Reviewerly/exHarmony huggingface dataset:
OPTIMIZING SIMILARITY THRESHOLD FOR ABSTRACT SIMILARITY METRIC IN SPEECH DIARIZATION SYSTEMS: A MATHEMATICAL FORMULATION

Speaker diarization is a critical task in speech processing that aims to identify "who spoke when?" in an audio or video recording that contains unknown amounts of speech from unknown speakers and unknown number of speakers. Diarization has numerous applications in speech recognition, speaker identification, and automatic captioning. Supervised and unsupervised algorithms are used to address speaker diarization problems, but providing exhaustive labeling for the training dataset can become costly in supervised learning, while accuracy can be compromised when using unsupervised approaches. This paper presents a novel approach to speaker diarization, which defines loosely labeled data and employs x-vector embedding and a formalized approach for threshold searching with a given abstract similarity metric to cluster temporal segments into unique user segments. The proposed algorithm uses concepts of graph theory, matrix algebra, and genetic algorithm to formulate and solve the optimization problem. Additionally, the algorithm is applied to English, Spanish, and Chinese audios, and the performance is evaluated using wellknown similarity metrics. The results demonstrate that the robustness of the proposed approach. The findings of this research have significant implications for speech processing, speaker identification including those with tonal differences. The proposed method offers a practical and efficient solution for speaker diarization in real-world scenarios where there are labeling time and cost constraints
Enhancing Mapless Trajectory Prediction through Knowledge Distillation

Scene information plays a crucial role in trajectory forecasting systems for autonomous driving by providing semantic clues and constraints on potential future paths of traffic agents. Prevalent trajectory prediction techniques often take high-definition maps (HD maps) as part of the inputs to provide scene knowledge. Although HD maps offer accurate road information, they may suffer from the high cost of annotation or restrictions of law that limits their widespread use. Therefore, those methods are still expected to generate reliable prediction results in mapless scenarios. In this paper, we tackle the problem of improving the consistency of multi-modal prediction trajectories and the real road topology when map information is unavailable during the test phase. Specifically, we achieve this by training a map-based prediction teacher network on the annotated samples and transferring the knowledge to a student mapless prediction network using a two-fold knowledge distillation framework. Our solution is generalizable for common trajectory prediction networks and does not bring extra computation burden. Experimental results show that our method stably improves prediction performance in mapless mode on many widely used state-of-the-art trajectory prediction baselines, compensating for the gaps caused by the absence of HD maps. Qualitative visualization results demonstrate that our approach helps infer unseen map information.
Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension

The traditional way of sentence-level event detection involves two important subtasks: trigger identification and trigger classifications, where the identified event trigger words are used to classify event types from sentences. However, trigger classification highly depends on abundant annotated trigger words and the accuracy of trigger identification. In a real scenario, annotating trigger words is time-consuming and laborious. For this reason, we propose a trigger-free event detection model, which transforms event detection into a two-tower model based on machine reading comprehension and prompt learning. Compared to existing trigger-based and trigger-free methods, experimental studies on two event detection benchmark datasets (ACE2005 and MAVEN) have shown that the proposed approach can achieve competitive performance.
CROSS-FREQUENCY DOMAIN FOR JPEG INVERSIBLE WATERMARKING USING MULTIPLE QUANTIZATION TABLES

Reversible data hiding techniques for JPEG images have big challenges in order to improve the capacity, quality, and flexible embedding method. In this article, we propose a new crossdomain using combination of multiple quantization tables in JPEG algorithm. We survey the efficiency of quantization coefficients on the DCT tables in order to decide the way to embed the watermark information. Compared to the conventional method, our experimental results show that our proposed method has better performance in term of both the increasing capacity and improving the quality of embedded JPEG images after data embedding.
Learning Background-Suppressed Dual-Regression Correlation Filters for Visual Tracking

