Reviewerly/exHarmony huggingface dataset:
Stateful Switch: Optimized Time Series Release with Local Differential Privacy

Time series data have numerous applications in big data analytics. However, they often cause privacy issues when collected from individuals. To address this problem, most existing works perturb the values in the time series while retaining their temporal order, which may lead to significant distortion of the values. Recently, we propose TLDP model that perturbs temporal perturbation to ensure privacy guarantee while retaining original values. It has shown great promise to achieve significantly higher utility than value perturbation mechanisms in many time series analysis. However, its practicability is still undermined by two factors, namely, utility cost of extra missing or empty values, and inflexibility of privacy budget settings. To address them, in this paper we propose {\it switch} as a new two-way operation for temporal perturbation, as opposed to the one-way {\it dispatch} operation. The former inherently eliminates the cost of missing, empty or repeated values. Optimizing switch operation in a {\it stateful} manner, we then propose $StaSwitch$ mechanism for time series release under TLDP. Through both analytical and empirical studies, we show that $StaSwitch$ has significantly higher utility for the published time series than any state-of-the-art temporal- or value-perturbation mechanism, while allowing any combination of privacy budget settings.
Utilization of Base64 Encryption Repetition for QR Code Presence on Online Attendance

The implementation of the attendance system at the University of Muhammadiyah Bengkulu needs to be developed to be more effective in its implementation. This study aims to build a more effective attendance system to answer the problems that still exist in lecture attendance. In the construction of the presence system, the use of base64 encryption repetition is used for the presence of the Qr code. QR Code is used for security and reliability in code scrambling. The randomization was intended to limit students so that they could not make attendance if the class did not match. This is done to ensure that the student is present in class and the lecturer can anticipate student fraud in taking attendance. Therefore, in order to maintain security and avoid leakage and fraud in attendance, we need a system that can protect and maintain the security of a system. Base64 encryption repetition can be used to protect each student's string.
Android-Based Information And Internet Dictionary Applications Using The Horspool Algorithm

Currently, many technical term dictionaries that are currently circulating are still in book form, making it difficult for users, especially students, because they have to manually search for the meanings and terms of informatics and the internet. One of the algorithms commonly used in the searching process is the Horspool algorithm. Horspool algorithm is a text matching process. Text matching will run efficiently if you have a table to determine how long the character is shifted if an inconsistency is detected in the jth character of the pattern. So it is hoped that the search process can be made in the application of the informatics and internet term dictionary. By using the Horspool algorithm, word matching will be carried out from the beginning of the text or from left to right so that the number and position of the searched term will determine the duration of the search.
Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infants

Although machine learning (ML) models of AI achieve high performances in medicine, they are not free of errors. Empowering clinicians to identify incorrect model recommendations is crucial for engendering trust in medical AI. Explainable AI (XAI) aims to address this requirement by clarifying AI reasoning to support the end users. Several studies on biomedical imaging achieved promising results recently. Nevertheless, solutions for models using tabular data are not sufficient to meet the requirements of clinicians yet. This paper proposes a methodology to support clinicians in identifying failures of ML models trained with tabular data. We built our methodology on three main pillars: decomposing the feature set by leveraging clinical context latent space, assessing the clinical association of global explanations, and Latent Space Similarity (LSS) based local explanations. We demonstrated our methodology on ML-based recognition of preterm infant morbidities caused by infection. The risk of mortality, lifelong disability, and antibiotic resistance due to model failures was an open research question in this domain. We achieved to identify misclassification cases of two models with our approach. By contextualizing local explanations, our solution provides clinicians with actionable insights to support their autonomy for informed final decisions.
The Application of K-Means Clustering Method for Rubber Price Grouping in Sengkuang Jaya Village Bengkulu Province

Sengkuang Jaya village is an area located in Bengkulu province. The calculation of price for rubber is still calculated manually in the village. This study wants to apply the k-means clustering method for calculating rubber prices according to the quality of rubber owned by farmers in Sengkuang Jaya Village, Bengkulu Province. This study aimed to determine prices based on rubber quality in Sengkuang Jaya Village,Bengkulu Province. The tehnique of collecting the data of this study used interview and observation.The results showed that from 15 samples of rubber latex data taken,the price of rubber with the highest cluster was found for rubber 2 weeks and over and for rubber 2 weeks down including cluster 2.The results of testing the rapidminer tools run well without any errors. Base on the result of the study can be suggested that further research can be developed with the same type of data but using better methods such as using the Fuzzy C-Means algorithm, Hard C-Means and completing the sample which may be up to 1 year of rubber storage.
Semi-randomized block Kaczmarz methods with simple random sampling for large-scale linear systems

