Reviewerly/exHarmony huggingface dataset:
Pengembangan Sekolah SMA/SMK Yapim Taruna Marelan Dengan Sistem Informasi Akademik Berbasis Web

Academic information system is one of the systems used in managing academic data that is applied in computer technology. This research aims to develop Yapim Taruna Marelan High School / Junior High School with a web-based academic information system, because it is related to those in Yapim Junior High School Maruna still using a manual-based academic system. High School / Junior High School Yapim Taruna Marelan, then formulates the problems that exist in this school, merging data, doing system design, and the final implementation program. The benefit of this research is to provide convenience from the school in processing data, both school data, teachers, students and others, academic information systems can provide facilities for schools, teachers and school citizens in accessing information about the school, and can be used as a media for delivering more effective and efficient information that can be accessed anywhere and anytime online, both for schools, students and the community.
Morph-Net: End-to-End Prediction of Nuclear Morphological Features from Histology Images

Analysis using morphological features of different types of nuclei have been shown to be useful for many different tasks in computational pathology. To obtain morphological features of nuclei in an image, a necessary first step in previous studies has been to apply a segmentation (and/or classification) method. The features are then calculated by using the obtained segmentation and classification maps. Although the segmentation and classification models may perform well on pixel-wise segmentation and classification tasks, they are not optimized for obtaining the morphological features. In this paper, we present Morph-Net, an end-to-end model that can directly predict morphological features of different types of nuclei for a given input image. We show that, the morphological features predicted directly by Morph-Net are more accurate compared to calculating them after a segmentation and classification step.
Task-Agnostic Continual Hippocampus Segmentation for Smooth Population Shifts

Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing. We explore how such methods perform in a task-agnostic setting that more closely resembles dynamic clinical environments with gradual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validation on two scenarios of hippocampus segmentation shows that our proposed method reliably maintains performance on earlier tasks without losing plasticity.
Radar Signal Sorting Based on Adaptive SOFM and Coyote optimization

In modern electronic warfare, the radar signal sorting method plays an important role in the electronic support measurement system. However, most of the traditional methods based on unsupervised clustering require prior information, such as the initial category centers and numbers, which has limitations in practical applications. To solve the problems mentioned above, a two-stage radar signal sorting method is proposed, which combines an improved self-organizing feature map (SOFM) network and coyote optimization algorithm, (i.e., SOCOA). In the first stage, the improved SOFM network is used to roughly sort the radar signals, and obtains the approximate number of categories and cluster center position of the input data. In the second stage, the coyote optimization algorithm is used to finely optimize the sorting process to obtain optimal results with the prior knowledge of the first stage. Experimental results show that our proposed method can improve the sorting performance without any prior information.
An Improved Tracking Method for Distributed Passive Sonar System

Underwater target tracking by distributed passive sonar system can be regarded as a time-sequence target state parameter estimation problem with multiple observation equations. Currently, the commonly used Bayesian tracking methods mainly include Kalman filter tracking and particle filter tracking methods. However, When the target is maneuvering or located in the fuzzy region of array direction finding, the performance of traditional tracker will decrease sharply and even become invalid. Aiming at the target tracking problem of passive distributed sonar observation system under the above situation, a Bayesian tracking method with radial velocity constraint is described in this paper, and an improved tracking algorithm is proposed, which is verified by simulation and SWELLEx-96 experiment data.
Research on Multiscale Pedestrian Detection Algorithm

To solve object detection issues in the field of surveillance video, including a low recognition rate and a high false alarm rate caused by the great change of scale and different degrees of occlusion, we propose a multiscale pedestrian detection method. A Res-spp module based on residual structure is proposed to fuse the local and global features of objects after the backbone network. Besides, the CIOU_LOSS is employed as an object box position loss to promote the stability of the object’s bounding boxes regression. In addition, we make a neck structure that involves Weight_Conat mechanism designed for cross-layer multiscale feature fusion connection, which is based on learnable weights. It aims to strengthen the capability of network multiscale feature integration. Experimental results on VOC pedestrian dataset and widerperson dataset not only show the accuracy of the proposed method, 87.9% and 86.69% respectively, but also demonstrates a noticeable effect on the improvement of transformation of different scale and the occlusion problem of pedestrians.
Improving Visibility of images - Underwater by Modeling of Back Light and Optimization of the Transmission Map

