Reviewerly/exHarmony huggingface dataset:
A New Generative Replay Approach for Incremental Class Learning of Medical Image for Semantic Segmentation

Deep neural networks suffer from the notorious problem of catastrophic forgetting when the tasks keep increasing. The inaccessibility of previous data due to privacy limitations and other issues directly leads to a significant drop in performance in prior tasks. Existing incremental class learning (ICL) methods in semantic segmentation are mostly regularization-based. While in this work, we incorporate a generative replay-based approach to alleviating catastrophic forgetting for the first time. We introduce SegGAN to generate both previous images and the corresponding pixel-level labels to circumvent privacy limitations and replay them to retain learned knowledge in the subsequent learning steps. Furthermore, we propose a novel filtering mechanism to select high-quality generated data by the consistency constraint of the Pseudo-Labeling and generative replay method. Specifically, we use Pseudo-Labeling to obtain the pseudo-labels of the generated images and select reliable data with high confidence by comparing generated labels with pseudo-labels.
An Improved Deep Text Clustering Via Local Manifold of an Autoencoder Embedding

Text clustering is a method for separating specific information from textual data and can even classify text according to topic and sentiment, which has drawn much interest in recent years. Deep clustering methods are especially important among clustering techniques because of their high accuracy. These methods include two main components: dimensionality reduction and clustering. Many earlier efforts have employed autoencoder for dimension reduction; however, they are unable to lower dimensions based on manifold structures, and samples that are like one another are not necessarily placed next to one another in the low dimensional. In the paper, we develop a Deep Text Clustering method based on a local Manifold in the Autoencoder layer (DCTMA) that employs multiple similarity matrices to obtain manifold information, such that this final similarity matrix is obtained from the average of these matrices. The obtained matrix is added to the bottleneck representation layer in the autoencoder. The DCTMA's main goal is to generate similar representations for samples belonging to the same cluster; after dimensionality reduction is achieved with high accuracy, clusters are detected using an end-to-end deep clustering. Experimental results demonstrate that the suggested method performs surprisingly well in comparison to current state-of-the-art methods in text datasets.
A Markerless 3D Pose Estimation Method for Mouse Using Multiple RGB-D Cameras

Research using animals plays an important role in basic research as well as in applied research such as the development of new drugs. In particular, the mouse is one of the most important experimental animals for medical research because of its small size and ease of breeding. However, they are often observed and evaluated by human eyes. This has some problems such as the subjective evaluation of the observer and human error. In order to solve this problem, it is necessary to have an objective and automatic analysis system. In recent years, several analysis systems have been developed. However, there are some problems in these systems, such as they can only classify behaviors roughly, and they require markers to be attached to mice. In this study, we developed a system that can perform detailed behavioral analysis of mice without adding markers using six RGB-D cameras.
Rancang dan Bangun Aplikasi Pemesanan Sayur dan Buah Berbasis Android

Technological development in Indonesia has increased quite rapidly, but many entrepreneurs have not fully utilized technology, including in the sale of vegetables and fruit, so that promotion and market reach is not optimal. The purpose of this study was to build a User Friendly Android-based fruit and vegetable ordering application using the Waterfall method and using the Black Box testing technique so that the application built was easy to use so that it covered a wider market reach. The result of this research is an android-based application in making sales and ordering, making it easier for people to buy and order vegetables and fruit.
Development of Simple Fractions Mathematics E-Module to Improve Understanding of Grade 3 Students in Online Learning at Elementary Schools in Sidoarjo Regency

During the COVID-19 pandemic for the 2020-2021 school year, Rangkah Kidul Elementary School carried out online learning. The online learning of simple fractions for grade 3 conducted at SDN Rangkah Kidul is less than optimal because it is more dominant in assignments and is teacher-centered. So that the ability of students to understand the material of simple fractions is lacking. For that we need interesting teaching materials that can be read and studied anywhere and anytime and can improve student understanding. This research aims to create and develop teaching material products in the form of e-modules. Therefore, this study examines the steps of e-module development, the feasibility of e-modules, and the effectiveness of the simple fractions mathematics e-module in improving the understanding of 3rd graders at SDN Rangkah Kidul. This research is a development research with ADDIE development model (Analysis, Design, Development, Implementation, Evaluation). Collecting data using questionnaires, tests, and documentation. The analysis technique uses a value comparison formula, pair sanple t test, and linear regression with the help of the SPSS 16 program. The results of the study explain the steps of e-module development which consist of analyzing needs and problems, compiling and planning learning objectives, developing products through feasibility tests, implementing products with field trials, and evaluate product use. Then the results of the feasibility of the e-module by material experts 87.14% and media experts 92.31% and field trials to 24 students of 94.91% meaning that the e-module is very feasible. Furthermore, there is the effect of using e-modules on increasing students' understanding with an increase in class average from 78.50 to 89.08 and the magnitude of the effect is 66.4%. In conclusion, the development of the simple fractions math e-module is feasible and effective to improve the understanding of grade 3 students in online learning at SDN Rangkah Kidul.
Towards Real- Time Classification of HPC Workloads via Out-of-band Telemetry

