri Berbasis Web

The belt level increase test is a routine activity carried out by Provincial Board (Pengprov) Taekowndo in Gorontalo at every period. The registration process for the taekwondo belt increase test is still done manually by using the registration from on the paper. Additionally, the information regarding Taekwondo (traditional Korean martial arts) in Gorontalo is still less. This research applies a waterfall method, and the design system uses a unified modeling language (UML) deign consisting of a case diagram, activity diagram, sequence diagram, and class diagram. The finding of this research is in the form of martial arts information system that can specifically facilitate the registration process for the taekwondo belt increase test and help get information easily. Besides, this system is tested with black-box and white-box testing and has met the criteria in accordance with the objectives. Keywords: Belt Level Increase Test, Taekwondo, Martial Arts Information System, Waterfall, UML.
Understanding Type I Errors, Type II Errors, and P-values

The goal of the activity is for students to develop an intuition about Type I and Type II errors, the relationship between Type I errors and the significance level, and how to calculate P-values in the context of a one-sample z -test. Students first explore how Type I and Type II errors can occur in real-life settings. They then use an interactive resource to learn how to calculate P-values of a two-sided z -test. Afterwards, they use another interactive resource to understand how the significance level is related to the probability of a Type I error. Finally, they apply what they learned in the earlier sections on an applied exercise.
Visual Decision-Support for Live Digital Forensics

Performing a live digital forensics investigation on a running system is challenging due to the time pressure under which decisions have to be made. Newly proliferating and frequently applied types of malware (e.g., fileless malware) increase the need to conduct digital forensic investigations in real-time. In the course of these investigations, forensic experts are confronted with a wide range of different forensic tools. The decision, which of those are suitable for the current situation, is often based on the cyber forensics experts' experience. Currently, there is no reliable automated solution to support this decision-making. Therefore, we derive requirements for visually supporting the decision-making process for live forensic investigations and introduce a research prototype that provides visual guidance for cyber forensic experts during a live digital forensics investigation. Our prototype collects relevant core information for live digital forensics and provides visual representations for connections between occurring events, developments over time, and detailed information on specific events. To show the applicability of our approach, we analyze an exemplary use case using the prototype and demonstrate the support through our approach.
Capturing Logical Structure of Visually Structured Documents with Multimodal Transition Parser

While many NLP pipelines assume raw, clean texts, many texts we encounter in the wild, including a vast majority of legal documents, are not so clean, with many of them being visually structured documents (VSDs) such as PDFs. Conventional preprocessing tools for VSDs mainly focused on word segmentation and coarse layout analysis, whereas fine-grained logical structure analysis (such as identifying paragraph boundaries and their hierarchies) of VSDs is underexplored. To that end, we proposed to formulate the task as prediction of "transition labels" between text fragments that maps the fragments to a tree, and developed a feature-based machine learning system that fuses visual, textual and semantic cues.Our system is easily customizable to different types of VSDs and it significantly outperformed baselines in identifying different structures in VSDs. For example, our system obtained a paragraph boundary detection F1 score of 0.953 which is significantly better than a popular PDF-to-text tool with an F1 score of 0.739.
Implementasi Chatbot Untuk Laporan Bug / Error Reporting Dengan Natural Languange Processing (Studi Kasus : CV. Solusi Prima)

Research at CV. Solusi Prima was done to help implement chatbot on bug report, in order to solve some problems in interaction with client. We use some research method such as observation, interview, and library research. From this research's result there are some problem with working system that is client and staff in terms of report bug, make report which is done separately and manually (via whatsapp). This matter make staff become confused between chat to see report bug and for staff to make report bug for their head manager, and for head manager it's cost a lot of time to recap all of his staff's works in patch bug. Especially if their customer and the staff keep report error numerously, can cause older report bugs not being recorded in system, or some report being recorded but not tidy enough, so the staff will get confused to input report bug one by one, because of that we use this chatbot system based on natural language processing method with messenger application (telegram).Aim to make it easier for staff and client in making bug reports, so no need for staff to make report manually without having to lose previously datas, other than that the data that is display base on realtime so whatever report that is display will automatically broadcasted by system to all company's staffs from the results of the testing, system worked is compatible with what we are planning from the start. With the use of chatbot that have been equipped with natural language processing, make user easier to do reporting without moving their hands from keyboard to mouse too often. All the informations in chatbot will be saved and can be used for doing data analythic, so making chatbot easier to use and making it easy for staff and client to report a bug.
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent

We focus on the problem of efficient sampling and learning of probability densities by incorporating symmetries in probabilistic models. We first introduce Equivariant Stein Variational Gradient Descent algorithm -- an equivariant sampling method based on Stein's identity for sampling from densities with symmetries. Equivariant SVGD explicitly incorporates symmetry information in a density through equivariant kernels which makes the resultant sampler efficient both in terms of sample complexity and the quality of generated samples. Subsequently, we define equivariant energy based models to model invariant densities that are learned using contrastive divergence. By utilizing our equivariant SVGD for training equivariant EBMs, we propose new ways of improving and scaling up training of energy based models. We apply these equivariant energy models for modelling joint densities in regression and classification tasks for image datasets, many-body particle systems and molecular structure generation.
NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Stochastic Systems