Light absorption in the water medium causes degradation and distortion of under-water images. This study uses the technique is image restoration. It restores the images captured and adjusts the color gradient level of the images of underwater. Underwater photographs are not decorated and do not hide naturally. Therefore, to avoid this, various image restoration methods are used to retrieve the original image. This results suggest that the proposed approach has significantly enhanced the visibility and reliability of images in underwater. Two parameters are considered for the image enhancement that is BckL (Background Light) and TrMap (Transmission Map). Color and Contrast adjustment can be developed for the enhancement of the underwater image. The proposed method is developed by enhancing the color and contrast of the image by using the annotation of image manually. A database with the annotated image (ABckL) compared with the histogram model of underwater image statistical analysis is performed. TrMap is estimated roughly for the R channel with the new dark underwater (NDUI) images via a statistical model. Light attention (LA) and underwater reverse saturation (URS) are applied to NDUI to modify the course of the R channel to improve the image enhancement. The difference between red Channel (R) and channel- G-B estimates the TrMap of G-B channel. The white balance method at the end stages improves the color and contrast of the image. Compared with other methods, the proposed method produced has achieved high accuracy, short computation time, color improvement.
Extended Reality as the Immersive Environment Conjunction of Augmented Reality, Virtual Reality, and Mixed Reality

Extended reality environments will exceed virtual reality and mixed reality environments and will be understood as a new level among several of the metaverse modalities that will become technology standards on the internet of the next future. Extended reality will address as well how the user's interface will experience dramatic changes in the way users manipulate digital 3D content. Interactive objects will adopt a holographic status. The use of natural tactile interactions determines the availability of our body as an integral interface that will obtain responses in several levels of perceptions, one of which is haptics. To create the meaningful experience of our body and the possible responses obtained from a spatial interaction or interface inside this metaverse, the content and its design must achieve a fully immersive dimension. In this crucial crossroad of body and interface interactions, phenomenological experiences will be vital to understand contemporary problematics and cultural changes.
Enhanced Ultrasound Breast Cancer Classification Based on Sparse Data and Two Customized Deep Learning Approaches

Breast cancer is one of the leading causes of death worldwide. Ultrasound images can be seen as an extremely convenient way to diagnose breast tumors, as it can be used alone or in conjunction with a mammogram to determine the nature of a breast lesion. It is a non-ionizing technique, harmless, and very accessible. In the era of artificial intelligence, Diagnosis Aided Systems (CAD) are mostly used to help smart radiologists' interpretation. For this purpose, this manuscript implements two common Deep Learning (DL) approaches to Breast Ultrasound (BUS) images and evaluates their performances in comparison to state-of-the-art results. Two publicly ultrasound breast cancer datasets have been used to compile the final classification model. Dataset consists of a total of 897 images, 537, and 360 images labelled as benign and malignant respectively. Promising results have been obtained from both DL techniques. The custom CNN classifier model has scored 92.53% accuracy and 90.83% sensitivity with a false-positive rate of 6.33%. For Transfer Learning (TL) approach, well-known pre-trained models have been used as feature extractors in addition to a basic classifier built on top. InceptionResNetV2 has been the best-scoring model for this approach, with 91.19% accuracy, 86.38% sensitivity, and a very low false-positive rate of 4.28%. VGG16 and InceptionV3 models also seem to outperform a study in the literature.
Fuzzy Logic and the Greatest Common Divisor on Audio-Based Data Hiding Method

With the massive development of information technology, all data cannot be assumed to be safe from illegal parties. Data can have a very high vulnerability to being accessed by the public, even if the data are private or confidential. In order to overcome this problem, it is necessary to have a method to hide confidential data or information that others should not know. Many methods introduced have not overcome common problems in data hiding, namely the low quality produced after the embedding process. This study summarizes the problems and proposes a method that focuses on improving the quality of stego-audio files by developing a fuzzification method using Fuzzy Logic and the Greatest Common Divisor (GCD) to produce better audio quality. The final result shows that the stego-audio quality improves as evaluated using a Peak Signal-to-Noise Ratio (PSNR) without additional steps such as smoothing or payload distribution.
Research on Video Object Tracking Based on Improved Camshift Algorithm

