Reviewerly/exHarmony huggingface dataset:
Perancangan sistem informasi akuntansi penjualan tunai pada konveksi Zunrock dengan vb.net metode RAD

The problem with sales cash receipt information system is a common problem that occurs in a company, the problems that arise occur because accumulation data with manual bookkeeping and the large number of cash receipts from sales which result in the data storage process being not well organized. The purpose of this study was to development accounting information system at Zunrock Apparel Convection. Researchers obtained data using interview techniques, literature study, and observation. The tools used in this survey are an interview form, a list of questions, and notes. In this research, the mechanism used system design stage. Method used is the RAD method. Testing cash receipts applications with Vb.Net program used black box testing method. Black box testing or also known as Behavioral Testing is a test carried out to observe input and output of the software without knowing code structure of the software. Based on results of design and test results of the zunrock apparel convection cash receipt application with Vb.Net program, it was concluded that use of the Rapid Application Development (RAD) method made cash receipt application design process more accurate and structured. This is because RAD method must be carried out sequentially based on predetermined forms.
Analysis of Multiplication Based on Vedic Mathematics

The usual segmentation methods were addressed here: edge detection, clustering, and region growth. The problem with segmentation is that no one approach is suited for a certain type of picture, nor are all techniques applicable to all images. The importance of multiplication is rising according to the technology process. It is one of the major applications which is used in various domains such as business and digital signal process etc. The way of general multiplication is much more time taking process. So, mainly as to remove this time delay in this paper the concept of Vedic mathematics is introduced. This paper introduced about Indian ancient Vedic mathematics is involved to clear those problems. Sixteen sutras among the Vedic mathematics the one sutra that is widely used and implemented is “URDHVA-TIRYAGBHYAM” is proposed. The multiplication process of two 4 bit and two 3 bit and also two 2-bit multiplications are gone through this paper. This process ultimately leads to the consumption of less time and faster implementation. But, with respect to the order of multiplication, the complexity will also increase. When compared to other multipliers it takes less area.
Gesture Controlled Mobile Robot with the Collaboration of MATLAB and PROTEUS

Gesture recognition is a type of technology that can be used to control robots, manipulators, and other devices. Human-Computer Interaction (HCI) and research into creating simple-to-use interfaces by utilising natural communication have gotten a lot of press recently, as have human manipulation skills. When using gesture recognition, the user who is making the gesture is not necessary to wear any equipment or gadgets on their body. A camera linked to a device reads the body’s gestures and produces an output. The major purpose of this paper is to use MATLAB to control motor direction in proteus while also combining both software with VSPE (visual serial port emulator). Before immediately implementing this method on a robot, we can first simulate the outcomes to improve accuracy or correct models.
DarthYOLO: Using YOLO for Real-Time Image Segmentation

Image segmentation is a common use case of image processing. It enables a wide variety of activities, from self-driving cars to traffic systems that are capable of governing them. Most state of the art models like Mask R-CNN and other transformer-based models perform well but have very high inference times. Therefore in this paper, a novel model architecture is proposed that can run on consumer grade hardware while giving near real time image segmentation. This is done by creating masks on regions of interest proposed by a lightweight object detection algorithm that is the YOLOv4. Such an algorithm allows for image segmentation in near-real time for resource constrained environments as compared to other state of the art models that have high inference times are unsuitable for deployment in said environments.
Steganalysis of Continuous Variable Quantum Steganographic Protocols

Steganography is the study of concealing and sending a secret message by making the eavesdropper unaware of its presence. First ever attempt to propose steganography via quantum key distribution (QKD) was made by Martin in his paper where BB84 QKD was used for communicating steganographic messages. The protocol have recently been found to be insecure against a detection based steganalysis attack which uses ambiguous quantum state discrimination. In this work, we extend the previous methods to develop new continuous variable quantum steganographic protocols using QKD and analyze their performance against steganalysis.
IoT BASED SECURED HEALTHCARE USING 6G TECHNOLOGY AND DEEP LEARNING TECHNIQUES

