Reviewerly/exHarmony huggingface dataset:
Improving Students’ Writing Ability of Descriptive Text Through Running Dictation Technique at The Eighth Grade of UPTD SMP Negeri 4 Gunungsitoli in 2022/2023

Writing is a important skill that needs to be mastered to develop their English in written media to convey their ideas. Writing is the process of expressing ideas thinking feelings in words on a piece of paper. In writing, students can represent thoughts or opinions in the form of written language. The aim of the research is to Improve Students' Writing Ability of Descriptive Text Through Running Dictation Technique at the Eighth Grade of UPTD SMP Negeri 4 Gunungsitoli, which the total numbers of the students was 29 persons. The research was conducted by using Classroom Action Research (CAR) design. This research was applied into two cycles. Each cycle consisted of planning, action, observation, and reflection. The instruments used by the researcher to collect the data were observation sheet, evaluation sheet and field note. From the result of each Cycle, the researcher found that the students got an improvement in their writing through Running Dictation. In the second meeting of cycle II, the researcher obtained the improvement of students, who achieved the Minimum Competence Criterion (70).There were 10 (35%) students who got score among 85-100 classified as “Very Good Criteria”, 13 (44%) students got score among 75-84 classified as “Good Criteria”, 6 (21 %) students who got score among 60-74 classified as “Enough Criteria”, no one students who reached the “Less Criteria” and no one students who reached the “Fail ”Criteria. In Cycle II the highest mark was 92 and the lowest was 71 . Based on the result above the researcher concluded that Running Dictation technique could be done in teaching writing and could improve the students’ writing ability.
An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning

Federated learning is a novel decentralized learning architecture. During the training process, the client and server must continuously upload and receive model parameters, which consumes a lot of network transmission resources. Some methods use clustering to find more representative customers, select only a part of them for training, and at the same time ensure the accuracy of training. However, in federated learning, it is not trivial to know what the number of clusters can bring the best training result. Therefore, we propose to dynamically adjust the number of clusters to find the most ideal grouping results. It may reduce the number of users participating in the training to achieve the effect of reducing communication costs without affecting the model performance. We verify its experimental results on the non-IID handwritten digit recognition dataset and reduce the cost of communication and transmission by almost 50% compared with traditional federated learning without affecting the accuracy of the model.
High Performance Real-time Anomaly Detection for Agricultural Monitoring Systems

Due to technological advancements, real-time monitoring of various agricultural parameters has become essential to modern farming practices. Monitoring agricultural parameters such as temperature, humidity, soil moisture, and plant growth helps farmers to optimize their farming practices to improve crop yields. However, near-real-time monitoring involves significant amounts of data, and analyzing this data in real time for anomalies is a challenging problem. In this paper, we propose a novel approach for anomaly detection in near real-time agriculture monitoring using a cortical coding method to address this issue. The cortical coding method utilizes a tree structure to cluster and learn patterns from time-series data. The proposed method has previously proven helpful in tasks such as vector quantization and anomaly detection. This paper explores the application of cortical coding-based anomaly detection in agriculture monitoring. Incremental and continuous learning is highly beneficial for early and accurate detection in systems with large data streams. The proposed method is evaluated on a dataset obtained from an agricultural monitoring station in Turkey. Experiments show that the proposed method outperforms widely used forecast-based anomaly detection methods.
eDashA: Edge-based Dash Cam Video Analytics

While the real-time analysis of dash cam video is of great practical importance for improving road safety, commercial dash cams lack the resources necessary to perform such video analytics. It is impractical to use clouds for this due to high latency and high bandwidth consumption. In this paper, we present eDashA, the first edge-based system that demonstrates the potential of near real-time video analytics using a network of mobile devices, on the move. In particular, it simultaneously processes videos produced by two dash cams of different angles (outward facing and inward facing dash cams) with one or more mobile devices on the move. Further, we devise several optimization techniques and incorporated them into eDashA. These techniques are simultaneous download and analysis, scheduling, segmentation and early stopping. We have implemented eDashA as an Android app and evaluated it using two dash cams and several heterogeneous smartphones. Experiment results show the feasibility of real-time video analytics on the move.
Secure and Cost Effective IoT Authentication and Data Storage Framework using Blockchain NFT

