Reviewerly/exHarmony huggingface dataset:
Optimizing New Student Admissions System Using Zachman Framework for Vocational High School: Case Study at SMK Al Hikmah Anjatan

Al Hikmah Anjatan Vocational School is located in Anjatan District, Indramayu Regency. Al Hikmah Anjatan Vocational School organizes a selection of new student admissions at the beginning of each new school year. At the acceptance of new students, they usually still use the conventional method, namely using paper. This admission process is inefficient and allows repetition of the same data and easily lost data which is a concern. The purpose of this research is to make a new online-based student admissions system at Smk Al Hikmah Anjatan to be more effective and accurate. In this case, a needs analysis is carried out in the form of a series of system modeling on the information system development plan using the Zachman Framework. In an effort to achieve the goals of Smk Al Hikmah Anjatan which are formulated in the vision and mission, it is necessary to design and determine an information architecture model to integrate information systems that suit business needs in schools, especially Smk Al Hikmah Anjatan. The results of this study indicate that the design of information systems based on the Zachman Framework can help develop a structured architecture for the admission of new students. This information system can be a foundation for Smk Al Hikmah Anjatan in facing fierce competition in the world of education, as well as increasing the efficiency and effectiveness of the new student admission process.
TD-NAAS: Template-Based Differentiable Neural Architecture Accelerator Search

This work presents TD-NAAS, a template-based differentiable approach towards the co-design of neural networks and hardware accelerator. Each neural operator is paired with the optimal hardware block that executes it efficiently, which is called a template. This approach reduces the search space by eliminating hardware design parameters and guarantees the most efficient accelerator. Evaluation results show that our method can build a neural network with higher accuracy and an accelerator with lower latency compared to the existing works.
A Performance-driven Neural Network Compiler for Multi-core Computing-In-Memory Accelerator

Nowadays, artificial neural networks(ANN) are being widely used in daily life, such as target detection, speech recognition, etc. Computing-In-Memory(CIM) has attracted much attention because of its low power consumption and high speed. In this paper, we propose a performance-driven neural network compiler for multi-core CIM-based accelerator. We provide a cycle accurate runtime simulator to evaluate the performance for neural network models deployed on target CIM accelerator, and generate executable codes accordingly. In addition, we propose two optimization heuristics to further improve the resulting inference performance for compiled ANN models. Experimental results show that the inference performance can be 3.5 times faster compared with baseline results on average.
Aesthetic QR Code With Basic Module Partitioning and Grayscale Transformation Method

As a type of two-dimensional barcode widely used in various scenarios, a QR code is an image composed of square black and white modules arranged in a specific way to record information. This paper proposes a method for beautifying QR codes by embedding trademarks or personal avatars without compromising the error correction ability of the QR code, based on the division of basic modules and gray-scale transformation. The method mainly consists of three steps: first, combining XOR of binary numbers with Gaussian-Jordan elimination to adjust the brightness/darkness order of the QR code modules, making each module match the corresponding module of the embedded image, resulting in a binary QR code image; second, changing the color and size of the bright and dark modules of the QR code to achieve simplicity and to match the theme color of the embedded image; third, a rendering algorithm is designed to handle the crucial pixels of binary QR codes in order to embed images within the QR codes. This approach enhances the visual quality of the QR codes while preserving their error correction capabilities. Experimental results show that the proposed method can resist some common attacks on QR codes and has a high success rate in scanning and decoding after beautification.
VST HMD system that instantly switches between rescue robot-mounted viewpoint and operator viewpoint

3D stereoscopic viewing system using a stereo camera and a HMD (Head Mounted Display) has been proposed as an information presentation system for operating a rescue robot. However, the HMD needs to be removed when the operator records the situation of the victim and obstacles on a recording sheet, which may cause stress to the operator and reduce the sense of realism. In this study, we focused on the fact that the VST (Video See-Through) HMD can see through the hardware to the real world, and constructed a system that instantly switches between the rescue robot-mounted viewpoint and the operator’s viewpoint. Experiments were conducted to compare the VST HMD and the HMD without VST in a task simulating the work required at a disaster site, and data were collected and statistically analysed on questionnaires regarding the time required for the task, recording accuracy, realism, and the user’s feeling of use.
臨床検査・バイオ実験作業者支援用マイクロチューブキャッパーの開発

