Reviewerly/exHarmony huggingface dataset:
Localized Questions in Medical Visual Question Answering

Visual Question Answering (VQA) models aim to answer natural language questions about given images. Due to its ability to ask questions that differ from those used when training the model, medical VQA has received substantial attention in recent years. However, existing medical VQA models typically focus on answering questions that refer to an entire image rather than where the relevant content may be located in the image. Consequently, VQA models are limited in their interpretability power and the possibility to probe the model about specific image regions. This paper proposes a novel approach for medical VQA that addresses this limitation by developing a model that can answer questions about image regions while considering the context necessary to answer the questions. Our experimental results demonstrate the effectiveness of our proposed model, outperforming existing methods on three datasets. Our code and data are available at https://github.com/sergiotasconmorales/locvqa.
Meaning and identity of proofs in a bilateralist setting: A two-sorted typed lambda-calculus for proofs and refutations

In this paper I will develop a lambda-term calculus, lambda-2Int, for a bi-intuitionistic logic and discuss its implications for the notions of sense and denotation of derivations in a bilateralist setting. Thus, I will use the Curry-Howard correspondence, which has been well-established between the simply typed lambda-calculus and natural deduction systems for intuitionistic logic, and apply it to a bilateralist proof system displaying two derivability relations, one for proving and one for refuting. The basis will be the natural deduction system of Wansing's bi-intuitionistic logic 2Int, which I will turn into a term-annotated form. Therefore, we need a type theory that extends to a two-sorted typed lambda-calculus. I will present such a term-annotated proof system for 2Int and prove a Dualization Theorem relating proofs and refutations in this system. On the basis of these formal results I will argue that this gives us interesting insights into questions about sense and denotation as well as synonymy and identity of proofs from a bilateralist point of view.
UW-ProCCaps: UnderWater Progressive Colourisation with Capsules

Underwater images are fundamental for studying and understanding the status of marine life. We focus on reducing the memory space required for image storage while the memory space consumption in the collecting phase limits the time lasting of this phase leading to the need for more image collection campaigns. We present a novel machine-learning model that reconstructs the colours of underwater images from their luminescence channel, thus saving 2/3 of the available storage space. Our model specialises in underwater colour reconstruction and consists of an encoder-decoder architecture. The encoder is composed of a convolutional encoder and a parallel specialised classifier trained with webly-supervised data. The encoder and the decoder use layers of capsules to capture the features of the entities in the image. The colour reconstruction process recalls the progressive and the generative adversarial training procedures. The progressive training gives the ground for a generative adversarial routine focused on the refining of colours giving the image bright and saturated colours which bring the image back to life. We validate the model both qualitatively and quantitatively on four benchmark datasets. This is the first attempt at colour reconstruction in greyscale underwater images. Extensive results on four benchmark datasets demonstrate that our solution outperforms state-of-the-art (SOTA) solutions. We also demonstrate that the generated colourisation enhances the quality of images compared to enhancement models at the SOTA.
SCITUNE: Aligning Large Language Models with Scientific Multimodal Instructions

Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent. Despite its popularity, this idea is less explored in improving the LLMs to align existing foundation models with scientific disciplines, concepts and goals. In this work, we present SciTune as a tuning framework to improve the ability of LLMs to follow scientific multimodal instructions. To test our methodology, we use a human-generated scientific instruction tuning dataset and train a large multimodal model LLaMA-SciTune that connects a vision encoder and LLM for science-focused visual and language understanding. In comparison to the models that are finetuned with machine generated data only, LLaMA-SciTune surpasses human performance on average and in many sub-categories on the ScienceQA benchmark.
Trainable Transformer in Transformer

