Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN REST API UNTUK INFORMASI PASAR TRADISIONAL DI KOTA YOGYAKARTA DENGAN METODE INCREMENTAL

Traditional markets in the city of Yogyakarta offer many unique things for both locals and tourists. There has yet to be an effort to centralise information on traditional markets which highlights its characteristics, including its uniqueness. Through this study, we designed a REST API that provides a service to access information on traditional markets in the city of Yogyakarta. The information available includes class, alternative names, location, description, history, facts, facilities, uniqueness, photos, and stalls. This service can be used by governments or other parties through external applications ensuring data accuracy. We used an incremental approach made of three iterations. Each iteration consisted of needs analysis, system design, API development, and testing and evaluation. This approach provided a more focused and targeted development based on the functional needs of each iteration. Each iteration was tested both on Postman and a web application. The result of this REST API is a JavaScript Object Notation (JSON) which can be easily processed by external applications.
Three-Dimensional-Slice-Super-Resolution-Net: A Fast Few Shooting Learning Model for 3D Super-Resolution Using Slice-Up and Slice-Reconstruction

Abstract 3D modeling accurately depicts the physical world but typically requires substantial data acquisition resources and significant storage space. We introduce a novel three-dimensional slice-reconstruction model (3DSR) to address these challenges. This 3D data super-resolution model leverages low-resolution 3D data as input to generate high-resolution results promptly and accurately, reducing the time and storage required to create detailed 3D models. To enhance the computational efficiency and accuracy of deep learning models, the 3D data are partitioned into multiple slices. The 3DSR processes each slice into a high-resolution 2D image, which is then reassembled into high-resolution 3D data. Our slice-up method and slice-reconstruction technique are specifically designed to preserve the primary characteristics of the 3D data. We employ a pre-trained deep 2D convolutional neural network to expand the resolution of the 2D image, resulting in excellent performance. This approach reduces the time required for training deep learning models and enhances computational efficiency during the resolution improvement process. Importantly, our model can deliver superior performance even when trained on fewer data.
Design and Development of Information Systems for Data Collection and Monitoring of Children's Growth and Development as an Effort to Prevent Stunting in Kotapari Village

Stunting is one of the problems being faced by Kotapari Village. The absence of an information system about stunting that is easily accessible to the public is one of the aspects that has slowed down the resolution of the stunting problem. To accelerate the resolution of the stunting problem, it is considered necessary to have an information system that can help the community easily and quickly. Methods of data collection is done by interviews and observation. In this study the system development method used is waterfall with the supporting software used are PHP and MySQL. Seeing the reality above, it is deemed necessary to build a website-based information system that is useful to facilitate Kotapari Village and the Community in working to resolve the stunting problem in Serdang Bedagai Regency. Apart from being able to provide stunting information to the community, this application also has a child health check menu related to child growth and a discussion menu that can be used by the community to communicate with Kotapari Village.
Image to English translation and comprehension: INT2-VQA method based on inter-modality and intra-modality collaborations

Existing visual question answering methods typically concentrate only on visual targets in images, ignoring the key textual content in the images, thereby limiting the depth and accuracy of image content comprehension. Inspired by this, we pay attention to the task of text-based visual question answering, address the performance bottleneck issue caused by over-fitting risk in existing self-attention-based models, and propose a scenario text visual question answering method called INT2-VQA that fuses knowledge manifestation based on inter-modality and intra-modality collaborations. Specifically, we model the complementary priori knowledge of locational collaboration between visual targets and textual targets across modalities and the contextual semantical collaboration among textual word targets within a modality. Based on this, a universal knowledge-reinforced attention module is designed to achieve a unified encoding manifestation of both relations. Extensive ablation experiments, contrast experiments, and visual analyses demonstrate the effectiveness of the proposed method and prove its superiority over the other state-of-the-art methods.
Deep learning transforms colorectal cancer biomarker prediction from histopathology images

Artificial intelligence (AI) is rapidly gaining interest in medicine, including pathological assessments for personalized medicine. In this issue of Cancer Cell, Wagner et al. demonstrate superior accuracy of transformer-based deep learning in predicting biomarker status in CRC. The work has implications for increased efficiency and accuracy in clinical diagnostics guiding treatment decisions in precision oncology.
Quantum-classical eigensolver using multiscale entanglement renormalization