The discriminative correlation filter (DCF)-based tracking method has shown good accuracy and efficiency in visual tracking. However, the periodic assumption of sample space causes unwanted boundary effects, restricting the tracker’s ability to distinguish between the target and background. Additionally, in the real tracking environment, interference factors such as occlusion, background clutter, and illumination changes cause response aberration and, thus, tracking failure. To address these issues, this work proposed a novel tracking method named the background-suppressed dual-regression correlation filter (BSDCF) for visual tracking. First, we utilize the background-suppressed function to crop out the target features from the global features. In the training step, while introducing the spatial regularity constraint and background response suppression regularization, we construct a dual regression structure to train the target and global filters separately. The aim is to exploit the difference between the output response maps for mutual constraint to highlight the target and suppress the background interference. Furthermore, in the detection step, the global response can be enhanced by a weighted fusion of the target response to further improve the tracking performance in complex scenes. Finally, extensive experiments are conducted on three public benchmarks (including OTB100, TC128, and UAVDT), and the experimental results indicate that the proposed BSDCF tracker achieves tracking performance comparable to many state-of-the-art (SOTA) trackers in a variety of complex situations.
ReqGo: A Semi-Automated Requirements Management Tool

This study deals with issues of changes in requirements management by dealing with requirements ambiguity and prioritization.A hypothesis about the possibility of integrating machine learning techniques and requirements management processes has been proven.It highlights the efforts in automating requirements ambiguity identification, requirements classification, and prioritization considering multi-criteria in decision-making through the utilization of Natural Language Processing (NLP) techniques and Universal Sentence Encoder.Naïve Bayes (NB) classifier has been applied with its remarkable performance on binarily classifying requirements.Although existing methods proved to improve one or two of the process significantly, it rarely integrates the whole requirements management activity.The proposed tool helps the development team to manage the requirements systematically.The prioritization algorithm is proved to work as expected by considering multiple constraints before calculating the priority value.Meanwhile, it identifies the ambiguity that exists in the requirement automatically.The ambiguity classifier successfully identifies 87.5% of requirements accurately.Possible future work could be done in improving the prioritization module by allowing automated estimation of priority value upon requirements change.Future work may extend the automation coverage by providing test case generation.
PEMANFAATAN MEDIA DAN TEKNOLOGI DI LINGKUNGAN BELAJAR ABAD 21

The application and adaptation of technology in the learning space has become a necessity to deal with changes in the globalization era. The development of information and communication technology has had a significant impact on the world of education, especially in the learning process. Innovation and creativity in learning have arisen thanks to technological developments. One of the learning concepts that has emerged is the use of digital media and the web and the internet to provide interactive and flexible learning experiences and assessments.Keywords: Technology, Learning, Assessment.
A PUBLIC KEY ENCRYPTION - AUTHENTICATION SCHEME BASED ON ELGAMAL CRYPTOGRAPHIC ALGORITHM

The article proposes a public-key cryptographic scheme based on the ElGamal algorithm. This scheme can perform security functions and authenticate the origin and integrity of the encrypted message simultaneously. Moreover, the proposed scheme is also used as the basis for developing an algorithm to establish a shared secret key for symmetric key cryptosystems.
THE EIGHTH GRADE STUDENTS’ ERRORS OF SMP NEGERI 6 LHOKSUKON ACEH UTARA IN WRITING NARRATIVE TEXT

The aims of the study is to find out the kinds of errors are made by the seventh grade students of SMP Negeri 6 Lhoksukon Aceh Utara in writing narrative text and the most dominant error made by the seventh grade students of SMP Negeri 6 Lhoksukon Aceh Utara in writing narrative text. The researcher applied descriptive research. The researcher describes students’ errors in writing recount text. Therefore this research belongs to qualitative research because only describes the students’ errors in writing narrative text. The population of the research is the whole students of the eighth grade in academic year of 2021/2022. The total amount of population is around 120 students. Meanwhile the sample of this research is the students who are taken from class VIII1 and VIII2and the total sample is 60. Based on the finding in this research, there are 150 errors made by the students of SMP Negeri 6 Lhoksukon in writing narrative text. The errors are classified as follows: (1) error content which consists of (a) orientation = 17 errors, (b) events = 26 errors, (c) reorientations = 19 errors, and the total errors are 62; (2) errors in vocabulary = 15 errors, (3) errors in grammar = 51 errors, and, (4) errors in mechanics = 20 errors. Errors in content of narrative text are 62 items 42 %. Errors in vocabulary are 15 items or 10%. The occurrence of errors in grammar are 51 or 34%. Meanwhile, errors in mechanic are 20 is or . are 14%.Key Words: Students’ Errors, Writing Narrative Text
Uncover rigorous application range of phenomena measure method to avoid adding extra unverifiable things into reality for explaining measured discrepancy