Target tracking is one of the important research directions of computer vision, widely used in intelligent surveillance, pedestrian detection, military war, and other fields. In this paper, the Camshift algorithm is improved based on multi-feature fusion. Aiming at the problem that the color of the tracking target is similar to that of the background, the Local Binary Pattern feature is partly improved and adaptively fused with the template generated by the joint chromaticity and saturation components. As it is easy to lose the target in the case of occlusion, the improved algorithm incorporates the Kalman filter. According to the statistics of the experimental results, the improved algorithm achieves a better tracking effect.
Construction of Intelligent Art Guidance System based on Intelligent Robot-Assisted Painting Equipment

This paper combines user portrait recognition technology, scene recognition technology and voice interaction technology to realize student attendance, classroom teaching interaction, and voice interaction on the Android platform. Connect the Raspberry Pi and Arduino with a camera. After the camera recognizes the image, it draws the corresponding pattern on the whiteboard. Through our equipment, a more natural and scientific human-computer interaction teaching mode is realized, the D-H modeling method is used to establish the mathematical model of the painting robot, and the robot kinematics is analyzed and verified using the Robotics Toolbox toolbox based on the MATLAB platform.
Assessing receptive vocabulary using state‑of‑the‑art natural language processing techniques

Abstract Semantic embedding approaches commonly used in natural language processing such as transformer models have rarely been used to examine L2 lexical knowledge. Importantly, their performance has not been contrasted with more traditional annotation approaches to lexical knowledge. This study used NLP techniques related to lexical annotations and semantic embedding approaches to model the receptive vocabulary of L2 learners based on their lexical production during a writing task. The goal of the study is to examine the strengths and weaknesses of both approaches in understanding L2 lexical knowledge. Findings indicate that transformer approaches based on semantic embeddings outperform linguistic annotations and Word2vec models in predicting L2 learners’ vocabulary scores. The findings help to support the strength and accuracy of semantic-embedding approaches as well as their generalizability across tasks when compared to linguistic feature models. Limitations to semantic-embedding approaches, especially interpretability, are discussed.
Security of plug-and-play continuous-variable quantum key distribution

Subject of study. Two-way continuous-variable quantum key distribution schemes are studied. Aim. This study aims to prove the security of the plug-and-play continuous-variable quantum key distribution system in the context of the model of trusted noise from legitimate users. Method. Based on an analog of the well-known implementation in the plug-and-play format, the performance of such a system operating under the GG02 protocol is evaluated. The evaluation is conducted in the presence of collective attacks, considering the finite-key effects. Main results. A secure key that satisfies the composability criteria can be distributed with a channel loss of 5 dB, which is expected to be approximately half that in a one-way scheme. Practical significance. Two-way quantum key distribution schemes are more stable than one-way schemes, and hence, their stability requires detailed consideration. This paper is the first, to our knowledge, to propose considering the security of a two-way implementation of continuous-variable quantum key distribution, considering the trusted equipment noise.
Perceptions of Technical Debt and its Management Activities - A Survey of Software Practitioners

Technical Debt (TD) is a metaphor reflecting technical compromises that can yield short-term benefits but might hurt the long-term health of a software system. Although several research efforts have been carried out, TD-related literature indicates that Technical Debt Management (TDM) is still incipient. Particularly in software organizations, there is still a lack of knowledge regarding how practitioners perceive TD and perform TDM in their projects. Our research focuses on characterizing TD and its management under the perspective of practitioners. For doing so, we conducted an online survey with 120 participants from 86 different organizations located in 5 different countries. Our results indicate that TD conception is widespread among more than 70% of respondents. Most of them (72%) recognized its importance and impact on software artifacts, being able to provide a valid example of three different TD Types (i.e., Design, Code, and Architectural). In addition, at least 65% of respondents consider TD identification, TD Repayment, and TD prevention as TDM activities in the spotlight. However, less than 15% adopt formal approaches to support these activities. This paper contributes to TD discussion and TDM activities by showing the practitioner’s perspective. Finally, further research will support observing how effective and efficient TDM activities can be in different contexts.
An efficient lightweight encryption model with re‐encryption scheme to create robust blockchain architecture for<scp>COVID</scp>‐19 data