Recent works attribute the capability of in-context learning (ICL) in large pre-trained language models to implicitly simulating and fine-tuning an internal model (e.g., linear or 2-layer MLP) during inference. However, such constructions require large memory overhead, which makes simulation of more sophisticated internal models intractable. In this work, we propose an efficient construction, Transformer in Transformer (in short, TinT), that allows a transformer to simulate and fine-tune complex models internally during inference (e.g., pre-trained language models). In particular, we introduce innovative approximation techniques that allow a TinT model with less than 2 billion parameters to simulate and fine-tune a 125 million parameter transformer model within a single forward pass. TinT accommodates many common transformer variants and its design ideas also improve the efficiency of past instantiations of simple models inside transformers. We conduct end-to-end experiments to validate the internal fine-tuning procedure of TinT on various language modeling and downstream tasks. For example, even with a limited one-step budget, we observe TinT for a OPT-125M model improves performance by 4-16% absolute on average compared to OPT-125M. These findings suggest that large pre-trained language models are capable of performing intricate subroutines. To facilitate further work, a modular and extensible codebase for TinT is included.
E-Voting System for Election with Mobile Based Apps

Election is an important moment for the democratic process in Indonesia, Indonesia has held elections regularly for a long time. Election in Indonesia still carried out conventionally by coming to Tempat Pemungutan Suara (TPS) or Voting Place to vote. The purpose of this research is to design a mobile based election e-voting system as means to conduct elections for head of state or region and make it easier for the people to vote without having to travel. The research method used in this research is descriptive analysis with a qualitative approach. The result of this research is to show that the E-PIL application can make it easier for Komisi Pemilihan Umum (KPU) to carry out election (pemilu) also make it easier for the people to vote only by accessing E-PIL application that can be accessed via each smartphone. The main concept of this application is e-voting feature for election that can be used in every election agenda. Also makes it easier for the KPU in voting process because E-PIL application can only be used by 1 ID on 1 device that integrated with the government. In the end, E-PIL application is present as a medium for elections as well as to adapt in today’s digital world
A systematic literature review of integrating augmented reality technology in science learning

Abstrak: Penelitian ini bertujuan untuk mengungkap tren penelitian selama sepuluh tahun terakhir pada penggunaan teknologi augmented reality (AR) dalam pembelajaran IPA. Review pada studi ini mengikuti pedoman PRISMA. Ditemukan 154 artikel pada tahap identifikasi kemudian dilakukan screening berdasarkan kriteria inklusi dan ekslusi sehingga menjadi 50 artikel untuk dilakukan review pada studi ini. Hasil review menunjukkan bahwa pembelajaran/prestasi akademik, motivasi dan sikap merupakan variabel yang paling banyak diteliti dalam artikel. Dimana prestasi akademik sangat dipengaruhi oleh motivasi dan sikap sehingga sering dipertimbangkan dalam penelitian. Aplikasi mobile dan buku bergambar AR merupakan jenis AR yang sering diimplementasikan karena mudah digunakan dan dapat dikembangkan dengan cepat dan praktis. Kecenderungan metodologi adalah desain kuantitatif. Alat pengumpulan data yang sering digunakan adalah tes kognitif. Metode pengambilan sampel yang paling sering digunakan adalah convenience sampling, dan populasi sampel yang sering diidentifikasi adalah siswa sekolah menengah. Rekomendasi penelitian yang sering diberikan oleh peneliti adalah melakukan identifikasi pengaruh penggunaan AR terhadap kemampuan afektif siswa dan mengidentifikasi persepsi guru. Hasil penelitian ini akan menjadi sebuah referensi bagi para peneliti yang tertarik untuk mengimplementasikan AR dalam pembelajaran dan rekomendasi untuk penelitian selanjutnya.Abstract: This study aimed to explore research trends over the past decade regarding the use of augmented reality (AR) technology in science learning. The study followed the PRISMA guidelines. A total of 154 articles were found at the identification stage and then screened based on inclusion and exclusion criteria so that there were 50 articles to be reviewed in this study. The review results show that learning achievement, motivation, and attitudes are the variables most researched in the article. Learning achievement is strongly influenced by motivation and attitude, so it is often considered in research. Mobile applications and AR picture books are often implemented because they are easy to use, fast, and have practical development. The methodological trend is a quantitative design. Data collection tools that are often used are cognitive tests. The frequently used sampling method is convenience sampling, and the sample population often identified is high school students. Researchers often give research recommendations to identify the effect of using AR on students' affective abilities and teacher perceptions. The results of this study will be a reference for researchers interested in implementing AR in education and recommendations for further research.
Correction