From 2040 forward, the entire healthcare industry will be dominated by the 6G communication revolution. It will control the whole medical industry in addition to numerous other fields. Medical services will be totally dependent on artificial intelligence and 6G communication technology, which will alter our perception of the way of life. Right now, reality presents the main impediments to clinical consideration, and 6G means to kill these obstructions. Similarly, 6G will be presented as a game-changing technology for medical services. In light of this, we foresee the healthcare system for the era of 6G communication technology. In this paper, we talk about the reception of AI with the Internet of Things (IoT) in the medical services framework and its effect on a few clinical fields.
2D Image Object Detection Aided by Generative Adversarial Networks: A Literature Review

Object Detection (OD) is one of the most critical tasks in 2D image processing. The researchers proposed multiple math models and frameworks based on Deep Convolutional Networks, such as R-CNN, SSD, and YOLO are the most common. Generative Adversarial Nets (GAN) represent a prominent field of study in machine learning, and it has been applied to many tasks with exciting results. This work aims to assess the potential of GANs applied to OD tasks and the proposed frameworks as a field of study. The methodology used was a systemic review of 14 papers. The conclusion shows that although OD and GANs are popular themes, there are not many developments in the intersection of both subjects. Therefore, OD with GAN-applied tasks is an excellent field to explore in future works.
Fast and Light-Weight Answer Text Retrieval in Dialogue Systems

Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available. The state-of-the-art technology for neural dense retrieval or re-ranking involves deep learning models with hundreds of millions of parameters. However, it is difficult and expensive to get such models to operate at an industrial scale, especially for cloud services that often need to support a big number of individually customized dialogue systems, each with its own text corpus. We report our work on enabling advanced neural dense retrieval systems to operate effectively at scale on relatively inexpensive hardware. We compare with leading alternative industrial solutions and show that we can provide a solution that is effective, fast, and cost-efficient.
Quantum Optimal Control: Practical Aspects and Diverse Methods

Quantum controls realize the unitary or nonunitary operations employed in quantum computers, quantum simulators, quantum communications, and other quantum information devices. They implement the desired quantum dynamics with the help of electric, magnetic, or electromagnetic control fields. Quantum optimal control (QOC) deals with designing an optimal control field modulation that most precisely implements a desired quantum operation with minimum energy consumption and maximum robustness against hardware imperfections as well as external noise. Over the last two decades, numerous QOC methods have been proposed. They include asymptotic methods, direct search, gradient methods, variational methods, machine learning methods, etc. In this review, we shall introduce the basic ideas of QOC, discuss practical challenges, and then take an overview of the diverse QOC methods.
Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations

DeepFake is becoming a real risk to society and brings potential threats to both individual privacy and political security due to the DeepFaked multimedia are realistic and convincing. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and failed in blocking the disinformation spreading in advance. To address this limitation, researchers study the proactive defense techniques by adding adversarial noises into the source data to disrupt the DeepFake manipulation. However, the existing studies on proactive DeepFake defense via injecting adversarial noises are not robust, which could be easily bypassed by employing simple image reconstruction revealed in a recent study MagDR. In this paper, we investigate the vulnerability of the existing forgery techniques and propose a novel \emph{anti-forgery} technique that helps users protect the shared facial images from attackers who are capable of applying the popular forgery techniques. Our proposed method generates perceptual-aware perturbations in an incessant manner which is vastly different from the prior studies by adding adversarial noises that is sparse. Experimental results reveal that our perceptual-aware perturbations are robust to diverse image transformations, especially the competitive evasion technique, MagDR via image reconstruction. Our findings potentially open up a new research direction towards thorough understanding and investigation of perceptual-aware adversarial attack for protecting facial images against DeepFakes in a proactive and robust manner. We open-source our tool to foster future research. Code is available at https://github.com/AbstractTeen/AntiForgery/.
Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining

The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills in order to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. Additionally, we release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning. The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSI-level multi-class circle glomerular detection results (which can be directly manipulated with ImageScope), (2) glomerular image patches with segmentation masks, and (3) different lesion types. To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining. The GGS fine-grained classification model achieved a decent performance compared with baseline supervised methods while only using 10% of the annotated data. The glomerular detection achieved an average precision of 0.627 with circle representations, while the glomerular segmentation achieved a 0.955 patch-wise Dice Similarity Coefficient (DSC).
Perencanaan Taman Islami Berbasis EDEN (Educational-Dynamical-Ecological-Natural) di Pondok Pesantren Tahfidz Qur’an Dewan Dakwah Lampung