The scale and scope of using IoT has had a rapid growth over the past few years. This growth has raised security and cost challenges of using IoT smart devices in large scale. In this research, we propose a comprehensive framework that addresses these challenges using cryptographic systems and blockchain NFTs. We analyze the credibility and scalability of our framework. In our proposed framework, we securely authenticate IoT devices and bind them to blockchain Non-Fungible Tokens (NFTs). To uniquely bind each IoT device to an NFT, we use the device's physical unclonable function (PUF). The link between the NFT and the device is difficult to break and can be traced anytime. Our framework also authenticates the device's acquired data using the device's PUF and securely stores the authenticated data on the blockchain. We use an approach that significantly reduces the blockchain costs and analyze it in large scale. Our analysis show that our framework is a secure and cost-effective solution for large-scale IoT authentication and data storage.
Multi-topology hierarchical collaborative hybrid particle swarm optimization algorithm for WSN

Wireless sensor networks (WSN) are widely used in many situations, but the disordered and random deployment mode will waste a lot of sensor resources. This paper proposes a multi-topology hierarchical collaborative particle swarm optimization (MHCHPSO) to optimize sensor deployment location and improve the coverage of WSN. MHCHPSO divides the population into three types topology: diversity topology for global exploration, fast convergence topology for local development, and collaboration topology for exploration and development. All topologies are optimized in parallel to overcome the precocious convergence of PSO. This paper compares with various heuristic algorithms at CEC 2013, CEC 2015, and CEC 2017. The experimental results show that MHCHPSO outperforms the comparison algorithms. In addition, MHCHPSO is applied to the WSN localization optimization, and the experimental results confirm the optimization ability of MHCHPSO in practical engineering problems.
Bayesan Model to Predict Resection Margin Status after Neoadjuvant Therapy in Pancreatic Adenocarcinoma

Background: Proportion of patients undergoing to surgery for pancreatic ductal adenocarcinoma (PDAC) after neoadjuvant treatment (NAT) has largely increased. R0 resection remains a strong prognostic factor for survival (OS), but conventional radiology is still not reliable to accurately determine response to NAT. Therefore, a preoperative predicting model is needed to identify patients with a higher chance to undergo R0 resection. We aimed to build a model to predict resection margin status after NAT using only patients' preoperative characteristics previously shown to be associated with resection margin. Methods: Patients undergoing surgery for PDAC after NAT from 2010 to 2020 were retrospectively analysed from the prospective collected database of Pancreatic Surgery of Pederzoli Hospital. A Bayesian Logistic regression model was estimated for predicting the R1 status. The Bayesian approach was chosen because it allowed to incorporate our data about R status predictors with prior data extracted from the previous literature. Model performance was evaluated calculating the Area Under the Curve (AUC) on the average posterior probability of R1. Results: Overall, 205 patients underwent curative-intent resection following NAT and were included in the analysis. Median age was 64 (56-70), 100 (48.8%) were female and 126 (66.3%) received either FOLFIRINOX or gemcitabine+nab-paclitaxel. Radiotherapy was administered to 52(25.4%) patients. Pancreaticoduodenectomy was performed in 149(72.6%) while distal pancreatectomy and total pancreatectomy in 49(23.6%) and 7(4.9%) respectively. R0 resection was achieved in 150(73.2%) of cases. In the final predictive model, age at surgery, location on pancreatic neck, arterial contact and preoperative tumor size, were directly associated with R1 status and ASA score and serum CA19-9 normalization were extracted from literature data (Figure 1). The model performance showed an AUC of 72%. Conclusion: We provided a valid model to predict margin status after NAT in order to identify patients with higher chances for radical surgery. Since literature about margin resection in PDAC is growing, the Bayesian approach's added value is to take into account previous knowledge in the field.
A Whole New Information World

This introductory article explores the interconnectedness of the articles in this issue through the lens of artificial intelligence (AI), bots, and other technologies. The articles presented in this issue strive to demonstrate how the library and information science (LIS) field uses AI to interrogate social conflicts, critically question our professional knowledge base, engage in localized community knowledge building, and create interactive maps to preserve cultural knowledge and decentralize Western metadata values in non-Western contexts. This introductory article is presented as a readerly and writerly response to those articles because, as an experiment, I have co-authored this piece using Google’s Bard, a recently released AI chatbot. Google’s Bard is a powerful tool that generates text, translates languages, writes creative content, and answers questions. In this editorial, I share my own experience using Bard to identify the implications of AI as a co-author of the text. I discuss its advantages (if any) and disadvantages, and I outline how AI could impact the future of the LIS field.
SISTEM INFORMASI KANTOR CAMAT VII KOTO BERBASIS WEB

