Reviewerly/exHarmony huggingface dataset:
Penggunaan Gadget untuk Orang Berkebutuhan Khusus

&#x0D; &#x0D; &#x0D; &#x0D; Recently, the use of gadgets, especially in the form of smartphones for various activities, has increased rapidly, from studying to working, all of which are done with gadgets. However, for some groups of people in Indonesia, one of them is people with disabilities or people with disabilities having difficulty using gadgets like other people use in general, some of them have difficulty operating gadgets when they have to be used directly like ordinary people do. The essence of this material is that there are accessibility features in gadgets, especially smartphones, along with their functions and how to activate the features through the default smartphone settings used. Accessibility gadgets are application features to make it easier for users to operate them. Accessibility options may vary by device and by individual operating system version. Today's gadgets have included accessibility application features in them, and this can make it easier for users to use their gadgets according to user needs. Because not all of these features cannot be activated automatically or there are even some gadgets that do not have the built-in accessibility features as needed, users must know how to install, activate, and use the features of this accessibility application.&#x0D; &#x0D; &#x0D; &#x0D;
EVALUATION OF THE LAUNDRY SCLEAN PARTNER'S WEBSITE USABILITY USING THE USABILITY SCALE SYSTEM METHOD

SCLEAN (Smart Cleaning) is a mobile device made to connect laundry with laundry service users. Mobile SCLEAN devices are also designed with two sides, namely from the side of the laundry service user using the mobile version and from the laundry service provider side using the Website version. The laundry admin website is a very important component in connecting laundry and laundry service users. Researchers want to conduct research that aims to evaluate the performance of the SClean laundry partner admin website and are expected to provide suggestions that are used as a reference for the improvement and development of the SClean laundry partner website by NuCom. By using the System Usability Scale method, it shows that the evaluation results have a score of 59, the value is included in the marginal category in grade D with an OK rating and website improvement recommendations for improving service features on the website and optimizing performance on the website.
ANALISIS PERFORMA WEBSITE SCLEAN MENGGUNAKAN PINGDOM TOOLS DAN PAGE SPEED INSIGHTS

: Team Nucom launched the SClean laundry application to provide services and facilitators for laundry service users. The SClean application can provide service and services to customers who need laundry sevice online through online access to overcome obstacles to washing clothes on time. SClean can be accessed via this link “https://sclean.org/” using a smarthphone via a browser and also directly download the application via this Playstore. Tested on the SClean website using pingdom tools and page speed insights to collect data results that can be analyzed in terms of speed and performance measurements. The test results using pingdom tools are 80 while using page speed insights are 98.
The Path of Cultivating Applied Accounting Talents Based on DES Model under Big Data, Intelligentization, Mobile Internet and Cloud Computing

Under the Big Data, Intelligentization, Mobile Internet and Cloud Computing, the accounting and the accounting profession face new opportunities and difficulties, and the new standards for accounting expertise are established. Because of the imbalance between supply and demand, there is currently an excess and oversupply of accounting skills in China. In order to deal with this challenge, this paper proposes the path of training applied accounting talents based on the DES model. On this basis, it examines the current situation of insufficient accounting talent cultivated by accounting higher education in China from a perspective of supply, and analyzes the society's demand for accounting talent under Big Data, Intelligentization, Mobile Internet and Cloud Computing from a perspective of demand. Then, it is found that the supply cannot match the demand effectively. Thus, in the Big Data, Intelligentization, Mobile Internet and Cloud Computing environment, this paper proposes the paths of reconstructing accounting talent cultivation goals, systematically adding information-based curriculum, optimizing teaching evaluation mechanism, focusing on faculty construction, establishing teaching modes of student participation, and stressing cross-border capability cultivation.
"Time to Exceed the Physical World to Reach the Quantum Dimension"

Eda Alemdar* Author Affiliations Faculty of Medicine, VN Karazin Kharkiv National University, Ukraine Received: January 12, 2021 | Published: February 11, 2022 Corresponding author: Eda Alemdar, Faculty of Medicine, VN Karazin Kharkiv National University, Ukraine DOI: 10.26717/BJSTR.2022.41.006666
Noisy Sorting Capacity

