Reviewerly/exHarmony huggingface dataset:
Multi-Objective Optimization for Sparse Deep Multi-Task Learning

Different conflicting optimization criteria arise naturally in various Deep Learning scenarios. These can address different main tasks (i.e., in the setting of Multi-Task Learning), but also main and secondary tasks such as loss minimization versus sparsity. The usual approach is a simple weighting of the criteria, which formally only works in the convex setting. In this paper, we present a Multi-Objective Optimization algorithm using a modified Weighted Chebyshev scalarization for training Deep Neural Networks (DNNs) with respect to several tasks. By employing this scalarization technique, the algorithm can identify all optimal solutions of the original problem while reducing its complexity to a sequence of single-objective problems. The simplified problems are then solved using an Augmented Lagrangian method, enabling the use of popular optimization techniques such as Adam and Stochastic Gradient Descent, while efficaciously handling constraints. Our work aims to address the (economical and also ecological) sustainability issue of DNN models, with a particular focus on Deep Multi-Task models, which are typically designed with a very large number of weights to perform equally well on multiple tasks. Through experiments conducted on two Machine Learning datasets, we demonstrate the possibility of adaptively sparsifying the model during training without significantly impacting its performance, if we are willing to apply task-specific adaptations to the network weights. Code is available at https://github.com/salomonhotegni/MDMTN
How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy

End-to-end learning has emerged as a major paradigm for developing autonomous systems. Unfortunately, with its performance and convenience comes an even greater challenge of safety assurance. A key factor of this challenge is the absence of the notion of a low-dimensional and interpretable dynamical state, around which traditional assurance methods revolve. Focusing on the online safety prediction problem, this paper proposes a configurable family of learning pipelines based on generative world models, which do not require low-dimensional states. To implement these pipelines, we overcome the challenges of learning safety-informed latent representations and missing safety labels under prediction-induced distribution shift. These pipelines come with statistical calibration guarantees on their safety chance predictions based on conformal prediction. We perform an extensive evaluation of the proposed learning pipelines on two case studies of image-controlled systems: a racing car and a cartpole.
Bugsplainer: Leveraging Code Structures to Explain Software Bugs with Neural Machine Translation

Software bugs cost the global economy billions of dollars each year and take up ~50% of the development time. Once a bug is reported, the assigned developer attempts to identify and understand the source code responsible for the bug and then corrects the code. Over the last five decades, there has been significant research on automatically finding or correcting software bugs. However, there has been little research on automatically explaining the bugs to the developers, which is essential but a highly challenging task. In this paper, we propose Bugsplainer, a novel web-based debugging solution that generates natural language explanations for software bugs by learning from a large corpus of bug-fix commits. Bugsplainer leverages code structures to reason about a bug and employs the fine-tuned version of a text generation model, CodeT5, to generate the explanations. Tool video: https://youtu.be/xga-ScvULpk
Curriculum Learning with Adam: The Devil Is in the Wrong Details

Curriculum learning (CL) posits that machine learning models -- similar to humans -- may learn more efficiently from data that match their current learning progress. However, CL methods are still poorly understood and, in particular for natural language processing (NLP), have achieved only limited success. In this paper, we explore why. Starting from an attempt to replicate and extend a number of recent curriculum methods, we find that their results are surprisingly brittle when applied to NLP. A deep dive into the (in)effectiveness of the curricula in some scenarios shows us why: when curricula are employed in combination with the popular Adam optimisation algorithm, they oftentimes learn to adapt to suboptimally chosen optimisation parameters for this algorithm. We present a number of different case studies with different common hand-crafted and automated CL approaches to illustrate this phenomenon, and we find that none of them outperforms optimisation with only Adam with well-chosen hyperparameters. As such, our results contribute to understanding why CL methods work, but at the same time urge caution when claiming positive results.
PENINGKATAN HASIL BELAJAR MATERI MEMBACA WAKTU MENGGUNAKAN MEDIA BENDA NYATA PADA SISWA KELAS II SDN SUMBER KEJAYAN 01 KECAMATAN MAYANG

Implementation of Learning Improvement is carried out because the achievement of student learning outcomes is still less than the KKM. This is because many students only pay attention, listen to explanations, absorb and accept the new information they receive, then record and memorize it and work on the practice questions given by the teacher. The author's goal in carrying out this research report is to improve student learning outcomes in grade II by implementing learning using real object media while reading material at SDN Sumber Kejayan 01. Assessment of learning activities is in the form of written tests and observations. The score sheet is in the form of individual scores, namely an increase in mastery results from cycle I with 14 students or 77.77% completeness, in cycle II it increases to 18 students or 100%. This shows an increase in mastery in the class has achieved mastery by using real object media.&#x0D; &#x0D; Keywords: Learning Outcomes, Reading Time, Media, Real Objects
Gesture Recognition based on Long-Short Term Memory Cells using Smartphone IMUs