The opening and closing operations of microtube's cap in clinical laboratory and biological experiment have the potential risks of contamination due to scattering and contact of specimens, and not only the accuracy of inspections and experiments, but also the safety of workers and experimenters. We have already developed a microtube capper for automated robot system of polymerase chain reaction (PCR) tests. There are needs for such microtube cappers not only in automation systems, but also in manual operations in biological safety cabinets. In this study, we propose a manually operated compact microtube capper under condition of opening and closing the cap without touching the microtube after microtube setting to the microtube capper. Demonstrations showed that experimenters or operators can safely and smoothly perform pipetting operations by tactile switch operations without touching the microtube while looking directly at the sample in the microtube.
Study on Robotic Palpation Hand

Palpation is a noninvasive and quick medical procedure that can be used to estimate disease by directly touching the affected area. Palpation is a skill passed down from master to apprentice through experience, and quantification of palpation and its reproduction by an autonomous robot have not been realized. In this study, to define palpation movements, we interviewed dermatologists who frequently perform palpation in their medical practice, and measured / analyzed the posture and finger pressure during palpation. As a result, the palpation movements of physicians was classified into five types ( "rubbing," "pushing," "pinching," "rocking," and "kneading") based on differences in the trajectory of the fingertips and the affected area condition. Thus the requirement specifications to develop robot hand was achieved.
価値反復を用いた移動ロボットによる屋外ナビゲーション

We examine our software package for mobile robot navigation in the real world. This software uses value iteration for updating a decision making policy in the whole state space. Though the computational cost of this method is too large for current computers, we expect that it will be standard in future. In the experiments, we use this package with a laptop computer on a mobile robot. Though the calculation time is still a problem, it works on the mobile robot for the first time.
Imitation of path-tracking behavior by end-to-end learning of vision and action

We investigate a method for offline learning of vision-based path-following behavior using pre-collected images and actions. Our previous method has learned such behavior online. The feature of the method is that it imitates the behavior generated by self-position estimation using a LiDAR sensor as an input to the behavior using vision as an input. However, it has been a problem that imitation learning requires a long training time. Therefore, we try to shorten the training time by using offline learning. Furthermore, we will clarify how much visual information around the path is required for applying the method to a real robot. As a result, we verified that the method shortens the training time. We also clarified the required visual information through experiments.
Accuracy-Tweakable Federated Learning with Minimal Interruption

Federated machine learning plays a significant role in pivotal industries such as health, finance and Internet-of-Things. Not needing to share training data makes it appealing for privacy preservation, which is crucial in these sectors. However, base-form federated learning does not offer adequate security guarantees, which has led to a diverse line of research to counteract the associated weaknesses. Existing work is inefficient when dealing with malicious clients, and accumulates varying levels of error from noisy intermediate models. In this paper, we present a federated learning protocol that utilizes a novel implementation of identifiable secret sharing together with learning with errors. The proposed protocol can effectively identify malicious clients, which allows only honest parameters to be obtained with minimal interruption to the training sequence. Furthermore, our protocol offers an accuracy tolerance mechanism that can be tweaked to suit the application. This prevents a residual noise level above the tolerance from degrading the intermediate model accuracy. In turn, it can ensure the end validation accuracy is above the desired level. We also show that our approach is lightweight on the clients, as we focus on efficient federated learning with smaller IoT devices.
Consortium Blockchain-based Community Electricity Trading with Arbitration

To reduce greenhouse gas emissions, the trading system in a community electricity market needs to consider both renewable and conventional energy resources. The intermittent nature of renewable power generation may lead to trading disputes. To promote renewable penetrations in community electricity markets, this paper develops trading and arbitration methods based on Hyperledger Fabric. A consortium blockchain-based community electricity trading system including both renewable and conventional energy resources has been designed. Fabric Gateway, a new service in Hyperledger Fabric that requires fewer connections and endorsements, is configured to increase the throughput of the trading system. A voting-based arbitration method used in the data market is applied with arbitration results and costs tuned for trading disputes of renewable energy resources. Testing results validate the functionality of market clearing, and demonstrate the efficiency of the system in terms of trading throughput and arbitration latency.
Enhancing Data Security in Networking Through AES Encryption and Steganography: A Comparative Analysis of DES and AES Algorithms