This article refers to:Business students’ learning and assessment in a COVID-19 world: empirical evidence from Finland
PENENTUAN KADAR ALKOHOL PADA PEMINUM ALKOHOL DENGAN METODE ALCOHOL SALIVA STRIP TEST

Alcohols are organic compounds containing hydroxyl functional groups. The alcohol content in the drink is ethanol. Alcohol poisoning is often associated with injuries from falls, fires, drowning, overdose, sexual abuse, work accidents, traffic accidents, and abusive relationship. This study aims to determine the level of alcohol in alcoholics using the Alcohol Saliva Strip Test method. The type of research used is descriptive with purposive sampling technique and cross-sectional approach and. Specimen collection and examination of the alcohol content in the respondent's saliva were carried out in March 2021 at Kp X, Depok City, West Java Province. The number of respondents was 28 people. The inclusion criteria for this study was alcoholic drinkers aged 17-25 years. The study exclusion criteria were using mouthwash, consuming ascorbic acid such as vitamin C, and having a history of gout. The specimen used in this research was saliva. The method is detected for alcohol content of the respondents used the RightSign brand Alcohol Saliva Strip Test. The results showed the age of the respondents who drank the most alcohol in the range of 17-19 years was 57%. The characteristics of respondents based on the frequency of alcohol consumption are highest in the frequent category (3 times a week or more) as much as 68%. The results showed 71% of the respondents positively drank alcohol and 29% of the respondents were negative. The lowest alcohol content in the respondent's saliva was 0.02% and the highest was 0.08%. The alcohol content in the saliva of the respondents who last drank alcohol 1 week ago was undetectable. This study concludes that the percentage of respondent who detected alcohol in their saliva was 71% and the highest alcohol content in the respondent's saliva is 0.08%. This study also shows that the period, alcohol concentration, and type of alcohol consumed can influence Blood Alcohol Concentration (BAC).
Meningkatkan Kemampuan Menghitung dengan Media Papan Hitung pada Siswa Kelas 2 SD Negeri Petir 2 Kota Tangerang

This study aims to develop a counting board learning media for grade 2 elementary schools. The research was conducted based on the basic facts in the field, it is known that the understanding of distribution is still low and there is a lack of supporting media for distribution. The type of research used is research that takes place in 3 stages. The research subjects were students of SD Negeri Petir 2 Tangerang City. The data collection technique used was a questionnaire which was assessed by giving questionnaires to students after using the counting board media. The results of this development research indicate that the developed learning media meets the good and feasible criteria so that it can be tested on grade 2 elementary school students. Based on the results of the total research sample, 90% of students received and felt the benefits of the division counting board learning media. The main benefit that students get is the division of the counting board which simplifies and speeds up the process of dividing arithmetic.
Enhancing the Performance of Solar Radiation Decomposition Models Using Deep Learning

Decomposed solar radiation models are commonly used to separate direct and diffuse irradiance from global irradiance.However, most of these models are designed to process hourly data, which may not be sufficient to capture the rapid changes in solar irradiance that occur within a shorter timescale.To address this issue, we examined the performance of existing decomposition models at different temporal resolutions ranging from 1 min to 1 h.We found that the errors in the decomposition models increased as the temporal resolution decreased.Specifically, as the timescale was reduced from hourly to every minute, the relative root-mean-square error (rRMSE) increased by more than 5%.These findings highlight the need to develop accurate models that can process sub-hourly data.Accordingly, we propose the use of deep learning models to estimate the direct irradiance using sub-hourly data.The proposed models significantly reduced the rRMSE by more than 7% compared to the existing models on a 1-min time scale.The results indicate that deep-learning models can provide accurate estimates of direct irradiance, even at sub-hourly temporal resolutions.
PELATIHAN PERCAKAPAN BAHASA INGGRIS TENTANG LIKE AND DISLIKE UNTUK ANAK-ANAK PANTI ASUHAN AR-RIDHO DEPOK JAWA BARAT