Abstract Handling electronic health records from the Internet of Medical Things is one of the most challenging research areas as it consists of sensitive information, which targets attackers. Also, dealing with modern healthcare systems is highly complex and expensive, requiring much secured storage space. However, blockchain technology can mitigate these problems through improved health record management. The proposed work develops a scalable, lightweight framework based on blockchain technology to improve COVID‐19 data security, scalability and patient privacy. Initially, the COVID‐19 related data records are hashed using the enhanced Merkle tree data structure. The hashed values are encrypted by lattice based cryptography with a Homomorphic proxy re‐encryption scheme in which the input data are secured. After completing the encryption process, the blockchain uses inter planetary file system to store secured information. Finally, the Proof of Work concept is utilized to validate the security of the input COVID based data records. The proposed work's experimental setup is performed using the Python tool. The performance metrics like encryption time, re‐encryption time, decryption time, overall processing time, and latency prove the efficacy of the proposed schemes.
A Performance Comparison of Commercial Speech Recognition APIs in Noisy Environments

This paper compares the performance of five commercial speech recognition APIs under noisy environments, namely those provided by Amazon AWS, Microsoft Azure, Google, Kakao, and Naver. To this end, we used an open dataset for development and evaluation of multi-channel noise processing technology provided in AI Hub. We tested each API’s performance with respect to the speaker’s gender and location and the speech content, and measured their error rate using both word error rate (WER) and character error rate (CER). Except for the AWS API, the error rate was higher when tested with female’s data than male’s one, and when tested with the data recorded from the side than the front. The error rate was also relatively high when the test sentences contained proper nouns such as person’s names and local names, and the shorter the sentences, the higher the error rate. Moreover, the Google API outperformed all the others in terms of both WER and CER, with 53% and 18% of error rate, respectively.
Unbalanced Learning for Diabetes Diagnosis Based on Enhanced Resampling and Stacking Classifier

Diabetes is characterized by an abnormally enhanced concentration of glucose in the blood serum. It has a damaging impact on several noble body systems. Today, the concept of unbalanced learning has developed considerably in the domain of medical diagnosis, which greatly reduces the generation of erroneous classification results. The paper takes a hybrid approach to imbalanced learning by proposing an enhanced multimodal meta-learning method called IRESAMPLE+St to distinguish between normal and diabetic patients. This approach relies on the Stacking paradigm by utilizing the complementarity that may exist between classifiers. In the same focus of this study, a modified RESAMPLE-based technique referred to as IRESAMPLE+ and the SMOTE method are integrated as a preliminary resampling step to overcome and resolve the problem of unbalanced data. The suggested IRESAMPLE+St provides a computerized diabetes diagnostic system with impressive results, comparing it to the principal related studies, reflecting the design and engineering successes achieved.
Developing a Component Comment Extractor from Product Reviews on E-Commerce Sites

Consumers often read product reviews to inform their buying decision, as some consumers want to know a specific component of a product. However, because typical sentences on product reviews contain various details, users must identify sentences about components they want to know amongst the many reviews. Therefore, we aimed to develop a system that identifies and collects component and aspect information of products in sentences. Our BERT-based classifiers assign labels referring to components and aspects to sentences in reviews and extract sentences with comments on specific components and aspects. We determined proper labels based for the words identified through pattern matching from product reviews to create the training data. Because we could not use the words as labels, we carefully created labels covering the meanings of the words. However, the training data was imbalanced on component and aspect pairs. We introduced a data augmentation method using WordNet to reduce the bias. Our evaluation demonstrates that the system can determine labels for road bikes using pattern matching, covering more than 88% of the indicators of components and aspects on e-commerce sites. Moreover, our data augmentation method can improve the macro-F1-measure on insufficient data from 0.66 to 0.76.
Par Pharm., Inc. v. Eagle Pharms., Inc.

Biotechnology Law ReportVol. 41, No. 5 Par Pharm., Inc. v. Eagle Pharms., Inc.United States Court of Appeals of the Federal Circuit, 2022 No. 2021-2342, 2022 WL 3449817Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMANEdited for Biotechnology Law Report by CHRISTOPHER M. HOLMANChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.*The synopsis is original content and the decision itself has been condensed for ease of readability.Search for more papers by this authorPublished Online:11 Oct 2022https://doi.org/10.1089/blr.2022.29283.cipAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 41Issue 5Oct 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN.Par Pharm., Inc. v. Eagle Pharms., Inc..Biotechnology Law Report.Oct 2022.258-261.http://doi.org/10.1089/blr.2022.29283.cipPublished in Volume: 41 Issue 5: October 11, 2022Online Ahead of Print:September 23, 2022PDF download