We propose a variational quantum eigensolver (VQE) for the simulation of strongly correlated quantum matter based on a multiscale entanglement renormalization ansatz (MERA) and gradient-based optimization. This MERA quantum eigensolver can have substantially lower computation costs than corresponding classical algorithms. Due to its narrow causal cone, the algorithm can be implemented on noisy intermediate-scale quantum (NISQ) devices and still describe large systems. It is particularly attractive for ion-trap devices with ion-shuttling capabilities. The number of required qubits is system-size independent and increases only to a logarithmic scaling when using quantum amplitude estimation to speed up gradient evaluations. Translation invariance can be used to make computation costs square-logarithmic in the system size and describe the thermodynamic limit. We demonstrate the approach numerically for a MERA with Trotterized disentanglers and isometries. With a few Trotter steps, one recovers the accuracy of the full MERA.
EFEKTIVITAS PEMBELAJARAN DARING DI PROGRAM STUDI PENDIDIKAN BAHASA DAN SASTRA INDONESIA UNIVERSITAS WIDYA DHARMA KLATEN

This study aims to determine the effectiveness of online lectures at the Indonesian Language and Literature Education Study Program, Widya Dharma University, Klaten. The questions to be answered through this research are: 1) Has the implementation of online lectures at the Indonesian Language and Literature Education Study Program at Widya Dharma Klaten University proven effective? 2) What are the advantages and disadvantages of online lectures at the Indonesian Language and Literature Education Study Program, Widya Dharma University Klaten? 3) What areas need to be improved so that the implementation of online lectures runs better?&#x0D; This research uses qualitative research methods with interview and documentation data collection techniques. The research subjects are lecturers and students at the Indonesian Language and Literature Education Study Program, Widya Dharma University, Klaten. The data analysis technique in this study used a qualitative descriptive data analysis interactive model from Milles and Michael Huberman which consisted of three concurrent activity lines, namely; data reduction, data presentation, and conclusion drawing.&#x0D; The research findings show that 1) The implementation of online learning in online lectures at the Indonesian Language and Literature Education Study Program, Widya Dharma University, Klaten, is less effective. 2) The advantages of online learning in online lectures at the Indonesian Language and Literature Education Study Program, Widya Dharma University Klaten A, are flexible study times, students are closer to their parents, students do not depend on teachers and train students' self-confidence. While the drawbacks of implementing online learning are teachers cannot interact directly with students, and the internet network is not stable. 3) Areas to improve so that online learning runs better is the use of more interesting learning media such as animated videos, slide shows, power point.
Enhanced detonators detection in X-ray baggage inspection by image manipulation and deep convolutional neural networks

Detecting detonators is a challenging task because they can be easily mis-classified as being a harmless organic mass, especially in high baggage throughput scenarios. Of particular interest is the focus on automated security X-ray analysis for detonators detection. The complex security scenarios require increasingly advanced combinations of computer-assisted vision. We propose an extensive set of experiments to evaluate the ability of Convolutional Neural Network (CNN) models to detect detonators, when the quality of the input images has been altered through manipulation. We leverage recent advances in the field of wavelet transforms and established CNN architectures-as both of these can be used for object detection. Various methods of image manipulation are used and further, the performance of detection is evaluated. Both raw X-ray images and manipulated images with the Contrast Limited Adaptive Histogram Equalization (CLAHE), wavelet transform-based methods and the mixed CLAHE RGB-wavelet method were analyzed. The results showed that a significant number of operations, such as: edges enhancements, altered color information or different frequency components provided by wavelet transforms, can be used to differentiate between almost similar features. It was found that the wavelet-based CNN achieved the higher detection performance. Overall, this performance illustrates the potential for a combined use of the manipulation methods and deep CNNs for airport security applications.
A Secure Information Transmission Scheme for the Cluster Blockchain of the Internet of Vehicles

In recent years, with the popularity of blockchain, many of its applications have been gradually introduced into the Internet of vehicles (IoV) and cloud computing. Due to the decentralized nature of IoV and blockchain, they are very suitable for each other's architecture. However, blockchain is a flat framework. This study considers how to import the cluster blockchain framework to improve the efficiency of blockchain when the Internet of vehicles forms a cluster or group architecture. In addition, the data of Internet of vehicles and cloud computing are exposed to the public network, how to protect the transmission data from being changed. This study proposes the elliptic curve digital signature (ECDSA) communication protocol to protect the transmission data of Merkle tree node. The ECDSA digital signature is used to sign the transaction block to ensure the security of data transmission in the Internet of vehicles.
Improving Text Simplification with Factuality Error Detection