Over the last few decades, Smartphone technology has seen significant improvements. Enhancements specific to built-in Inertial Measurement Units (IMUs) and other dedicated sensors of the smartphones(which are often available as default) such as- Accelerometer, Gyroscope, Magnetometer, Fingerprint reader, Proximity and Ambient light sensors have made devices smarter and the interaction seamless. Gesture recognition using these smart phones have been experimented with many techniques. In this solution, a Recurrent Neural Network (RNN) approach, LSTM (Long-Short Term Memory Cells) has been used to classify ten different gestures based on data from Accelerometer and Gyroscope. Selection of sensor data (Accelerometer and Gyroscope) was based on the ones that provided maximum information regarding the movement and orientation of the phone. Various models were experimented in this project, the results of which are presented in the later sections. Furthermore, the properties and characteristics of the collected data were studied and a set of improvements have been suggested in the future work section.
An algorithm to approximate the real trilogarithm for a real argument

We present an algorithm to approximate the real trilogarithm for a real argument with IEEE 754-1985 double precision accuracy. The approximation is structured such that it can make use of instruction-level parallelism when executed on appropriate CPUs.
IoT Data Trust Evaluation via Machine Learning

Various approaches based on supervised or unsupervised machine learning (ML) have been proposed for evaluating IoT data trust. However, assessing their real-world efficacy is hard mainly due to the lack of related publicly-available datasets that can be used for benchmarking. Since obtaining such datasets is challenging, we propose a data synthesis method, called random walk infilling (RWI), to augment IoT time-series datasets by synthesizing untrustworthy data from existing trustworthy data. Thus, RWI enables us to create labeled datasets that can be used to develop and validate ML models for IoT data trust evaluation. We also extract new features from IoT time-series sensor data that effectively capture its auto-correlation as well as its cross-correlation with the data of the neighboring (peer) sensors. These features can be used to learn ML models for recognizing the trustworthiness of IoT sensor data. Equipped with our synthesized ground-truth-labeled datasets and informative correlation-based feature, we conduct extensive experiments to critically examine various approaches to evaluating IoT data trust via ML. The results reveal that commonly used ML-based approaches to IoT data trust evaluation, which rely on unsupervised cluster analysis to assign trust labels to unlabeled data, perform poorly. This poor performance can be attributed to the underlying unsubstantiated assumption that clustering provides reliable labels for data trust, a premise that is found to be untenable. The results also show that the ML models learned from datasets augmented via RWI while using the proposed features generalize well to unseen data and outperform existing related approaches. Moreover, we observe that a semi-supervised ML approach that requires only about 10% of the data labeled offers competitive performance while being practically more appealing compared to the fully-supervised approaches.
VQA Therapy: Exploring Answer Differences by Visually Grounding Answers

Visual question answering is a task of predicting the answer to a question about an image. Given that different people can provide different answers to a visual question, we aim to better understand why with answer groundings. We introduce the first dataset that visually grounds each unique answer to each visual question, which we call VQAAnswerTherapy. We then propose two novel problems of predicting whether a visual question has a single answer grounding and localizing all answer groundings. We benchmark modern algorithms for these novel problems to show where they succeed and struggle. The dataset and evaluation server can be found publicly at https://vizwiz.org/tasks-and-datasets/vqa-answer-therapy/.
Layering and subpool exploration for adaptive Variational Quantum Eigensolvers: Reducing circuit depth, runtime, and susceptibility to noise

Adaptive variational quantum eigensolvers (ADAPT-VQEs) are promising candidates for simulations of strongly correlated systems on near-term quantum hardware. To further improve the noise resilience of these algorithms, recent efforts have been directed towards compactifying, or layering, their ansatz circuits. Here, we broaden the understanding of the algorithmic layering process in three ways. First, we investigate the non-commutation relations between the different elements that are used to build ADAPT-VQE ans\"atze. Doing so, we develop a framework for studying and developing layering algorithms, which produce shallower circuits. Second, based on this framework, we develop a new subroutine that can reduce the number of quantum-processor calls by optimizing the selection procedure with which a variational quantum algorithm appends ansatz elements. Third, we provide a thorough numerical investigation of the noise-resilience improvement available via layering the circuits of ADAPT-VQE algorithms. We find that layering leads to an improved noise resilience with respect to amplitude-damping and dephasing noise, which, in general, affect idling and non-idling qubits alike. With respect to depolarizing noise, which tends to affect only actively manipulated qubits, we observe no advantage of layering.
Identifying Cardiovascular Disease Risk Factors in Adults with Explainable Artificial Intelligence