Sorting is the task of ordering $n$ elements using pairwise comparisons. It is well known that $m=\Theta(n\log n)$ comparisons are both necessary and sufficient when the outcomes of the comparisons are observed with no noise. In this paper, we study the sorting problem in the presence of noise. Unlike the common approach in the literature which aims to minimize the number of pairwise comparisons $m$ to achieve a given desired error probability, we consider randomized algorithms with expected number of queries $\mathsf{E}[M]$ and aim at characterizing the maximal ratio $\frac{n\log n}{\mathsf{E}[M]}$ such that the ordering of the elements can be estimated with a vanishing error probability asymptotically. The maximal ratio is referred to as the noisy sorting capacity. In this work, we derive upper and lower bounds on the noisy sorting capacity. We establish two lower bounds, one for fixed-length algorithms and one for variable-length algorithms. The proposed algorithms exploit the connection between noisy searching and channel coding with feedback and incorporate the insertion sort algorithm with the Burnashev-Zigangirov algorithm for channel coding with feedback. Moreover, we derive an upper bound for the noisy sorting capacity and an upper bound for the achievable rates of algorithms based on insertion sort.
Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Public Health

The promise of visualization recommendation systems is that analysts will be automatically provided with relevant and high-quality visualizations that will reduce the work of manual exploration or chart creation. However, little research to date has focused on what analysts value in the design of visualization recommendations. We interviewed 18 analysts in the public health sector and explored how they made sense of a popular in-domain dataset. in service of generating visualizations to recommend to others. We also explored how they interacted with a corpus of both automatically- and manually-generated visualization recommendations, with the goal of uncovering how the design values of these analysts are reflected in current visualization recommendation systems. We find that analysts champion simple charts with clear takeaways that are nonetheless connected with existing semantic information or domain hypotheses. We conclude by recommending that visualization recommendation designers explore ways of integrating context and expectation into their systems.
KARAKTERISTIK KLINIS GLAUKOMA PRIMER SUDUT TERTUTUP DI PUSAT MATA NASIONAL RUMAH SAKIT MATA CICENDO PADA TAHUN 2020

Clinical Characteristics of Primary Angel Closed Glaucoma in the National Eye Center of Cicendo Eye Hospital in 2020.Primary angle-closure glaucoma (PACG) is a disease of damage to the optic nerve due to closure of the anterior chamber angle with the main risk factor is an increase in IOP which causes narrowing of the visual field and decreased visual function.Glaucoma management aims to lower IOP.This study aims to describe the characteristics of PACG at PMN Cicendo Eye Hospital Bandung in 2020 based on gender, regional distribution, profession, age, chief complaint, laterality, diagnosis, visual acuity, C/D ratio, IOP, risk factors, comorbidities, management, and duration of control.The study used a descriptive cross-sectional design, using secondary data through medical records of glaucoma patients at Cicendo Eye Hospital Bandung in the period of January 2020 -December 2020.The sample of this study was 176 patients and 290 eyes diagnosed with PACG.The results of this study showed that PACG was more common in women (64.2%) with an average age of 61 years.The majority diagnosed in two eyes (64.8%) with PACG (58.6%) as the most common diagnosis.Decreased vision (70.5%) was the most common chief complaint.The patient's average IOP was 32.7 and most patients (47,24%) came with visual acuity below 3/60.Hypertension (40.9%) is the most common comorbidity.Medicines (95.9%) were given to almost all patients with trabeculectomy was the most preferred procedure done to the patients.Most patients had control for 1 month (36.9%) with control for 12 months had the lowest average IOP in the last control, 20.36.
A secure and efficient multi-domain data sharing model on consortium chain

Abstract In a large-scale networking scenario with massive distribution of devices, data is independently generated and maintained by multiple domains. In order to solve the problem of isolated data island in multi-domain, this paper designs a multi-domain data sharing model based on consortium chain. Aiming at the problems of low efficiency and the less consideration of intra-domain consensus processes in traditional data sharing schemes, this paper optimizes the security and efficiency of data sharing from two parts: inter-domain chain and intra-domain chain. In order to ensure the fine-grained, dynamic and secure access strategy in inter-domain chain, this model combines the attribute-based access control model (ABAC) with smart contract, and a permission grading mechanism is designed to solve the problem of low retrieval efficiency in this access control model through reducing the retrieval range by grading and matching Policy set. In view of the problem of high delay of node consensus in the large-scale networking environment of intra-domain chain, a Layered Practical Byzantine Fault Tolerance optimization (LPBFT) algorithm with introducing a reputation model is designed, which greatly improves the efficiency and security of the intra-domain consensus. The consortium chain model is designed and implemented from the perspectives of inter-domain and intra-domain, which optimized the security and efficiency of the multi-domain data sharing model. Finally, this paper implements a prototype system with the Hyperledger Fabric platform, and verifies the validity of this model from the access control within the inter-domain and the consensus algorithm within the intra-domain separately.
Reduction of DNNs in edge computing