Randomized block Kaczmraz method plays an important role in solving large-scale linear system. One of the key points of this type of methods is how to effectively select working rows. However, in most of the state-of-the-art randomized block Kaczmarz-type methods, one has to scan all the rows of the coefficient matrix in advance for computing probabilities or paving, or to compute the residual vector of the linear system in each iteration to determine the working rows. Thus, we have to access all the rows of the data matrix in these methods, which are unfavorable for big-data problems. Moreover, to the best of our knowledge, how to efficiently choose working rows in randomized block Kaczmarz-type methods for multiple linear systems is still an open problem. In order to deal with these problems, we propose semi-randomized block Kaczmarz methods with simple random sampling for linear systems with single and multiple right-hand sides, respectively. In these methods, there is no need to scan or pave all the rows of the coefficient matrix, nor to compute probabilities and the residual vector of the linear system in each step. Specifically, one can update all the solutions of a large-scale linear system with multiple right-hand sides simultaneously. The convergence of the proposed methods is considered. Numerical experiments on both real-world and synthetic data sets show that the proposed methods are superior to many state-of-the-art randomized Kaczmarz-type methods for large-scale linear systems.
IoT Device Identification Based on Network Traffic Characteristics

IoT device identification plays an important role in monitoring and improving the performance and security of IoT devices. Compared to traditional non-IoT devices, IoT devices provide us with both unique challenges and opportunities in detecting the types of IoT devices. Based on critical insights obtained in our previous work on understanding the network traffic characteristics of IoT devices, in this paper we develop an effective machine-learning based IoT device identification scheme, named iotID. In developing iotID, we extract 70 features of TCP flows from three complementary aspects: remote network servers and port numbers, packet-level traffic characteristics such as packet inter-arrival times, and flow-level traffic characteristics such as flow duration. Different from existing work, we take into account the imbalance nature of network traffic generated by various devices in both the learning and evaluation phases of iotID. Our performance studies based on network traffic collected on a typical smart home environment consisting of both IoT and non-IoT devices show that iotID can achieve a balanced accuracy score of above 99%.
Exploiting Rich Textual User-Product Context for Improving Sentiment Analysis

User and product information associated with a review is useful for sentiment polarity prediction. Typical approaches incorporating such information focus on modeling users and products as implicitly learned representation vectors. Most do not exploit the potential of historical reviews, or those that currently do require unnecessary modifications to model architecture or do not make full use of user/product associations. The contribution of this work is twofold: i) a method to explicitly employ historical reviews belonging to the same user/product to initialize representations, and ii) efficient incorporation of textual associations between users and products via a user-product cross-context module. Experiments on IMDb, Yelp-2013 and Yelp-2014 benchmarks show that our approach substantially outperforms previous state-of-the-art. Since we employ BERT-base as the encoder, we additionally provide experiments in which our approach performs well with Span-BERT and Longformer. Furthermore, experiments where the reviews of each user/product in the training data are downsampled demonstrate the effectiveness of our approach under a low-resource setting.
Jalan Kito Application For Services, Suggestions And Complaints Of Traffic And Land Transfortation (LLAJ) Geographic Information System-Based Safety Facilities

Bengkulu Province Transportation Agency (Dishub) constituses one of the agencies engaged in public services, especially in the Traffic and Land Transportation (LLAJ) section to provide road safety facilities for the community. There are various kinds of facilities that have been provided at several location points, but the Bengkulu Province Transportation Agency (Dishub) does not immediately receive accurate information, resulting in delays in handling the damaged facilities. Therefore, complaints from the public are needed to find out the direct damage that is at the point of location of road safety facilities. Public complaints usually often occur in the field of public facilities and services which are handled by government agencies. The community's complaints have not been conveyed directly properly but only through social media intermediaries so that it makes people confused about who to complain to. So in this study, we will discuss the Jalan Kito application which is a service for advice and complaints of the Traffic and Land Transportation(LLAJ) safety facilities based on the Geographic Information System to help the public in reporting damage to road safety facilities. This study employed the Extreme Programming, Extreme Programming (XP) method, which is a software development method that is fast, efficient, low risk, flexible, predictable, scientific, and fun. This model tends to use an Object-Oriented approach. The stages that must be passed include Planning, Design, Coding, and Testing, For testing, it used the Black Box method. In testing with this Black Box method, the results obtained are that all menus, pages, and features that are made run according to what is expected
Hyperbolic Hierarchical Contrastive Hashing