Service is the provision (serving) of the needs of individuals or communities who have an interest in the organization in accordance with the basic rules and procedures specified. Good service will ultimately be able to provide satisfaction to the community. The public can directly assess the performance based on the services received. One of the problems that need attention at the Koto VII Sub-District Head Office is Community Service, which is currently still using the manual method of recording through the Service agenda book. This method will make the working method ineffective because one of the obstacles faced is when Community Service can extend the time long enough because it is still manual. This system is designed using DFD (Data Flow Diagram). The sources of research data are secondary data obtained from studies of several journals, articles, government regulations, company data, internet, and others. Which is considered relevant to the research topic. With the system application that was built, it can help, simplify, and speed up the process of Community Service to the community being more well-documented and processing the required data faster.
Implicit Discourse Relation Recognition by Scoring Special Tokens

Implicit discourse relation recognition is one of the most difficult tasks in natural language understanding. Because of the lack of explicit connectives, the ability to extract logical information between the two discourse arguments is highly required. Previous studies mainly focus on designing complex neural network layers and various kinds of interactions between the two arguments, without further exploring the logical semantics in the pre-trained language models. We propose a novel method that utilizes the power of the pre-trained language model (RoBERTa) by introducing different kinds of extra special tokens, which can represent the relations between the discourse arguments respectively. On one hand, these special tokens learn to aggregate category features that profit classification; on the other hand, they can also be regarded as new “words” that the prediction of them inspires the pre-train language model. To effectively learn these special tokens, a scorer is then trained to give the discourse connected by corresponding special tokens higher scores than those with other special tokens. The experiments show the result of our approach exceeds our baseline by 4.24% F1, and the state-of-the-art model by approximately 2.37% F1 on the four-way classification of the PDTB 2.0 dataset.
IMPLEMENTASI TANDA TANGAN ELEKTRONIK PADA WEBSITE SISTEM INFORMASI KEPEGAWAIAN (SIMPEG) KABUPATEN SAROLANGUN

Sarolangun Regency through its agency, namely Badan Kepegawaian dan Sumber Daya Manusia (BKPSDM), has a website called the Sistem Informasi Kepegawaian (SIMPEG) which aims to provide public services such as filing for leave and filing periodic salaries for employees within the Sarolangun Regency government. Currently the submission process has been made through the system but has not been able to validate documents electronically. Therefore, a mechanism is needed to validate the document using an electronic signature. This electronic signature is a solution and has been provided by a Government agency called the Balai Sertifikasi Elektronik (BSrE). The research method used in this research is the waterfall method. In this research, a system design and its implementation will be made so that the SIMPEG website can process the electronic signature of the BSrE.
An Integrated Virtual Surgical Planning System for Nasal Airway Obstruction

Nasal airway obstruction (NAO) is a condition where nasal passages are blocked, preventing normal airflow through the nose. NAO is one of the most common conditions treated by otolaryngologists using surgical procedures. However, surgical procedures have a high failure rate and surgery planning has the potential to improve surgical outcomes. However, current surgical planning systems are challenging to use, are not interactive, and don't consider physiological parameters. This paper reports the development of nSurgSim, an integrated virtual surgical planning application that incorporates model generation, computational fluid dynamics feedback, and patient-outcome analysis. We conducted a usability study with twenty-one board-certified ENT surgeons to evaluate the ease of use, perceived usefulness, and potential for clinical adoption. Overall, the study result shows nSurgSim will be a valuable tool in the ENT surgeon's arsenal for complex cases and a powerful tool for a general understanding of procedures and improvement in procedure outcomes.
THE USAGE OF STORYTELLING METHOD TO IMPROVE THE STUDENTS' SPEAKING SKILLS

Educators need a lot of creativities to make the students interested in learning, especially English which often makes students think it’s difficult subject to learn. Storytelling is one the interesting methods to apply in teaching and learning process. This study aims to make students more active and courageous in expressing themselves in learning English and is also an attempt to determine of effect of using storytelling methods. This research was conducted in class X SMKN 1 Bongas Indramayu with population of 353 students and took sample of 36 students. The writers used the following types of tests: 1). Pre-test. 2). Treatment and 3). Post-test. This research is to answer questions; 1). How is the speaking ability of the students? 2). How is the fluency of students’ speaking skills? And 3) How is the use of the storytelling method in improving students’ speaking skills at SMKN 1 Bongas Indramayu? This study uses quantitative method. The population in this study was 353 and a sample of 36 students was taken from class 10. From this research, there are good results of using storytelling method in improving students’ speaking skills
Data Integrity Verification in Mobile Edge Computing With Multi-Vendor and Multi-Server