Detecting illicit workloads on High Performance Computing (HPC) systems is an important task. Such workloads might indicate a user account is being misused, for example to run cryptocurrency miners or password crackers. Existing solutions use in-band collection of data, which may slow down the workloads on the system. We present early results demonstrating that real-time classification of HPC workloads based on out-of-band telemetry may be possible. Further, we show a method-dependent accuracy of greater than 99 %, with the best results achieving near-perfect classification. These results suggest that creating a real-time classifier of HPC jobs that does not impact system performance is possible.
Improve embedded knowledge graph multi-hop question answering based on relational path

Multi-hop Knowledge Graph Question Answering (KGQA) requires reasoning about multi-hop inference relations between topic entities and answers on the knowledge graph(KG) and returning correct answers. The difficulty of obtaining the implied inference relations in multi-hop questions described in natural language and the sparse knowledge graph bring challenges to the multi-hop KGQA. In this paper, we propose an embedded knowledge graph multi-hop Q&A model based on relational paths, which exploits the relational chains in the knowledge graph and the semantic similarity of multiple questions to improve the accuracy of multi-hop KGQA task. The comparative experiments prove that our method significantly outperforms the state-of-the-art counterparts. Comprehensive ablation experiments also validate the effectiveness of our approach on multi-hop KGQA tasks.
Exploring the Limits

In this chapter, we will explore limits as they relate to programming language design in several different ways:
Infographics in Higher Education: Instructional Media for Students' Writing Proficiency

The majority of ESL/EFL students is that writing is challenging and frustrating. Limited vocabulary and low idea gathering appears to be most difficult issues in writing process. This research was quasi-experimental, with a control group that was only used for the post-test. This research aims to analyses the effectiveness of infographics as instructional media on students' writing proficiency. Two classes totalling 64 students made up the sample, which was drawn using random cluster sampling. A writing test served as the research instrument. An independent t-test was used to assess the data. The findings indicated that the experimental group's students were more proficient writers. The experimental group did better than the control group in terms of means (85.97 &gt; 83.48) and the t-test results (3.126 &gt; 3.125). In comparison to the conventional instruction, it was shown that using infographics as instructional media had a substantial impact on students writing proficiency. The effect size computation yielded a result of 0.78, which was examined in the Cohen's d table. In conclusion, infographics had a significant effect on students' writing proficiency as an instructional media.
Detecting Video Anomalous Events with an Enhanced Abnormality Score

Detecting video anomalous events is vital for human monitoring. Anomalous events usually contain abnormal actions with exaggerated motion and little motion. We define the former and the latter as dynamic anomalies and static anomalies, respectively. We define the video data of events where a few persons perform diverse actions indoors as Indoor Event Data (IED). Many frame prediction approaches have succeeded in detecting dynamic anomalies. However, they are prone to overlooking static anomalies in IED. To solve this problem, we propose an Enhanced Abnormality Score (EAS), which is a combination of prediction, dynamic, appearance, and motion scores. To specifically target static anomalies, we calculate a score to evaluate the dynamic degrees of actions. We use an appearance score of a frame to detect static anomalies from appearance. This score is generated from a clustering-based distance of a pre-trained CNN feature. We also use a motion score based on flow reconstruction to balance the appearance score. We conduct extensive experiments on two datasets involving indoor human activities. Quantitative and qualitative experimental results show that our proposal achieves the best performance among its variants and the state-of-the-art methods.
Using Modules

When developing more complex Python applications, as the program grows in size, it's a good idea to split it up into several files for easier maintenance and reusability of the code. To do this, we use modules.
Federated Tumor Segmentation with Patch-Wise Deep Learning Model