Hierarchical semantic structures, naturally existing in real-world datasets, can assist in capturing the latent distribution of data to learn robust hash codes for retrieval systems. Although hierarchical semantic structures can be simply expressed by integrating semantically relevant data into a high-level taxon with coarser-grained semantics, the construction, embedding, and exploitation of the structures remain tricky for unsupervised hash learning. To tackle these problems, we propose a novel unsupervised hashing method named Hyperbolic Hierarchical Contrastive Hashing (HHCH). We propose to embed continuous hash codes into hyperbolic space for accurate semantic expression since embedding hierarchies in hyperbolic space generates less distortion than in hyper-sphere space and Euclidean space. In addition, we extend the K-Means algorithm to hyperbolic space and perform the proposed hierarchical hyperbolic K-Means algorithm to construct hierarchical semantic structures adaptively. To exploit the hierarchical semantic structures in hyperbolic space, we designed the hierarchical contrastive learning algorithm, including hierarchical instance-wise and hierarchical prototype-wise contrastive learning. Extensive experiments on four benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art unsupervised hashing methods. Codes will be released.
Complex SQL-NoSQL Query Translation for Data Lake Management

A data lake refers to an extremely large data resource or repository.Data lakes store large amounts of data and use advanced analytics to pair data from multiple sources with different types of structured, semistructured, and unstructured information.NoSQL databases such as Mongodb, Redis, Neo4j, and Cassandra are nontabular and they store data differently rather than use relational tables.NoSQL databases come in many forms, mostly documents, key values, wide columns, and graphs based on their data model.NoSQL gives less complicated scalability and higher overall performance as compared with traditional relational databases.NoSQL databases can store different types of data, but they cannot fully support Automation, Consistency, Isolation, and endurance (ACID) features, i.e., trigger functions in multi-transaction management, because a NoSQL database uses a non-relational database system.Thus, an interpreter is necessary for SQL-to-NoSQL queries.We used ANTLR (ANother Tool for Language Recognition), which has five main stages: The input SQL query, the tokenizer, the parser, the parser tree, and lastly the generation of the query results in NoSQL.The tool gave users to write a flexible multi-pass language parser that is expected to solve problems in querying complex ACID functions and other problems in complex queries in NoSQL databases.In the measurement, analysis, and evaluation of the translation results through the comparison of each NoSQL criterion against a Relational Database Management System (MySQL), the scores obtained were as follows.The performance criterion achieved the highest score (98.40%) by the MongoDB database, followed by scalability (97.40%) and accuracy (97.00%).The criterion with the lowest score was complexity (91.65%).
Classroom Methods for Enhancing Equivalence Retrieval for Translators

The problem of equivalence retrieval is one of the most challenging for trainee translators and interpreters. Numerous techniques have been proposed by translator trainers and theorists to help overcome memory-related issues (Gile 1995, Al-Hammadi 2012, Lörscher 2012, etc.). This paper proposed two teaching methods to be adopted by translator trainers with the aim of enhancing trainee proficiency, specifically concerning equivalence retrieval. The present article generally belongs to the cognitive paradigm. The proposed teaching methods, namely semantic breakdown and teaching-common-senses-first, take ‘meaning’ as their focus, basically drawing on ideas from schema theory, and relevance theory. A future empirical investigation of the two proposed teaching methods would help validate them.
Android-Bases Menu Selection Application At Café De One In Giri Kencana Village, North Bengkulu

In the era of the rapid development of telecommunications, there are many types mobile phones whicht are smart phones on the market. One example that is being widely known in the mobile phone market, many smartphones use Android platform. There are many advantages of android. A café also possible to take advantage of android technology both for customer satisfaction and the benefit of the cafe owner. This study uses the UCD method which conducts interviews with cafe owners or research sites so that they can get results on how the needs, designs and feasibility are as well as tests on the menu selection application. From the results of the research that has been done, it can be concluded that the application is in accordance with the wishes of costumers, and by using the blackbox method it is also in accordance with the wishes of the cafe owner.
Analysis of Eevee Engine Rendering Engineering in Making 3D Animation Videos Mukomuko Hospital