Abstract Application of Deep Neural Networks (DNN) in Edge Computing has emerged as a consequence of the need of real time and distributed response of different devices in a large number of scenarios. For this end, shredding these original structures is urgent due to the high number of parameters needed to represent them. As a consequence, the most representative components of different layers are kept in order to maintain the network's accuracy as close as possible from the entire network's ones. To do so, two different approaches have been developed in this work. First, Sparse Low Rank Method (SLR) has been applied to two different Fully Connected (FC) layers to watch their effect on the final response, and similarly the method has been applied to one of these layers. In the contrary, in this last case, the relevances of the previous FC layer's components were considered taking into account the connections to each of the components from the other FC layer, considering the relationship of relevances across layers. Experiments had been carried out in well-known architectures to conclude whether the relevances throughout layers have less effect on the final response of the network than the independent relevances intra-layer.
Cloud Computing Forensic Challenges for Law Enforcement

Cloud computing is a relatively new concept that offers the potential to deliver scalable elastic services to many. The notion of pay-per use is attractive and in the current global recession hit economy it offers an economic solution to an organizations' IT needs. Computer forensics is a relatively new discipline born out of the increasing use of computing and digital storage devices in criminal acts (both traditional and hi-tech). In the last decade computer forensics has developed in terms of procedures, practices and tool support to serve the law enforcement community. However, it now faces possibly its greatest challenges in dealing with cloud computing. Through this paper we explore these challenges and suggest some possible solutions. Keywords: Forensic, Cybercrime, law Enforcement, Digital Universe, Cyberspace, Security BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Samuel Opuni Boateng (2022): Cloud Computing Forensic Challenges for Law Enforcement Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 339-344 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P54
Device Information for Forensic Analysis

This document provides an overview of information for forensic analysis related to device name, serial number and model of devices used to connect to social networking platforms. The increasing use of social networking applications on smartphones makes these devices a gold mine for forensic researchers. Potential evidence can be captured on these devices and recovered with the right tools and research methods. The increasing proliferation of network devices in homes and buildings increases the possibilities of finding digital traces relevant to an investigation, physical or virtual: cyber-attacks, identity theft, etc. connected to the network can also find useful traces on the devices themselves found or stored in an associated cloud account that can be identified by device ID, model, and serial number. Keywords: Digital Forensics; Device Information, Evidence, Storage, Cyber forensics framework. BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Paul Antwi (2022): Device Information for Forensic Analysis Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 373-378 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P59
Simple O(1) Query Algorithm for Level Ancestors

This note describes a very simple O(1) query time algorithm for finding level ancestors. This is basically a serial (re)-implementation of the parallel algorithm of Berkman and Vishkin (O.Berkman and U.Vishkin, Finding level-ancestors in trees, JCSS, 48, 214--230, 1994). Although the basic algorithm has preprocessing time of O(n log n), by having additional levels or using table lookup, the preprocessing time can be reduced to almost linear or linear. The table lookup algorithm can be built in O(1) parallel time with $n$ processors and can also be used to simplify the parallel algorithm of Berkman and Vishkin and make it optimal.
Analysis of the deletions of DOIs: What factors undermine their persistence and to what extent?

Digital Object Identifiers (DOIs) are regarded as persistent; however, they are sometimes deleted. Deleted DOIs are an important issue not only for persistent access to scholarly content but also for bibliometrics, because they may cause problems in correctly identifying scholarly articles. However, little is known about how much of deleted DOIs and what causes them. We identified deleted DOIs by comparing the datasets of all Crossref DOIs on two different dates, investigated the number of deleted DOIs in the scholarly content along with the corresponding document types, and analyzed the factors that cause deleted DOIs. Using the proposed method, 708,282 deleted DOIs were identified. The majority corresponded to individual scholarly articles such as journal articles, proceedings articles, and book chapters. There were cases of many DOIs assigned to the same content, e.g., retracted journal articles and abstracts of international conferences. We show the publishers and academic societies which are the most common in deleted DOIs. In addition, the top cases of single scholarly content with a large number of deleted DOIs were revealed. The findings of this study are useful for citation analysis and altmetrics, as well as for avoiding deleted DOIs.
Domain Adaptive Person Search