The emerging <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> obile <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> dge <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> omputing (MEC) paradigm reforms the way of data caching by motivating App vendors to store latency-sensitive data on distributed edge servers. In volatile MEC environments, ensuring <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> dge <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ata <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> ntegrity (EDI) is a major concern for App vendors. Existing EDI solutions only consider the scenario with a single App vendor and multiple edge servers, neglecting more complex multi-vendor and multi-server cases. If multiple App vendors check their data replicas cached on the same edge server simultaneously, integrity verification efficiency will drop exponentially. To mitigate this challenge, we make the first attempt to develop a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> mart <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> nspection <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> lgorithm (SIA) to pre-select unreliable data replicas for different App vendors in each verification round by jointly considering cache services' QoS (Quality-of-Service) and data replicas' unverified time. By implementing this approach, edge servers can merely verify the selected data replicas, greatly reducing computation and communication overheads in EDI verification. Theoretically, SIA can achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(n)$</tex-math></inline-formula> expected time complexity. Supported by SIA, we expand the EDI problem in multi-vendor and multi-server MEC environments (referred to as the MVMS-EDI problem) and propose a smart contract-based approach entitled MVMS-SC to tackle the problem efficiently and impartially. We provide a rigorous theoretical analysis of the correctness, security, and efficiency of MVMS-SC. Both large-scale and small-scale experiments with real-world datasets are correspondingly performed on a single machine and a real platform to validate the superiority of MVMS-SC in terms of computation and communication efficiencies.
Illuminating Unexplored Corners in Healthcare Space Using Ambience Intelligence

The term “Ambient Intelligence” describes physical environments that are perceptive to and responsive to human presence. It has been made possible by developments in machine learning and contactable sensors. In this paper, we will discuss about how AmI technology might assist people with various physical, mental or chronic illnesses. We examine how this technology could enhance our comprehension of the hidden areas of healthcare. Early implementations in hospitals may soon allow for more effective clinical processes and raise patient safety in operating rooms and intensive care units. We focus on the four key facets of reliability, that is, privacy, fairness, transparency and research ethics. We will also concentrate on a few technological problems and potential solutions associated with acquiring knowledge from a huge cluster of data and detecting unexpected events in clinical settings. By carefully utilizing this technology, we would be able to comprehend the intricate interactions between the crucial human behaviours and physical environment.
Choose Your Colour: Tree Interpolation for Quantified Formulas in SMT

Abstract We present a generic tree-interpolation algorithm in the SMT context with quantifiers. The algorithm takes a proof of unsatisfiability using resolution and quantifier instantiation and computes interpolants (which may contain quantifiers). Arbitrary SMT theories are supported, as long as each theory itself supports tree interpolation for its lemmas. In particular, we show this for the theory combination of equality with uninterpreted functions and linear arithmetic. The interpolants can be tweaked by virtually assigning each literal in the proof to interpolation partitions (colouring the literals) in arbitrary ways. The algorithm is implemented in SMTInterpol.
OpenCitations selected for SCOSS second funding cycle

The Global Sustainability Coalition for Open Science Services (SCOSS) is launching its second funding cycle, and OpenCitations is one of three open science infrastructure organizations whose services have been evaluated and selected for presentation to the international scholarly community for crowd-sourced sustainability funding, along with the Public Knowledge Project (PKP) and the Directory of Open Access Books (DOAB). OpenCitations is an in
Using the ORCID Public API for author disambiguation in the OpenCitations Corpus

Among the external services used, the&lt;strong&gt; ORCID Public API &lt;/strong&gt;is of crucial importance for the task of author disambiguation. During the OCC ingestion workflow, the main metadata of an article are usually retrieved from the Crossref API.
SISTEM INFORMASI PENJUALAN MAKANAN RINGAN (SIPMAR) BERBASIS WEB PADA R &amp; R DENGAN PHP DAN MYSQL

The research aims to build a sales information system on the R&amp;R based on the web. The system was developed using the PHP programming language using MySQL as a database. The method of data collection is done by way of interviews, observation, and coupled with a study of the library which has a connection with the problems faced. While the research method used is the descriptive qualitative research with the Waterfall model development system. From research conducted, found problems such as; 1) Manual recording is not neat, 2) not regularly structured sales transactions, and 3) Often lost transaction notes , resulting in difficulties in recording transactions and also errors in recording the end of month reports. The effort to solve this problem is by creating a web-based sales information system using MySQL