Blender is a free and open source 3D computer software, Blender is used to create animated films, visual effects, and video games. This software features 3D modeling, UV unwrapping, texturing, ringing and skinning, fluid and smoke simulation, particle simulation, animating, match moving, camera tracking, rendering, video editing, compositing and a built-in game engine. Where we can design and create 3D animated films not only as a medium of entertainment in cinema, but also can be used in various fields including visual socialization media, which can bring new colors in the delivery of information. The formulation of this research is to determine how long the render time is generated to perform the rendering process using the Eevee rendering engine in Blender Software with a video duration of 54 seconds and a resolution of 320, 720, 1080 pixels on the 3D animation video of Mukomuko Hospital and the quality of the graphics/visuals produced by Eevee rendering engine. The animation video used is the 3D animation “Mukomuko Hospital”. The process of making animation using Blender 3D application and Adobe Premiere CC animation video design. The software used is Blender 2.82. The duration of the video analyzed using Eevee Engine Render is 54 seconds. The image resolution analyzed using the Eevee Engine is 320, 720 and 1080 pixels. The conclusion of this research is in the pre, production and post production process of 3D animation video at RSUD Mukomuko in accordance with the planning made previously. The rendering time generated to perform the rendering process using the Eevee rendering engine in Blender Software with a video duration of 54 seconds and a resolution of 320, 720, 1080 pixels, obtained various results with their respective advantages and disadvantages.
High Accurate Multicriteria Cluster-Based Collaborative Filtering Recommender System

A Recommender System (RS) is one of the appropriate answers by researchers for alleviating the problem of customers' overload with information from an internet source.In RS, users' evaluation of the items assists in quickly determining the user's preferences and removing the choosing overhead for the user the next time they search.However, using user ratings for an item based on multiple aspects has given researchers little attention to recommending an object accurately.This research proposes a model-based collaborative filtering movie recommendation system based on user ratings across various criteria.The proposed systems' model was established using clustering and classification methods, such as k-means, kmodes, and Multinomial logistic regression.The proposed work consists of two different steps; first, clustering mode; after clustering the dataset with kmeans, k-modes were used to re-cluster it into many sub-clusters for search domain reduction purposes.The second is classification mode; the Multinomial Logistic Regression (MLR) model was created to predict the closest cluster class to newly active users.Since the MLR is one of the probabilistic models, it is more accurate than cluster methods in the prediction process.The proposed approach uses distance indicators, such as modified Mahalanobis distance and Euclidean distance, to measure the similarity between new active and in-group users.The evaluation of the proposed methodology was done using the MAE and Silhouette scores.Different values of Silhouette score were achieved in this study for a different number of features and clusters, with the best k-means score being 0.822.Based on multicriteria Yahoo!! Movie Dataset, the MAE result indicates that this study is better than the existing one.
Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark

In recent years, vision-centric perception has flourished in various autonomous driving tasks, including 3D detection, semantic map construction, motion forecasting, and depth estimation. Nevertheless, the latency of vision-centric approaches is too high for practical deployment (e.g., most camera-based 3D detectors have a runtime greater than 300ms). To bridge the gap between ideal research and real-world applications, it is necessary to quantify the trade-off between performance and efficiency. Traditionally, autonomous-driving perception benchmarks perform the offline evaluation, neglecting the inference time delay. To mitigate the problem, we propose the Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving. On the basis of the 2Hz annotated nuScenes dataset, we first propose an annotation-extending pipeline to generate high-frame-rate labels for the 12Hz raw images. Referring to the practical deployment, the Streaming Perception Under constRained-computation (SPUR) evaluation protocol is further constructed, where the 12Hz inputs are utilized for streaming evaluation under the constraints of different computational resources. In the ASAP benchmark, comprehensive experiment results reveal that the model rank alters under different constraints, suggesting that the model latency and computation budget should be considered as design choices to optimize the practical deployment. To facilitate further research, we establish baselines for camera-based streaming 3D detection, which consistently enhance the streaming performance across various hardware. ASAP project page: https://github.com/JeffWang987/ASAP.
3D Visualization as A Web-Based Promotional Media (Case Study: Faculty of Engineering, University of Muhammadiyah Bengkulu)

Promotion is an activity that is commonly carried out by various universities, especially when they are about to enter a new school year to attract prospective new students. The Faculty of Engineering is one of the faculties at the University of Muhammadiyah Bengkulu. The Faculty of Engineering has a variety of promotional media, one of which is a website with the address at ft.umb.ac.id. The official website of the Faculty of Engineering provides information about achievements that have been achieved, important activities carried out by students and lecturers, profiles of the Faculty of Engineering and each study program in it, and links to related website pages. However, the website does not provide information about what is in the Faculty of Engineering. This makes website users not know how the atmosphere is when they are in the Faculty of Engineering. The research method used in the study was research and development (R&amp;D) method. This research produces a website page that contains a 3D visualization of the Faculty of Engineering building and rooms and objects in it that can be used as additional promotional media options for the Faculty of Engineering, University of Muhammadiyah Bengkulu.