Today, the scientific community comprehensively accepts adding many extra unverifiable things into reality, e.g. extra mass: dark matter, extra energy: dark energy, extra position: superposition, extra dimensions, or even extra parallel reality. Considering reality is not understood completely, adding something into reality's unknown part is indeed a shortcut for explaining some measured discrepancy, but such behaviors are likely to distort the instinct of reality. We report any inherent measured discrepancy involving an indirect measure method that relies on an artificially-defined equivalence, e.g. inertia mass indirectly measures mass by equating mass with force/acceleration. Time measure relies on an equivalence between time and some phenomena, e.g. swing of pendulum, fall of sands or electron jumping between two states. Since Galileo, mathematical equal sign is introduced so naturally between different physical properties. We argue any such equivalence only holds true within a limited phenomena range. Although some method's application range has been summarized, e.g. inertia mass only applies to 'macro, low-speed, inertia-system', we state such scattered descriptions collected from experience are not rigorous. We propose a general frame that can identify a more rigorous application range. Hence, any inherent measured discrepancy results from the measured phenomena exceeding the method's rigorous application range, e.g. remote objects actually exceed the Doppler effect's application range. By ignoring this, large-scale red shift inevitably misleads us to the dark energy's existing necessity. Similarly, dark matter only logically exists to expand the rigorous application range of dynamical mass.
Post-Pandemic Implementation of the School Literacy Movement (GLS) in Sukoharjo 4 Public Madrasah Ibtidaiyah

Since 2019, the corona outbreak has spread so that learning is online. The tasks the teacher gives do not fully help children learn but burden children and parents, hampering children's literacy habituation. This research is deemed necessary to determine the School Literacy Movement (GLS) implementation and the inhibiting and supporting factors of literacy. This research uses a descriptive qualitative method. The location in this study is MIN 4 Sukoharjo. Data collection techniques are interviews, documentation and observation. The research instruments included field note sheets, documentation, and interview sheets. The subjects of this study were deputy heads in the field of R&amp;D, homeroom teachers, and grade 1 and grade 4 students. The results showed that implementing the School Literacy Movement at MIN 4 Sukoharjo was not optimal due to changes in offline-online-offline learning returning after the pandemic. Factors inhibiting the implementation of GLS at MIN 4 Sukoharjo are limited reading materials, some classes do not have reading corners, library transfers, parental economics and parenting. Factors supporting the implementation of GLS include: the availability of facilities, excellent programs, learning that is designed with various methods, and teachers can build good communication with parents.
An Ensemble Learning Interpretation of Geometric Semantic Genetic Programming

Abstract Geometric semantic genetic programming (GSGP) is a variant of genetic programming (GP) that directly searches the semantic space of programs to produce candidate solutions. GSGP has shown considerable success in improving the performance of GP in terms of program correctness, however this comes at the expense of exponential program growth. Subsequent attempts to address this growth have not fully-exploited the fact that GSGP searches by producing linear combinations of existing solutions. This paper examines this property of GSGP and frames the method as an ensemble learning defining mutation and crossover as examples of boosting and stacking, respectively. The ensemble interpretation allows for simple integration of regularisation techniques that significantly reduce the size of the resultant programs. Additionally, this paper examines the quality of parse tree base learners within this ensemble interpretation of GSGP and suggests that future research could substantially improve the quality of GSGP by examining more effective initialisation techniques. The resulting ensemble learning interpretation leads to variants of GSGP that substantially improve upon the performance of traditional GSGP in regression contexts, and produce a method that frequently outperforms gradient boosting.
Pemodelan Machine Learning untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN)

This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.
How do linguistic illusions arise? Rational inference and good-enough processing as competing latent processes within individuals

Non-literal interpretations of implausible sentences such as "The mother gave the candle the daughter" have been taken as evidence for a rational error-correction mechanism that reconstructs the intended utterance from the ill-formed input ("... gave the daughter the candle"). However, the good-enough processing framework offers an alternative explanation: readers sometimes miss problematic aspects of sentences because they are only processing them superficially, which leads to acceptability illusions. As a synthesis of these accounts, I propose that rational inferences about errors on the one hand and good-enough processing on the other are competing latent processes that simultaneously occur within the same comprehender. In support of this view, I present data from a two-dimensional grammaticality/ interpretability judgment task with different types of subtly ill-formed sentences. Both rational inference and good-enough processing predict positive interpretability judgments for such sentences, but only good-enough processing also predicts positive grammaticality judgments. By fitting a lognormal race model jointly to judgments and response latencies, I show that rational inference and good-enough processing actively trade off with each other during reading. Furthermore, individual differences measures reveal that participant traits such as linguistic pedantry, interpretational charity, and analytic/intuitive cognitive styles contribute to variability in the processing patterns.
Effect of Interpersonal Communication and Learning Facilities on Learning Motivation in Elementary Schools, Sorong Regency