The aim of this study was to evaluate the relationship between risk factors causing cardiovascular diseases and their importance with explainable machine learning models.In this retrospective study, multiple databases were searched, and data on 11 risk factors of 70 000 patients were obtained. Data included risk factors highly associated with cardiovascular disease and having/not having any cardiovascular disease. The explainable prediction model was constructed using 7 machine learning algorithms: Random Forest Classifier, Extreme Gradient Boost Classifier, Decision Tree Classifier, KNeighbors Classifier, Support Vector Machine Classifier, and GaussianNB. Receiver operating characteristic curve, Brier scores, and mean accuracy were used to assess the model's performance. The interpretability of the predicted results was examined using Shapley additive description values.The accuracy, area under the curve values, and Brier scores of the Extreme Gradient Boost model (the best prediction model for cardiovascular disease risk factors) were calculated as 0.739, 0.803, and 0.260, respectively. The most important risk factors in the permutation feature importance method and explainable artificial intelligence-Shapley's explanations method are systolic blood pressure (ap_hi) [0.1335 ± 0.0045 w (weight)], cholesterol (0.0341 ± 0.0022 w), and age (0.0211 ± 0.0036 w).The created explainable machine learning model has become a successful clinical model that can predict cardiovascular patients and explain the impact of risk factors. Especially in the clinical setting, this model, which has an accurate, explainable, and transparent algorithm, will help encourage early diagnosis of patients with cardiovascular diseases, risk factors, and possible treatment options.
A Highly Robust Medical Image Watermarking Method for Medical Real-time Applications.

Watermarking such as other security concepts is an ongoing challenging research issue, especially for medical images, to protect patient privacy. Medical images need to be shared and transferred between hospitals and specialists as quickly as possible for better diagnosis. Fast and simple watermarking is needed as well as the robust transferring of channel noise, such as salt and pepper noise and robust cropping that may occur from specialists and signature encryption for patient privacy.In this article, a highly robust and simple watermarking method is introduced. The proposed method has very low computational complexity and at the same time, it is very robust to interference and uses simple computations such as (XORs) Exclusive ORs and rotations that can be done in real-time. The proposed method uses a combination of hidden neighboring signature information, Sudoku permutation, and noise pre-processing to achieve high robustness against salt and pepper noise and cropping. Simple signature encryption is also used.The proposed method is examined in different medical image datasets. The experimental results indicate the proposed watermarking system is robust to salt and pepper noise density of up to 90% and about 70% cropping. The number of computations including encryption is five XOR per pixel and a rotation per block of signature size.A novel method for medical image watermarking is presented. The proposed method is in the spatial domain, has encryption, and uses only XOR computation. The proposed method is highly robust to noise and cropping which is necessary for medical uses. The proposed method can be used efficiently for real-time watermarking for medical and nonmedical image datasets.
DEKADENSI MORAL DI KAMPUNG SAMBUNG AYAM DESA PABUARAN KECAMATAN PABUARAN KABUPATEN SERANG PROVINSI BANTEN

The purpose of this research is to describe moral decadence in the chicken-fed village in the Pabuaran community, Pabuaran sub-district, Serang district, Banten province. The approach used is qualitative with the type of case study research. The research location is located in Indonesia. The results of the study show that: moral decadence in Kampung Connect Ayam is often carried out by men, aged 20-50 years and working, educated Players range from elementary to high school/vocational school, player betting intensity in one or two months, 4-12 times and cock games. Players Can use their own fighting cocks or bet on other people’s cocks. Second, how to play cockfighting gambling consists of several stages, namely preparation, implementation and completion. The preparatory stage consists of players or chicken owners looking for or determining enemies and determining locations. The next step is execution, at this execution stage the cocks fight each other for 90 minutes, consisting of 6 circles. In the final phase, the cock loses or wins And the distribution of the betting funds. The chicken that is usually played in cockfights is the male Bangkok chicken. Third, the gambler who connects the chicken usually carries out the action with friends and neighbors, meanwhile the money from the bet is used to have fun, get drunk and fulfill their daily needs.
CONTENT BASED BOOK RECOMMENDATION SYSTEM

The Content-Based Book Recommendation System is an innovative project that leverages content-based filtering, collaborative filtering with K-means clustering, and similarity checks to provide personalized book recommendations to users.Traditional book recommendation systems often rely solely on user preferences or item similarity, which can limit the accuracy and relevance of the recommendations.This project aims to overcome these limitations by incorporating multiple techniques to improve the quality of recommendations.The content-based filtering approach considers a characteristics of each book, such as genre, author, and keywords, to establish a profile for each user.By comparing the user profile with the book features, the system identifies relevant recommendations that align with the user's interests.Additionally, collaborative filtering enhances the recommendations by leveraging similarities among users' preferences and behaviours.The system employs K-means clustering to group users with similar interest and generate more accurate recommendations within each cluster.
Managing cybersecurity risk in healthcare settings