A chief challenge of deep learning in computer-aided diagnosis is to collect a large heterogeneous dataset from multiple hospitals for constructing a robust deep learning model, because of hospitals’ strict rules for sharing sensitive medical data. Federated learning (FL) allows collaborative learning among hospitals without sharing data to prevent a leakage of private information. FL, however, has issues of high computational demands and data shortage in local hospitals. To address these issues, we developed an FL framework coupled with a patch-wise deep learning model, a massive-training artificial neural network (MTANN), in tumor segmentation in CT. We performed experiments on the proposed MTANN-based federated tumor segmentation with a small-sized multisite liver tumor dataset. Our model achieved a Dice of 0.712 comparable to 0.723 by the gold-standard centralized training model, which was higher than 0.592 (P < 0.05) by the state-of-the-art Res-U-Net model. Furthermore, the proposed framework required a training time of 0.25 hours that was less than that of 5 hours by the Res-U-Net on a GPU server.
Region Based Non-Uniform Scheme for Effective Retargeting

With the invent of displays of varied features like bendable displays, twistable displays and slant displays the feed of these displays need to be adjusted so that useful objects of the scene are not destroyed. In general, if we want to project a particular scene of specified size after capturing on the display of other dimensions, image resizing is done. In image resizing, the whole image will be converted into dimension of the target display. In the process the whole image, including all the objects may be expanded or squeezed depending on the size of the target display. But, in image retargeting, in addition to changing the size of the image to suit the target display, some additional intelligence will be added there by preserving or retaining useful objects and removing unnecessary background information. In this paper, the global significance map will drive the retargeting which is formed by merging the local significance maps. The local significance maps are derived after forming regions by considering the average intensity levels locally. The simulation results clearly show the superiority of the scheme proposed
Artificial Intelligence for Prevention of Breast Cancer

This paper show a computational tool that allow the analysis of the information obtained from a database that contains features of the fine needle aspiration (FNA) procedure, whose goal is to diagnose breast masses applying artificial intelligence. The methodology include the recollection of data, process of cleaning, neural networks and Bayesian’s networks for prediction of the data, evaluation and comparison.
SPARQL and SKOS Enabled Multilingual Polyhierarchical Thesaurus : Designing a Framework

The goal of this work is to investigate a multilingual thesaurus with a polyhierarchical structure based on TemaTres to publish vocabulary on the web for the simple retrieval of multilingual information by users. It could easily retrieve related metadata from other sources to improve advanced library services. The TemaTres and LAMP Architecture built and created this integrated prototype framework on the Ubuntu platform. Apart from that, it investigates Google Translate for easy data entry in various terms and facets across many languages. It must configure vocabulary settings to build a multilingual enabled polyhierarchical structure based on SPARQL Endpoint and API services. The study's primary outcomes include the development of a multilingual integrated framework for easy retrieval of information on various terms and facets by users. A multilingual data entry framework on a specific topic can be created using Unicode and TemaTres. There are numerous scripts and strategies available for web publishing. This study is unique in that it focuses on creating and retrieving multilingual thesaurus based on polyhierarchical relationships. The library professionals and users will benefit significantly from these integrated vocabulary services. It makes SPARQL linked data facilities by providing a visual vocabulary and thesaurus construction interface based on Web 2.0 and API services.
Daric: A Storage Efficient Payment Channel with Punishment Mechanism

Lightning Network (LN), the most widely deployed payment channel for Bitcoin, requires channel parties to generate and store distinct revocation keys for all n payments of a channel to resolve fraudulent channel closures. To reduce the required storage in a payment channel, eltoo introduces a new signature type for Bitcoin to enable payment versioning. This allows a channel party to revoke all old payments by using a payment with a higher version number, reducing the storage complexity from $$\mathcal {O}(n)$$ to $$\mathcal {O}(1)$$ . However, eltoo fails to achieve bounded closure, enabling a dishonest channel party to significantly delay the channel closure process. Eltoo also lacks a punishment mechanism, which may incentivize profit-driven channel parties to close a payment channel with an old state, to their own advantage. This paper introduces Daric, a payment channel with unlimited lifetime for Bitcoin that achieves optimal storage and bounded closure. Moreover, Daric implements a punishment mechanism and simultaneously avoids the methods other schemes commonly use to enable punishment: 1) state duplication which leads to exponential increase in the number of transactions with the number of applications on top of each other or 2) dedicated design of adaptor signatures which introduces compatibility issues with BLS or most post-quantum resistant digital signatures. We also formalise Daric and prove its security in the Universal Composability model.
Classification-Based Diverse Workflows Scheduling in Clouds