Person search is a challenging task which aims to achieve joint pedestrian detection and person re-identification (ReID). Previous works have made significant advances under fully and weakly supervised settings. However, existing methods ignore the generalization ability of the person search models. In this paper, we take a further step and present Domain Adaptive Person Search (DAPS), which aims to generalize the model from a labeled source domain to the unlabeled target domain. Two major challenges arises under this new setting: one is how to simultaneously solve the domain misalignment issue for both detection and Re-ID tasks, and the other is how to train the ReID subtask without reliable detection results on the target domain. To address these challenges, we propose a strong baseline framework with two dedicated designs. 1) We design a domain alignment module including image-level and task-sensitive instance-level alignments, to minimize the domain discrepancy. 2) We take full advantage of the unlabeled data with a dynamic clustering strategy, and employ pseudo bounding boxes to support ReID and detection training on the target domain. With the above designs, our framework achieves 34.7% in mAP and 80.6% in top-1 on PRW dataset, surpassing the direct transferring baseline by a large margin. Surprisingly, the performance of our unsupervised DAPS model even surpasses some of the fully and weakly supervised methods. The code is available at https://github.com/caposerenity/DAPS.
Is GPT-3 all you need for Visual Question Answering in Cultural Heritage?

The use of Deep Learning and Computer Vision in the Cultural Heritage domain is becoming highly relevant in the last few years with lots of applications about audio smart guides, interactive museums and augmented reality. All these technologies require lots of data to work effectively and be useful for the user. In the context of artworks, such data is annotated by experts in an expensive and time consuming process. In particular, for each artwork, an image of the artwork and a description sheet have to be collected in order to perform common tasks like Visual Question Answering. In this paper we propose a method for Visual Question Answering that allows to generate at runtime a description sheet that can be used for answering both visual and contextual questions about the artwork, avoiding completely the image and the annotation process. For this purpose, we investigate on the use of GPT-3 for generating descriptions for artworks analyzing the quality of generated descriptions through captioning metrics. Finally we evaluate the performance for Visual Question Answering and captioning tasks.
Error-Aware Spatial Ensembles for Video Frame Interpolation

Video frame interpolation~(VFI) algorithms have improved considerably in recent years due to unprecedented progress in both data-driven algorithms and their implementations. Recent research has introduced advanced motion estimation or novel warping methods as the means to address challenging VFI scenarios. However, none of the published VFI works considers the spatially non-uniform characteristics of the interpolation error (IE). This work introduces such a solution. By closely examining the correlation between optical flow and IE, the paper proposes novel error prediction metrics that partition the middle frame into distinct regions corresponding to different IE levels. Building upon this IE-driven segmentation, and through the use of novel error-controlled loss functions, it introduces an ensemble of spatially adaptive interpolation units that progressively processes and integrates the segmented regions. This spatial ensemble results in an effective and computationally attractive VFI solution. Extensive experimentation on popular video interpolation benchmarks indicates that the proposed solution outperforms the current state-of-the-art (SOTA) in applications of current interest.
Improving Adversarial Robustness via Mutual Information Estimation

Deep neural networks (DNNs) are found to be vulnerable to adversarial noise. They are typically misled by adversarial samples to make wrong predictions. To alleviate this negative effect, in this paper, we investigate the dependence between outputs of the target model and input adversarial samples from the perspective of information theory, and propose an adversarial defense method. Specifically, we first measure the dependence by estimating the mutual information (MI) between outputs and the natural patterns of inputs (called natural MI) and MI between outputs and the adversarial patterns of inputs (called adversarial MI), respectively. We find that adversarial samples usually have larger adversarial MI and smaller natural MI compared with those w.r.t. natural samples. Motivated by this observation, we propose to enhance the adversarial robustness by maximizing the natural MI and minimizing the adversarial MI during the training process. In this way, the target model is expected to pay more attention to the natural pattern that contains objective semantics. Empirical evaluations demonstrate that our method could effectively improve the adversarial accuracy against multiple attacks.
Self-Distilled Vision Transformer for Domain Generalization

In the recent past, several domain generalization (DG) methods have been proposed, showing encouraging performance, however, almost all of them build on convolutional neural networks (CNNs). There is little to no progress on studying the DG performance of vision transformers (ViTs), which are challenging the supremacy of CNNs on standard benchmarks, often built on i.i.d assumption. This renders the real-world deployment of ViTs doubtful. In this paper, we attempt to explore ViTs towards addressing the DG problem. Similar to CNNs, ViTs also struggle in out-of-distribution scenarios and the main culprit is overfitting to source domains. Inspired by the modular architecture of ViTs, we propose a simple DG approach for ViTs, coined as self-distillation for ViTs. It reduces the overfitting of source domains by easing the learning of input-output mapping problem through curating non-zero entropy supervisory signals for intermediate transformer blocks. Further, it does not introduce any new parameters and can be seamlessly plugged into the modular composition of different ViTs. We empirically demonstrate notable performance gains with different DG baselines and various ViT backbones in five challenging datasets. Moreover, we report favorable performance against recent state-of-the-art DG methods. Our code along with pre-trained models are publicly available at: https://github.com/maryam089/SDViT.