Information transfer has accelerated in the modern era. Numerous security difficulties were caused by the increasing transmission. The most popular methods to address this issue are cryptography and steganography. Steganography is the method of hiding secret data with a non- secret file to avoid data breaches whereas cryptography uses programs to encrypt and decrypt the data to make it secure. They both need to be combined so as to build a secure network that is not easily visible for hackers to breach as it looks like average data, despite the fact that these methods are frequently used together to reach higher degrees of security, a highly secure system is still required to transfer information through any communication channel while reducing the possibility of intrusion. In this paper, we suggest a data encryption system that makes use of effective AES and DES cryptography techniques. We present a way for secure data transfer by combining these encryption techniques with the technique of steganography. Our method effectively employs steganography techniques by inserting the encrypted text within an image's pixels. In addition to improving the security of data hiding compared to conventional techniques, this paper also enables us to assess the advantages and disadvantages of AES and DES in the context of data encryption and steganography.
PENDAMPINGAN PENERAPAN MANAJEMEN PENJADWALAN DALAM OPTIMALISASI PROSES PEMBELAJARAN

Scheduling learning activities is a routine activity every semester which is complicated and requires high accuracy. If the scheduling process is carried out inaccurately, there will be many implementation conflicts. In addition, learning scheduling must be completed on time so that the learning process can run according to the specified time. Likewise the scheduling process carried out at the Margarana Tourism Vocational School which has limited study space with a large number of subjects at all levels carried out each semester, this makes it difficult to maximize the use of classrooms. Through the Udayana Serving Program, assistance is provided for the management of the room arrangement system which can optimize the use for teachers of the Margarana Tourism Vocational School. The method of implementing community service is carried out by socialization, lectures, discussions and workshops in the form of training in the application system for setting up learning spaces. Evaluation results based on the implementation of the service program on testing the usability component of the system, that on average participants stated 58.67% agreed and 16% strongly agreed. Meanwhile, in the management application system standard test, 60% agreed and 20% strongly agreed. Based on the evaluation results, participants are able to understand and apply scheduling management, the scheduling system provides access for each teacher to set their own schedule and the leader can approve schedule submissions through the system.&#x0D; Keywords: Scheduling, Room, Vocational School, Learning Proces
DDDA Development of a Distributed De-Duplication Approach using Big Data Analysis in Hybrid Cloud Environment

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.
English Language and Linguistic Teaching Strategies Based on Deep Learning

This research paper investigates the potential of in corpora ting deep learning techniques into English language and linguistic teaching strategies. With the rapid advancements in deep learning and its proven success in various domains, exploring its application in language education holds significant promise. The paper reviews existing literature on deep learning and language processing, examines case studies of deep learning in educational settings, and proposes practical strategies for implementing deep learning in English language and linguistic instruction. The goal is to assess the benefits, challenges, and implications of using deep learning in language teaching and contribute to the ongoing conversation on innovative approaches to enhance language learning out comes. Deep learning, a subfield of machine learning, has witnessed remarkable advancements and demonstrated exceptional capabilities in various domains. In recent years, researchers and educators have started exploring the integration of deep learning technique using to language teaching methodologies. This research paper aims to investigate the potential benefits, challenges, and implementation strategies of using deep learning in English language and linguistic education. The paper reviews existing literature, examines case studies, and presents practical insights to enhance the effectiveness of language instruction through deep learning methodologies.
Linear Regressor Model for Internet of Things Automated Thermal Conditioning of a Smart Classroom Environment Using Limited dataset

This research paper presents a Linear Regressor (LR) modeling approach for automated thermal conditioning of smart spaces in a classroom environment using limited data constraints. Sensitive studies were performed in order to identify the breaking point of the LR model of polynomial-order-of-1 below which it will not be possible to obtain meaningful estimates and controllability actions, considering a range between 5% and 30% limited training data points. Based on several simulation experiments, it was found that the LR breaks at the 10% training data level. This result is valuable for recommender systems that may experience difficulty in obtaining enough primary dataset. Applications that provide early warning signals will also find this study useful.
Software Cost Estimation Using Temporal Data Mining Techniques: An Over View

Data mining is a process that involves extracting new, non-trivial information from huge datasets. This information can be used in a variety of applications. Several fields, including temporal pattern recognition, statistics, optimisation, temporal databases, high-performance computing, visualisation, and parallel computing, all meet in the rapidly developing field of temporal data mining. The growth of digital data and the processing power of computers have severely limited a field called data mining that integrates computer science and statistics. The software cost estimation category is one of data mining's disciplines. This study's primary objective is to comprehend the temporal mining methods currently in use in order to spot the gaps and flaws in the most recent software cost estimation technologies. This research also includes a concise summary of recent developments in the field of temporal data mining that has taken place recently. This study serves as a springboard for a discussion of the reasons why temporal data mining was required in the industry, which played a significant part in the development of the current generation of temporal data mining techniques for software cost assessment.
Tackling Climate Change With Artificial Intelligence