Cybersecurity attacks have been steadily increasing in the healthcare sector over the past decade. Health data is a valuable source of reliable and permanent personal information making it an attractive target. Institutions that have faced limited cybersecurity funding must now augment their approach to combat this threat. The Internet of Things (IoT) refers to the connection of physical operational devices to digital networks allowing for communication between devices. In the healthcare setting, this includes patient monitoring, diagnostics, and even robotic surgery devices. This increased connectivity increases the importance of agile and robust cybersecurity measures. A progressive approach must involve collaboration between information technology, clinical, and administrative leaders to be successful. Adequate protection of patient data and the integrity of digital infrastructure must be a priority mandate at the enterprise level.
INCREASING STUDENTS' READING COMPREHENSION OF RECOUNT TEXT THROUGH PICTURES IN CAROUSEL BRAINSTORMING STRATEGY FOR 8TH GRADE AT MTS TAQWIYATUL WATHON ACADEMIC YEAR 2022/2023

The purpose of the research was to compare students' levels the extent of comprehension before and after being introduced to Carousel brainstorming strategy for reading recount texts through pictures. The research methodology is a quantitative pre-experimental research design. The sample was 33 students of Eight grade A at MTs Taqwiyatul Wathon. The instrument for research consisted of a pre-test and a post-test. The researcher employed Indonesian KKM (Minimum completeness criteria) at MTs Taqwiyatul Wathon with the KKM score of 70 as the criterion. pre test results indicate were 6,06% met the KKM criteria, and the post test results were 78,8% accomplishing the KKM standards. The researcher also employed the N-Gain method to ascertain whether or not the addition of recount text through pictures in Carousel brainstorming strategy. In this case, the The N-Gain Formula resulted in a mean value of 58.3155. Students' reading comprehension dramatically increase after being introduced to recount text through pictures in Carousel brainstorming strategy
THEMATIC PROGRESSION ANALYSIS ON STUDENTS’ ANALYTICAL EXPOSITION TEXTS

This study aims to investigate how university students organize their ideas in Exposition texts, particularly Analytical Exposition texts, and to what extent their Analytical Exposition texts are consistent with argumentative language features in terms of Thematic Progression, based on the framework of Systemic Functional Linguistics. The data were taken from nine Analytical Exposition texts of university students at low, moderate, and high levels of achievement. The result shows that the students organize their ideas in three ways of Thematic Progression namely Constant Theme Theme Progression (CTP) or Re-iteration pattern, Simple Linear Theme Progression (SLP) or Zig-Zag pattern, and Derived Theme Progression (DTP) or Multiple Theme pattern. In terms of Thematic Progression consistency, some texts from middle and high achievers are consistent with the argumentative language features, since they employed SLP than CTP and DTP. However, some students still need more guidance to improve the quality of their writings
The Students’ Perception of Instagram Accounts in Improving English Speaking Skills

This study aimed to determine students' perceptions of English learning accounts on Instagram in improving speaking skills and to find out what effects students get from English learning accounts on Instagram in improving speaking skills. The research method used in this study was a mixed–method design. The population in this study were all English education students at Serambi Mecca University, while the selected sample of this study were odd-semester students of English Education who actively used Instagram and also followed English learning accounts on Instagram. They were consisting of 8 semester-1 students, 7 semester-3 students, 12 students from semester 5, and 7 students from semester 7. The sampling technique used was purposive sampling. This study used a questionnaire and an interview as instruments. Quantitative data were collected from the questionnaire using a Likert scale. While from the interview, qualitative data was gathered using a semi-structured set of questions. The data from the questionnaire were analyzed using the percentage formula, while the data from the interview was described in detailed words. This research found that 97% of students believe that the material shared by English learning accounts on Instagram was easy to understand, and 100% of students stated that they were motivated by material shared by English learning accounts on Instagram. It was concluded that accounts that share English learning materials on Instagram can be learning media for students.
Implementasi Metode Port Knocking pada MikroTik RouterOS untuk Mendukung Keamanan Jaringan

This study aims to implement the Port Knocking security method in a local network using MikroTik RouterOS. The Port Knocking method is employed to enhance system security by closing access to specific ports in the network and only allowing access to these ports when a predefined sequence of port knocks is performed. The research encompasses several stages, including the analysis of the port data to be tested, the configuration of the MikroTik router, and the security system testing. The results of this study demonstrate the successful securing of the Winbox and WWW ports on the MikroTik server using the Port Knocking method. Users are required to execute the predetermined port knocking sequence before accessing these services. Furthermore, the Port Knocking method effectively blocks ping access to the MikroTik server for users who have not completed the authentication process. These findings indicate that the implementation of the Port Knocking method significantly enhances the local network security on the MikroTik server.