Community service is the practical implementation of science, technology, art, and culture directly into society aimed at helping a particular community in various activities without expecting anything in return. In 2022, English Language and Literature study program lecturers held an English Conversation Workshop on Like and Dislike of children at Ar-Ridho Orphanage, Depok, West Java. The purpose of this workshop is to improve participants' English skills, especially speaking. Global competition is increasingly fierce demanding the creation of quality human resources, one of which is by having international communication skills, namely using English. A total of 20 participants attended this workshop face-to-face. The methods used are the lecture method and the role-play technique. With the implementation of this workshop, participants are expected to be able to enhance their confidence and motivation to speak and practice the material provided in daily conversations.
News from the Field

Transitions and new leadership at ACRL University of Nevada, Reno University Libraries join Greater Western Library Alliance ARLIS/NA announces new mission, vision, values New from ACRL—Instructional Identities and Information Literacy ACRL releases Managing Crises in the Academic Library: Past, Present, and Future UNC Charlotte names 2023 Atkins Fellows Choice’s The Authority File podcast wins 2023 Publisher Podcast Award
ANALISIS SPEAKING ANXIETY DALAM PROSES PEMBELAJARAN PADA MAHASISWA PBI SEMESTER 5 UIN RADEN MAS SAID SURAKARTA

This study is a qualitative study where the researcher aims to observe and analyze speaking anxiety in 5th semester English Education students at UIN Raden Mas Said Surakarta. This study adopts or uses data collection techniques in the form of interviews and questionnaires. Researchers collected 14 respondent data from students majoring in English Education in semester 5 who had undergone interviews and had filled out questionnaires. Because this research uses an qualitative approach, the data analysis technique used is qualitative descriptive non-statistical. The findings show that some of the main causes of speaking anxiety in students are self-confidence, lack of vocabulary and also lack of preparation. The results of this study contribute to the field of language teaching in terms of student factors, especially speaking anxiety during classroom learning. This study aims to apply the method of overcoming speaking anxiety in the learning process in the classroom, especially English Education. Keywords: English Education; Speaking Anxiety; Students
PELATIHAN BAHASA ISYARAT BAGI MAHASISWA UNIVERSITAS MUHAMMADIYAH PRINGSEWU

The law has guaranteed people with disabilities to get fair treatment in all aspects of life. So far, people with disabilities, especially the deaf, have had problems participating in various activities due to the unavailability of a sign language interpreter. This Community Service activity aims to produce new sign language translators who are able to help deaf people participate in general activities. This service is carried out by holding sign language classes for 10 weeks. Classes are attended by elementary, middle, high school, and lecturers as well as young people who want to dedicate themselves as companions with disabilities
Detection of driver distraction in the Australian naturalistic driving study videos using pre-trained models and transfer learning

Naturalistic driving data (NDS), collected from fitted vehicles with multiple sensors, including cameras, is a rich source to understand how drivers become distracted. However, detecting the distraction events traditionally required watching NDS videos which is expensive and time-consuming. Adopting state-of-the-art machine learning techniques to automate distraction detection using videos will save effort and money. Reducing/annotating a random sample of the videos and using it to train a machine learning model to carry out the data reduction for the rest of the dataset is possible. This paper investigates the feasibility of using pre-trained deep neural networks and random forest to detect driver distraction in the Australian Naturalistic Driving Study (ANDS) video data. The pre-trained model was fine-tuned using transfer learning; therefore, it can be used to classify (i.e. distracted/non-distracted) successive frames independently. To capture the time dependency between the consecutive images/frame during the training phase, we converted the 1-D output probability of the fine-tuned model into a higher dimension space. The distraction probability of each frame at a time is transformed into a vector by concatenating the distraction output probabilities of all frames within a time window centred at time. Moreover, the vector centred at the time was assigned the actual label of the frame at this time. Then we used Random Forest (RF) classifier to model the non-linear relationship between the probabilities in this new space and the distracted/non-distracted labels. This proposed framework was trained using some ANDS trips and tested using another set of trips. When applied to the dashboard camera, the proposed framework achieved 0.609, 0.218 and 0.325 for the true positive rate (TPR), the false positive rate (FPR), and the precision, respectively. Moreover, the face camera achieved 0.748, 0.344 and 0.651 for TPR, the FPR and the precision, respectively. These promising results suggest the proposed framework can be used routinely in many other data reduction tasks.
A Mini-batch Stochastic Recursive Gradient Method with Barzilai-Borwein Step-Size for Machine Learning 