Islamic Garden Planning Based on EDEN (Educational-Dynamical-Ecological-Natural) in Pondok Pesantren Tahfidz Qur’an Dewan Dakwah Lampung. Pondok Pesantren Tahfidz Qu’an Dewan Dakwah Lampung is one of the centers of religious education in Kecamatan Kemiling, Bandar Lampung. As an educational area in a strategic region, PPTQ Dewan Dakwah Lampung has the potential to make Bandar Lampung even more advanced. Reciprocity between the government and residents is needed in structuring the PPTQ Dewan Dakwah Lampung related to optimization the land use. The research aims to plan a landscape that can create a quality place to support activities and fulfill user needs. This research used a survey and descriptive qualitative methods. The data were collected through field observation, interview, and literature study, for further were analyzed refers to the complete design process. The research results direct the landscape to an EDEN-based concept (Educational-Dynamical-Ecological-Natural) included in Islamic Placemaking in EDEN (I-PLAN). The application of the elements presented in the site plan or 3D models is adjusted to the concept of an Islamic Garden. By applying I-PLAN can be a safe and comfortable means of education, contemplation, recreation, and behavior guide for users.
On the Value of ML Models

We argue that, when establishing and benchmarking Machine Learning (ML) models, the research community should favour evaluation metrics that better capture the value delivered by their model in practical applications. For a specific class of use cases -- selective classification -- we show that not only can it be simple enough to do, but that it has import consequences and provides insights what to look for in a ``good'' ML model.
Pixel-wise Deep Image Stitching

Image stitching aims at stitching the images taken from different viewpoints into an image with a wider field of view. Existing methods warp the target image to the reference image using the estimated warp function, and a homography is one of the most commonly used warping functions. However, when images have large parallax due to non-planar scenes and translational motion of a camera, the homography cannot fully describe the mapping between two images. Existing approaches based on global or local homography estimation are not free from this problem and suffer from undesired artifacts due to parallax. In this paper, instead of relying on the homography-based warp, we propose a novel deep image stitching framework exploiting the pixel-wise warp field to handle the large-parallax problem. The proposed deep image stitching framework consists of two modules: Pixel-wise Warping Module (PWM) and Stitched Image Generating Module (SIGMo). PWM employs an optical flow estimation model to obtain pixel-wise warp of the whole image, and relocates the pixels of the target image with the obtained warp field. SIGMo blends the warped target image and the reference image while eliminating unwanted artifacts such as misalignments, seams, and holes that harm the plausibility of the stitched result. For training and evaluating the proposed framework, we build a large-scale dataset that includes image pairs with corresponding pixel-wise ground truth warp and sample stitched result images. We show that the results of the proposed framework are qualitatively superior to those of the conventional methods, especially when the images have large parallax. The code and the proposed dataset will be publicly available soon.
Maintaining AUC and $H$-measure over time

Measuring the performance of a classifier is a vital task in machine learning. The running time of an algorithm that computes the measure plays a very small role in an offline setting, for example, when the classifier is being developed by a researcher. However, the running time becomes more crucial if our goal is to monitor the performance of a classifier over time. In this paper we study three algorithms for maintaining two measures. The first algorithm maintains area under the ROC curve (AUC) under addition and deletion of data points in $O(\log n)$ time. This is done by maintaining the data points sorted in a self-balanced search tree. In addition, we augment the search tree that allows us to query the ROC coordinates of a data point in $O(\log n)$ time. In doing so we are able to maintain AUC in $O(\log n)$ time. Our next two algorithms involve in maintaining $H$-measure, an alternative measure based on the ROC curve. Computing the measure is a two-step process: first we need to compute a convex hull of the ROC curve, followed by a sum over the convex hull. We demonstrate that we can maintain the convex hull using a minor modification of the classic convex hull maintenance algorithm. We then show that under certain conditions, we can compute the $H$-measure exactly in $O(\log^2 n)$ time, and if the conditions are not met, then we can estimate the $H$-measure in $O((\log n + \epsilon^{-1})\log n)$ time. We show empirically that our methods are significantly faster than the baselines.
Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks

This paper presents our latest effort on improving Code-switching language models that suffer from data scarcity. We investigate methods to augment Code-switching training text data by artificially generating them. Concretely, we propose a cycle-consistent adversarial networks based framework to transfer monolingual text into Code-switching text, considering Code-switching as a speaking style. Our experimental results on the SEAME corpus show that utilising artificially generated Code-switching text data improves consistently the language model as well as the automatic speech recognition performance.
ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts

Named entity recognition (NER) is an important task that aims to resolve universal categories of named entities, e.g., persons, locations, organizations, and times. Despite its common and viable use in many use cases, NER is barely applicable in domains where general categories are suboptimal, such as engineering or medicine. To facilitate NER of domain-specific types, we propose ANEA, an automated (named) entity annotator to assist human annotators in creating domain-specific NER corpora for German text collections when given a set of domain-specific texts. In our evaluation, we find that ANEA automatically identifies terms that best represent the texts' content, identifies groups of coherent terms, and extracts and assigns descriptive labels to these groups, i.e., annotates text datasets into the domain (named) entities.
Multivariate Goppa codes

In this paper, we introduce multivariate Goppa codes, which contain as a special case the well-known, classical Goppa codes. We provide a parity check matrix for a multivariate Goppa code in terms of a tensor product of generalized Reed-Solomon codes. We prove that multivariate Goppa codes are subfield subcodes of augmented Cartesian codes. By showing how this new family of codes relates to tensor products of generalized Reed-Solomon codes and augmented codes, we obtain information about the parameters, subcodes, duals, and hulls of multivariate Goppa codes. We see that in certain cases, the hulls of multivariate Goppa codes (resp., tensor product of generalized Reed-Solomon codes), are also multivariate Goppa codes (resp. tensor product of generalized Reed-Solomon codes). We utilize the multivariate Goppa codes to obtain entanglement-assisted quantum error-correcting codes and to build families of long LCD, self-dual, or self-orthogonal codes.
Centroid-UNet: Detecting Centroids in Aerial Images

In many applications of aerial/satellite image analysis (remote sensing), the generation of exact shapes of objects is a cumbersome task. In most remote sensing applications such as counting objects requires only location estimation of objects. Hence, locating object centroids in aerial/satellite images is an easy solution for tasks where the object's exact shape is not necessary. Thus, this study focuses on assessing the feasibility of using deep neural networks for locating object centroids in satellite images. Name of our model is Centroid-UNet. The Centroid-UNet model is based on classic U-Net semantic segmentation architecture. We modified and adapted the U-Net semantic segmentation architecture into a centroid detection model preserving the simplicity of the original model. Furthermore, we have tested and evaluated our model with two case studies involving aerial/satellite images. Those two case studies are building centroid detection case study and coconut tree centroid detection case study. Our evaluation results have reached comparably good accuracy compared to other methods, and also offer simplicity. The code and models developed under this study are also available in the Centroid-UNet GitHub repository: https://github.com/gicait/centroid-unet
5th Place Solution for VSPW 2021 Challenge

In this article, we introduce the solution we used in the VSPW 2021 Challenge. Our experiments are based on two baseline models, Swin Transformer and MaskFormer. To further boost performance, we adopt stochastic weight averaging technique and design hierarchical ensemble strategy. Without using any external semantic segmentation dataset, our solution ranked the 5th place in the private leaderboard. Besides, we have some interesting attempts to tackle long-tail recognition and overfitting issues, which achieves improvement on val subset. Maybe due to distribution difference, these attempts don't work on test subset. We will also introduce these attempts and hope to inspire other researchers.
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals

It is a consensus that small models perform quite poorly under the paradigm of self-supervised contrastive learning. Existing methods usually adopt a large off-the-shelf model to transfer knowledge to the small one via distillation. Despite their effectiveness, distillation-based methods may not be suitable for some resource-restricted scenarios due to the huge computational expenses of deploying a large model. In this paper, we study the issue of training self-supervised small models without distillation signals. We first evaluate the representation spaces of the small models and make two non-negligible observations: (i) the small models can complete the pretext task without overfitting despite their limited capacity and (ii) they universally suffer the problem of over clustering. Then we verify multiple assumptions that are considered to alleviate the over-clustering phenomenon. Finally, we combine the validated techniques and improve the baseline performances of five small architectures with considerable margins, which indicates that training small self-supervised contrastive models is feasible even without distillation signals. The code is available at \textit{https://github.com/WOWNICE/ssl-small}.