In the past few years, the field of text simplification has been dominated by supervised learning approaches thanks to the appearance of large parallel datasets such as Wikilarge and Newsela. However, these datasets suffer from sentence pairs with factuality errors which compromise the models’ performance. So, we proposed a model-independent factuality error detection mechanism, considering bad simplification and bad alignment, to refine the Wikilarge dataset through reducing the weight of these samples during training. We demonstrated that this approach improved the performance of the state-of-the-art text simplification model TST5 by an FKGL reduction of 0.33 and 0.29 on the TurkCorpus and ASSET testing datasets respectively. Our study illustrates the impact of erroneous samples in TS datasets and highlights the need for automatic methods to improve their quality.
DexBERT: Effective, Task-Agnostic and Fine-Grained Representation Learning of Android Bytecode

The automation of an increasingly large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). One foundational building block in the application of ML to software artifacts is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">representation</i> of these artifacts ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , source code or executable code) into a form that is suitable for learning. Traditionally, researchers and practitioners have relied on manually selected features, based on expert knowledge, for the task at hand. Such knowledge is sometimes imprecise and generally incomplete. To overcome this limitation, many studies have leveraged representation learning, delegating to ML itself the job of automatically devising suitable representations and selections of the most relevant features. Yet, in the context of Android problems, existing models are either limited to coarse-grained whole-app level ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , apk2vec) or conducted for one specific downstream task ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , smali2vec). Thus, the produced representation may turn out to be unsuitable for fine-grained tasks or cannot generalize beyond the task that they have been trained on. Our work is part of a new line of research that investigates effective, task-agnostic, and fine-grained universal representations of bytecode to mitigate both of these two limitations. Such representations aim to capture information relevant to various low-level downstream tasks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , at the class-level). We are inspired by the field of Natural Language Processing, where the problem of universal representation was addressed by building Universal Language Models, such as BERT, whose goal is to capture abstract semantic information about sentences, in a way that is reusable for a variety of tasks. We propose DexBERT, a BERT-like Language Model dedicated to representing chunks of DEX bytecode, the main binary format used in Android applications. We empirically assess whether DexBERT is able to model the DEX <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">language</i> and evaluate the suitability of our model in three distinct class-level software engineering tasks: Malicious Code Localization, Defect Prediction, and Component Type Classification. We also experiment with strategies to deal with the problem of catering to apps having vastly different sizes, and we demonstrate one example of using our technique to investigate what information is relevant to a given task.
Binarization of Metaheuristics: Is the Transfer Function Really Important?

In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm. Different binarization schemes are presented and a set of actions, which combine different transfer functions and binarization rules, under a selector based on reinforcement learning is proposed. The experimental results show that the binarization rules have a greater impact than transfer functions on the performance of the algorithms and that some sets of actions are statistically better than others. In particular, it was found that sets that incorporate the elite or elite roulette binarization rule are the best. Furthermore, exploration and exploitation were analyzed through percentage graphs and a statistical test was performed to determine the best set of actions. Overall, this work provides a practical approach for the selection of binarization schemes in binary combinatorial problems and offers guidance for future research in this field.
Leveraging ChatGPT for Real-Time Decision-Making in Autonomous Systems

The advent of autonomous systems has revolutionized industries ranging from transportation to manufacturing. One of the key challenges in autonomous systems is real-time decision-making, which requires a balance between complex data analysis and swift action. This paper explores the application of ChatGPT, a state-of-the-art language model, in addressing this challenge by leveraging its natural language understanding capabilities for real-time decision-making in autonomous systems. The paper begins by discussing the fundamental role of decision-making in autonomous systems, highlighting the need for rapid and informed choices to ensure safe and efficient operation. It then introduces ChatGPT and its unique ability to comprehend and generate human-like text. By fine-tuning ChatGPT with domain-specific data and integrating it into autonomous systems, the model can effectively interpret real-time data inputs and provide context-aware recommendations. The implications of leveraging ChatGPT for real-time decision-making are profound, as it not only streamlines decision processes but also allows for intuitive human-machine interaction. The paper concludes by highlighting the significance of collaborative research and development efforts between AI and autonomous systems experts to maximize the benefits of integrating ChatGPT into the decision-making fabric of autonomous technologies.
Geochemistry of Sabkhas in Abu Ghraib, Western Baghdad, Iraq