Abstract As a mini-batch version of the SARAH algorithm, the MB-SARAH algorithm has received extensive attention due to its simple recursive scheme for updating stochastic gradient estimates. In this paper, we give a modification of the MB-SARAH method via cooperating with the BB step-size, shorted to MB-SARAH-BB. The MB-SARAH-BB combines some advantages of both MB-SARAH and BB methods, providing robustness in selecting initial step size during the optimization process. In the framework of MB-SARAH-BB, we propose a novel implementable method, Ada-MB-SARAH-BB, which utilizes adaptive probability for sampling in the mini-batch stochastic recursive gradient computation during the inner loop iteration. We establish the linear convergence of the MB-SARAH-BB and Ada-MB-SARAH-BB methods under some mild assumptions. Numerical experiments on standard machine learning datasets demonstrate that, the MB-SARAH-BB is effective and more competitive than the recent successful stochastic gradient methods. In addition, numerical experiments also demonstrate that the performance of Ada-MB-SARAH-BB is generally better than or comparable to MB-SARAH-BB method. MSC Classification: 90C15 · 90C25 · 90C30
RANSOMWARE ATTACKS AS A CYBERSECURITY INSURANCE COVERAGE THREAT

The main purpose of this article is to analyze ransomware risk and its impact on the loss ratio in cyber insurance. To achieve this goal, the article indicates the scale of the threat of ransomware attacks and the prospects for change in the field of cyber insurance protection. Methodologically, the focus is on analysis and literature studies in order to properly describe and classify cyber threats, including ransomware risk. Statistical data were analyzed to find the scale of ransomware threats. Through the analysis, attention was drawn to the need for changes in the approach to cyber risk by both entrepreneurs and insurance companies. The originality of the study lies in its attempt to capture the necessity of changes in the field of cyber insurance, and justify their introduction. A research gap was identified, as the problem of ransomware attacks became particularly acute during the COVID-19 pandemic.
How Openness Could Strengthen Academia’s Partnerships with the Intelligence Community

tarting with Vannevar Bush's seminal 1945 report, Science, the Endless Frontier, the federal government has invested significantly in support of fundamental research at
Aplikasi Model ARIMA dalam Peramalan Data Harga Emas Dunia Tahun 2010-2022

Gold investment is one of the favorite investments during the Covid-19 pandemic because the price of gold is relatively volatile but shows an increasing trend. Savvy investors investing in gold need to be able to predict future opportunities. Therefore, price estimation is needed to develop a buying and selling strategy to maximize profits. The Autoregressive Integrated Moving Average (ARIMA) model is a suitable method for predicting time series data, so the best ARIMA model will be applied for forecasting world gold prices. The best ARIMA model is selected based on the Akaike Information Criterion (AIC) and Mean Absolute Percentage Error (MAPE) criteria. Monthly world gold price data for 146 periods are applied in this study and will be used to predict gold prices for the following six periods. ARIMA(0,1,1) is the best model obtained from the analysis results, with AIC and MAPE values ​​of 1264.731 and 11.972%, respectively. Forecasting results show that world gold prices will increase for the next periods.