This study aims to determine whether there is an influence from parents' interpersonal communication and learning facilities at home on students' learning motivation in elementary schools in Sorong Regency. This type of research is a quantitative correlational design. Using multiple linear regression statistical techniques with a significance level of 5%. Data was collected in class V at SD Inpres 15, Sorong Regency, with 53 students. The results showed a positive and significant influence between parents' interpersonal communication and learning facilities at home on students' learning motivation in elementary schools. This result is evidenced by the analysis results, namely the Sig value of 0.000 &lt;0.05, which means the hypothesis is accepted. As for the results of the coefficient of determination, that is equal to 0.546 and shows that the influence of interpersonal communication between parents and learning facilities at home on student motivation is equal to 54.6%. So interpersonal communication and learning facilities positively affect students' learning motivation.
GCA: A Graph-based Channel Attention Module for Convolutional Neural Networks

&lt;p&gt;Convolutional neural networks (CNNs) have achieved great success in many computer vision tasks. Recently, attention mechanisms have proven to be critical in lifting the performance of deep convolutional neural networks (CNNs). In this work, we investigate effective attention mechanisms and propose a novel network unit, which we call the “Graph Channel Attention” (GCA) block, that dynamically encourages the communication across channels by explicitly modelling the cross-channel interaction. The unit is designed with a graph channel attention mechanism in two steps, where the first step captures position information from different spatial locations through a position prior module and the second step enables channels to exchange information via a message exchange module. The proposed GCA block is efficient and can be easily plugged into existing deep neural networks. Extensive experiments show that the proposed method attains superior performance compared with other lightweight attention mechanisms on image classification, object detection, instance segmentation etc. In particular, the proposed method achieves a 4.12% top-1 accuracy improvement on ImageNet classification over a ResNet50 backbone. Furthermore, detailed analyses show that the proposed method could significantly reduce redundancy in features and learn more diverse feature representations.&lt;/p&gt;
A Review of Research on Direct Reported Speech

Being an interest in different fields, Direct Reported Speech (DRS) is mainly studied in linguistics and sociology as a complex phenomenon in texts and conversations. This paper makes a review of research on DRS according to different research orientations in the fields of syntactic, semantic, phonetic, pragmatic, psycholinguistic, sociolinguistic and Conversation Analytic studies, providing an interdisciplinary perspective for understanding DRS and future studies on it.
AMAZON S3 STATIC WEBSITE HOSTING

Nowadays, especially in period of energetic crisis, it is very important to get reliable data storage and uninterrupted access to websites. Considering that the world’s mainstream is using the clouds services for educational, scientific, business purposes it is important to explore features of hosting websites in major cloud service providers. One of the leader’s vendors in cloud servicing is Amazon with its Web Services (AWS). AWS offers hosting solutions such as Lightsail, Amplify Console, Simple Storage Service (S3) and Elastic Cloud Computing, from simple static websites to complicated dynamic, The article deals with the method of hosting static websites. Simple Storage Service was used for this purpose. The method has many benefits: the service is simple, cheap, scales well, manages everything and has great integration with other AWS services. This method is the best for websites that do not contain server-side scripting, like PHP or ASP.NET. It is ideal for websites that change infrequently, such as personal, promo, startup websites, websites of small businesses and organizations. The article focuses on how set permissions, properties and policies of AWS S3 bucket for hosting a website. But usage of S3 bucket alone provides only http connection. That’s why to deploy secure connection and speed up delivery of the static content was proposed to use AWS CloudFront service. CloudFront is content delivery network service, that uses the latest version of Transport Layer Security Protocol and saves files in the cache for 24 hours. The article shows how to configure CloudFront distribution to serve https requests for an Amazon S3 bucket. Configuration S3 bucket REST API endpoint was used as the most secure case. As a result, latency was reduced, security was improved due to traffic encryption and the content of S3 bucket was kept private.