Cloud workflow scheduling is a typical combinatorial optimization problem and becomes more challenging due to the increasing diversity of workflows. However, current research employs the same scheduling strategy on diverse workflows. In fact, a scheduling strategy may perform well on one workflow but poorly on other workflows owning to the unique characteristics of each workflow. Therefore, in practical applications, selecting suitable scheduling strategies for diverse workflows is a critical issue. To solve it, this paper investigates a diverse workflows scheduling problem and presents a classification-based workflow scheduling framework, which includes workflow parser, workflow classifier, workflow scheduler, resource manager and workflow status tracker, to manage and schedule diverse workflows using suitable strategies. Based on the framework, we propose a classification-based workflow scheduling algorithm (CWSA) to optimize the economic cost of workflow execution under deadline constraints. We conduct the experiments using diverse workflow instances randomly generated from five types of real-world workflows to evaluate the proposed CWSA approach. The results demonstrate the superiority of CWSA compared with the state-of-the-art approaches. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Diverse workflows (i.e., many workflows with various types, such as Montage, LIGO and Cybernetics) in clouds are widespread. How to efficiently schedule them in cloud is very important. This paper formulates the diverse workflows scheduling problem and proposes a CWSA to solve it. The basic idea of CWSA is to select a suitable scheduling strategy for each workflow. Specifically, in CWSA, we design a classification neural network architecture that consists of a graph neural network and a fully connected neural network to classify each workflow to its suitable deadline distribute strategy by its characteristics and deadline constraint. Then CWSA obtains the sub-deadlines of tasks and assigns tasks to appropriate VMs (Virtual Machines). Furthermore, as an important factor in workflow scheduling, the transmission time between dependent tasks is introduced into the graph neural network, which improves the classification accuracy.
Addressing Emerging Harm from Scams and Other New Technologies

This chapter of the monograph is devoted to topical issues of countering digital crime and minimizing the consequences of criminal activity. The study is of an overview nature; the author provides their typification of digital crime in relation to digital platforms, reveals the scientific component of this concept, and provides predictive information about the prospects for the development of digital “platform” crime and measures taken by states to counter it.
Consistent and Efficient Batch Operations for NoSQL Databases with Hybrid Timestamp

NoSQL databases, such as HBase or Cassandra employ weak consistency models to provide good scalability and availability. However, they often lack functionality that would help programmers reason about the correctness of their applications. Notably, they do not support consistent batch operations that could be used for important tasks, such as batch updates or maintaining secondary indexes. Some systems add transaction support to NoSQL databases. However, they often bring much overhead to existing single-row operations. This paper proposes an efficient algorithm for supporting batch operations on existing NoSQL databases. It reuses the existing local timestamp and adds a global timestamp to ensure batch operations’ consistency. Our implementation based on HBase shows that compared to transactional systems, our algorithm improves the throughput of batch operations by up to 2 $$\times $$ . Meanwhile, the latency of single-row operations only increases by around 12%. In comparison, other transactional systems increase their latency by over 3 $$\times $$ .
Unsupervised Domain Adaptation for Semantic Segmentation with Global and Local Consistency

Unsupervised domain adaptation(UDA) for semantic segmentation aims to learn from labeled synthetic data to segment the unlabeled real data. Many recent methods use generative networks to acquire real-like images for mitigating domain shift. However, these methods only ensure global style consistency between two domains and fail to impose pixel-wise constraint which is also referred to as local content consistency. To address the above problem, we propose a global and local consistency network to reduce the domain gap in unsupervised domain adaptation for semantic segmentation. To this end, we first constrain global style consistency through a generative adversarial network to acquire real-like latent domain images. Then we enhance local content consistency based on pixel-wise entropy minimization. Experimental results show that our method has superiority over other competitive methods on GTA5 $$\rightarrow $$ Cityscapes.
Cloud and AI-supported Mobile Information Multi-agent System for Smart Energy-saving

This study aims to explore a cloud and AI-supported mobile multi-agent system to quickly and precisely provide good quality and real-time information, provided by the Dr. What-Info system, for smart energy-saving. The illustrated system and experiments verify and validate the effectiveness and efficiency of the proposed system.
Explanation of Multi-Label Neural Networks with Layer-Wise Relevance Propagation

Neural networks are considered a black-box model as their strength in modeling complex interactions makes its operation almost impossible to explain. Still, neural networks remain very interesting tools as they have shown promising performance in various classification tasks. Layer-wise relevance propagation is a technique that, based on a propagation approach, is able to explain the predictions obtained by a neural network. In this work, we propose four adaptations of this technique to operate on multi-label neural networks. The proposed methods provide new ways of distributing the relevance between the output layer and the preceding ones. The efficacy of these adaptations is demonstrated after an experimental study. The study is carried out based on existing evaluation criteria in the literature that measure the explanation's quality. These methods are applied to a case study in which a neural network is used to detect secondary coinfections in patients infected with SARS-CoV-2. Overall, the proposed methods provide a post-hoc interpretability stage of the results.