In this paper, we introduce NNSynth, a new framework that uses machine learning techniques to guide the design of abstraction-based controllers with correctness guarantees. NNSynth utilizes neural networks (NNs) to guide the search over the space of controllers. The trained neural networks are "projected" and used for constructing a "local" abstraction of the system. An abstraction-based controller is then synthesized from such "local" abstractions. If a controller that satisfies the specifications is not found, then the best found controller is "lifted" to a neural network for additional training. Our experiments show that this neural network-guided synthesis leads to more than $50\times$ or even $100\times$ speedup in high dimensional systems compared to the state-of-the-art.
Membership Inference Attacks Against Self-supervised Speech Models

Recently, adapting the idea of self-supervised learning (SSL) on continuous speech has started gaining attention. SSL models pre-trained on a huge amount of unlabeled audio can generate general-purpose representations that benefit a wide variety of speech processing tasks. Despite their ubiquitous deployment, however, the potential privacy risks of these models have not been well investigated. In this paper, we present the first privacy analysis on several SSL speech models using Membership Inference Attacks (MIA) under black-box access. The experiment results show that these pre-trained models are vulnerable to MIA and prone to membership information leakage with high adversarial advantage scores in both utterance-level and speaker-level. Furthermore, we also conduct several ablation studies to understand the factors that contribute to the success of MIA.
Experimental Evaluation of Indexing Between HBase and MongoDB

HBase and MongoDB are non-relational databases where HBase is column-oriented, and MongoDB is documentoriented.MongoDB uses indexing to speed up the searching process, while HBase does not have secondary indexing, a lookup table was created to save an index of searching.This research used a prototype developed in a similar data model design of the HBase and MongoDB database to compare the response time performance in searching operation using keyword frequency criteria in the document management system.The results show that the primary indexes of HBase is faster than secondary indexes of MongoDB.The time difference between HBase and MongoDB is almost a half.
A Case Study of Using PBL

This study examines the effectiveness of the study through a case of PBL(problem-based-learning) class conducted in a balanced culture course called at 00- University in the second semester of 2020. The effects of learning are as follows: First, PBL(problem-based-learning) has sufficient active interaction between the teacher and the learner. In the face of prolonged non-face-to-face learning, the PBL teaching method has sufficient interaction between the professors-learner and the learner. Second, PBL learning can actively utilize various problems that fit the characteristics of the subject and actively utilize the process of role sharing and collaboration. By presenting various problem situations suitable for the subject, students will be able to share roles individually or as a team, and fully experience the process of collaboration and discussion in the process of investigating the data. Third, critical perceptions of problem situations can be extended. In modern times, a variety of problem situations arise and critical perceptions of them must be fully learned. In a mass production and mass consumption society, students should develop the ability to blindly recognize and distinguish between real and fake information in a flood of information. The limitations identified in this class case are, first, the nature of the subject, Understanding Culture and Philosophy, which makes it possible to discuss the global cultural phenomenon, but it should be discussed in terms of philosophy. Second, it is not easy to work as a team on non-face-to-face online. Nevertheless, PBL is a very effective method of learning in which active interactions and learning activities take place between professors and students, whether face-to-face or face-to-face online learning.
Problem Based Learning Untuk Meningkatkan Keaktifan dan Motivasi Belajar Peserta Didik Kelas II di SDN Dukuh Menanggal I

Student activity and learning motivation of class II students are still low, so it is necessary to implement a Problem Based Learning model to improve it. This study aims to increase student activity and motivation in learning theme 9 through the Problem Based Learning model in class II Semester 2 SDN Dukuh Menanggal I. This research is a classroom action research conducted in three cycles consisting of two meetings each cycle. Data collection techniques on student research, questionnaires and observation sheets. The results of the study after using the Problem Based Learning model in the first cycle showed an increase of 57. In the second cycle there was an increase with the acquisition of the average value of student activity reaching 71 and Cycle 3 experiencing a significant increase seen from the average results obtained. get reached 84 students' learning motivation in cycle 1 obtained the average value of student activity reached 53. Cycle 2 the average value of student learning motivation reached 70. There was an increase in cycle 3 which was quite significant seen from the average results obtained reached 86. It was concluded that the Problem Based Learning model could increase students' activeness and motivation in learning the theme of 9th grade class II Semester 2 SDN Dukuh Menanggal I.
온라인 기반 놀이치료 훈련 프로그램 만들어 가기에 관한 참여적 실행연구: 숙련 놀이치료자의 발달장애아동 놀이치료 교육

Objectives: The purpose of this study is to develop a play therapy training program as a form of alternative learning for children with developmental disabilities. This study conducted a play therapy training program for experienced play therapists using flipped learning and analyzed the process of cooperation and change between researchers and the play therapists. Methods: To enable researchers and participants to create an online-based play therapy training program and to analyze the process of cooperation and change, a participatory action research (PAR) methodology was applied. Five play therapists with six to seven years of play therapy experience were the participants. The study analyzed various qualitative data (recorded materials for online conferences, learning results, video, online community activity materials, chat rooms, e-mail, participants’ reflection essay, researcher notes, interview materials). Results: This study followed a process in which participants discussed specific educational contents and method through online communication, and then reflected on this discussion in the subsequent educational session. This study composed the final program through three action research processes. Also, the results of the study revealed topics such as: 1) Come alive (subtopics: Notice, “I’m not a beginner...; Be aware, “I know but…”); and, 2) Alive-alive (subtopics: “LAN cable” colleagues; Learning “with my own”; Learning “in my opinion”) in the process of cooperation and change between researchers and participants. Conclusion: The findings of this study could be used for creating an online educational environment as a form of alternative learning. It is expected that the application of Participatory Action Research (PAR) methodology, that helps participants become subjects of learning through a cooperative process and mutual communication with researchers, can help overcome the limitations of the existing play therapy supervision method.