The Intergovernmental Panel on Climate Change's 6th Appraisal report, distributed in 2021, shows that the climate is getting warmed by about 2° F (1.1° C) since the 1780s, due to the emission of gases that traps heat. Climates alter is a noteworthy threat confronting humankind. To help, as the experts in machine learning, the depiction of the effectiveness of machine learning as a device in diminishing greenhouse gas emanations and making a difference in society adjusting to a changing climate. From keen frameworks to fiasco administration, we recognize high-impact issues where existing crevices can be filled by machine learning, in collaboration with other areas. Our proposals envelop energizing investigative questions as well as promising commerce openings. We are taking part in the global effort against climate change as members of the machine learning community. Buildings and cities, and specifically how to maximize a building's use of energy, is one of the many recommendations that has received the greatest attention. While a quarter of all energy-related emissions globally are attributable to buildings, the report claims that by combining straightforward adjustments with cutting-edge techniques, emissions from existing buildings might be reduced by up to 90%. To start this stretch, this statement attracted our attention. Find a way to manage, and subsequently optimize, the use of energy in buildings. After conducting a thorough investigation, we decided to focus our consideration on Non-Intrusive Stack Observing (NILM). The irregular relapse issue involving the creation of a network of neural for individual machines to forecast the window of time of a machine stack by comparing the time window of the overall data is referred to as Neural NILM. While NILM is the process of figuring out the control demands of multiple devices inside a building when several devices are monitored by an individual electric meter. (Abstract)
BAF-Net: Bidirectional Attention-aware Fluid Pyramid Feature Integrated Multi-modal Fusion Network for Prognosis

In this work, we proposed a bidirectional attention-aware fluid pyramid features integrated fusion network (BAF-Net) with cross-modal interactions for multi-modality medical images prognosis. The network is composed of two identical branches to preserve the unimodal feature and one paralleled bidirectional attention-aware distillation stream to assimilate cross-modal complements progressively and learn supplementary refined features in both the bottom-up and the top-down processes. The fluid pyramid connections were adopted to integrate the hierarchical features at different levels of deep neural network, and channel-wise attention modules were exploited to mitigate the cross-modal cross-level incompatibility. Furthermore, the depth-wise separable convolution was introduced to fuse the cross-modal cross-level features to alleviate the increase of parameters to a great extent. Better performance was obtained by the proposed BAF-Net compared to unimodal network for cancer prognosis in a public TCIA dataset (head & neck cancer (HNC) dataset composed of 800 patients from nine centers).
A Deep Learning Approaches for Natural Language Processing and Sentiment Analysis in Social Media

Twitter and Facebook are far more effective at disseminating information than other, more established social media platforms. As social media has grown, it has developed into a gold mine of information that companies as well as scholars can use to create models to analyze this repository and acquire data that can be used for tactics involving word-of-mouth advertising. For these rapid information exchanges, many Natural Language Processing (NLP) techniques that concentrate on identifying formal terms are inappropriate. On the other hand, the vocabulary used on social media is somewhat specialized and contains certain symbols. In this work, we present a novel deep learning-based methodology for social media sentiment analysis. From the data we collect, we build a dataset. After analysing these specific phrases, our goal is to produce a semantic dataset to aid future research. Retrieved data are greatly useful for future applications. Multiple platforms of social media are crawled using Python for gathering trial data.
Clustering ensemble based on sample’s representation

The clustering ensemble is formed by combining clustering analysis and ensemble learning. However, most clustering ensemble methods treat all samples equally, which negatively affects the final clustering result. To this end, we propose sample’s representation to evaluate the sample’s importance in clustering comprehensively. The sample’s representation measures the clustering importance of samples from two perspectives: the stability of the relationship between the sample and its neighbor samples and the closeness of the relationship between the sample and its neighbor samples. According to the representation of each sample, we divide a dataset into cluster core and cluster halo. Then we obtain the credible underlying structure through the cluster core samples. Finally, the cluster halo samples are gradually allocated to the above structure to get the final clustering result. The working steps of the algorithm are shown on two synthetic datasets, and experiments on nine real datasets fully demonstrate that the algorithm outperforms 11 other state-of-the-art clustering ensemble methods.