The study aims to detail the chemistry of Sabkha located in Abu-Graib, western Baghdad to know the content of the rare elements distributed. Sabkhas are found in dry areas with significant evaporation rates. The quantity of dissolved salts rises as water evaporates from the shallow basins, eventually causing salt to crystallize. The creation of the distinctive salt pans and crusts on the top is greatly influenced by this process. The trace elements (Ti, V, Cr, Mn, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Y, Zr, Nb, Mo, Sn, Ba, Ta, Tl, Pb, Th, U, I, and Br) beside the major oxides were analyzed using XRF technique. There are no clear significant differences in the distribution of the elements between the two Sabkhas in the village of Kadim Alaa’ab and the village of Haj Shiaa. Sabkhas are characterized by limited oxygen supplies in the brine, high temperatures, and precipitation of evaporite minerals including chlorides, sulfates, and sulfides. Halite predominates in the Sabkha sediments due to the association of chloride with Na2O. The CaO is distributed forming calcite, dolomite and gypsum as well as a small amount of clay minerals. The association of trace elements, especially Ni, Rb, Ta, Tl and Br, is mainly concentrated with clay minerals, followed by their concentration in silica, especially Y, Zr and Th. As for halite, it is not associated with trace elements.
What contribution do robots make to the care of older people? A scoping review

Background and research aim:The aim of this research was to identify the main benefits and barriers of using robotic technology to assist in activities of daily living (ADLs) in older people, as reported in empirical research exploring user perspectives.Most of the literature currently focuses on socially assistive companion robots.Methodology: A scoping literature review was conducted.Three bibliometric databases were searched covering the last 10 years.60 publications were included in the review.Results: Most studies were pilot studies and were conducted with participants who had no identified cognitive impairment.Studies reported mainly positive findings, with older people finding the robots easy to use, and with a high rate of acceptance.Nevertheless, several barriers were identified, and studies pointed to some potential social and psychological harms that could come from using these devices, such as increasing social isolation.Conclusions: Pilot studies suggest potential benefits associated with using robots for ADLs.At the same time, a number of barriers still remain.Costings (including social, psychological and financial) need to be undertaken to measure the usefulness of these devices.Further work must give more attention to the complex and contextual needs associated with a diverse patient base.
Frequency-insensitive spatiotemporal shaping of single photon in multiuser quantum network

Abstract Exploiting the fundamental features of quantum mechanics, an entanglement-based quantum network offers a promising platform for many dramatic applications such as multi-user cryptography. Nevertheless, the implementation of a large-scale quantum network in real-world scenarios remains challenging owing to the multiple scattering events in complex environment, particularly those frequency-sensitive scatterings that disturb quantum correlation both spatially and temporally. Here, we demonstrate the frequency-insensitive spatiotemporal control of entangled photons in a fully connected network by leveraging a Fourier-transform setup and the genetic algorithm. Such an approach can effectively improve the entanglement distribution process through a multimode fiber while the quantum characteristic of the network can be maintained well after the spatiotemporal shaping. Our scheme can serve as a bridging technology to establish entanglement between remote nodes of spectrally interconnected quantum systems and has great potential applications in future real-world quantum networks.
Abnormal Behavior Detection in Video Surveillance Using Inception-v3 Transfer Learning Approaches

The use of video surveillance systems has increased due to security concerns and their relatively low cost. Researchers are working to create intelligent Closed Circuit Television (CCTV) cameras that can automatically analyze behavior in real-time to detect anomalous behaviors and prevent dangerous accidents. Deep Learning (DL) approaches, particularly Convolutional Neural Networks (CNNs), have shown outstanding results in video analysis and anomaly detection. This research paper focused on using Inception-v3 transfer learning approaches to improve the accuracy and efficiency of abnormal behavior detection in video surveillance. The Inception-v3 network is used to classify keyframes of a video as normal or abnormal behaviors by utilizing both pre-training and fine-tuning transfer learning approaches to extract features from the input data and develop a new classifier. The UCF-Crime dataset is used to train and evaluate the proposed models. The performance of both models was evaluated using accuracy, recall, precision, and F1 score. The fine-tuned model achieved 88.0%, 89.24%, 85.83%, and 87.50% for these measures, respectively. In contrast, the pre-trained model obtained 86.2%, 86.43%, 84.62%, and 85.52%, respectively. These results demonstrate that transfer learning using Inception-v3 architecture can effectively classify normal and abnormal behaviors in videos, and fine-tuning the weights of the layers can further improve the model's performance. Index Terms— Abnormal behavior detection, Video surveillance, Deep learning, Transfer learning, InceptionV3.