Reviewerly/exHarmony huggingface dataset:
News Classification Based On News Headline Using SVC Classifier

News gives new insight and information from all over the world. News has many categories, such as politic, economy, science, and other common news categories. Every news will have their own category based on its content. The classification of news is usually done manually by inputting the category during the news posting. Some of the categories may be inputted incorrectly. The news classifier can be the solution for problem, but the news classifications out there are usually based on the news content. The classifier will receive the word vector inputs that are taken from the news content and try to classify it into one of certain categories. Unfortunately, news contents can be longer and harder to be processed rather than processing the news headline. The news headline is shorter and packs a decent information for the classifier to find out what category it is. Besides the news headline usage, the classifier also needs to be chosen correctly. In this paper, the SVC model will be tested using the news headline data to classify the news and compare with several other models, such as Linear Regression, Multinomial Naive Bayes, Decision Tree, and Random Forest. The common variables to be compared are the accuracy, recall, and precision to evaluate the SVC model.
Autonomous Vehicle Guided with RFID Position Detection for Warehouse Management System

Finding the location of goods stored in the warehouse is part of the quality of the management warehousing system. Location goods usually always change or reposition to rearrange space in warehouses. Changes in the goods placement system have resulted in the location of goods being stored in the database. Retrieval of goods from the location requires an AGV (Automatic Guided Vehicle) which can work automatically based on data on the position of goods in the database. AGVs in warehouses use a lot of lanes on the warehouse floor as road guides. A search robot in the form of a car is developed by using data on the position of goods in the database as the direction of the robot's destination. The robot has Wi-Fi communication and uses RFID as a guide for the robot's path. RFID can be placed at certain points where the AGV needs direction assistance so that the robot does not rely on black lines. The movement of the robot is calculated through the actual position data from the RFID tag which is read when the robot passes the RFID and the position data of the item that has been given. The use of robots with the ability to detect their actual position is in accordance with the principles of a warehouse management system that requires automation and real-time data. So with this robot, automatic search media can be obtained, and improve management and storage in warehouses.
Improvement of Optimal-Ate Pairing on Cocks-Pinch Curve with Embedding Degree 6 in Affine Coordinates

In recent years, several advanced cryptographic protocols that provides practical security and privacy features have been proposed. Pairing-based cryptography is one of the next generation public-key encryption methods that realize the high performance cryptographic protocols. Elliptic curves used in the pairing-based cryptography include pairing friendly curves with a wide variety of parameters. As a type of pairing friendly curves, Guillevic et al. presented parameters for Cocks-Pinch curve with embedding degree <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=5,6,7,8$</tex> . Furthermore, Nanjo et al. proposed that the computational cost of the pairing on the Cocks-Pinch curve with embedding degree <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=6$</tex> is the second candidate for the BLS curve with embedding degree <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=12$</tex> at the 128-bit security level. Therefore, the authors propose and improve Optimal-Ate pairing on Cocks-Pinch curve with embedding degree <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=6$</tex> in Affine coordinates. Specifically, Miller's algorithm for Optimal-Ate pairing is optimized for Cocks-Pinch curves, and an effective Pseudo 4-sparse multiplication is derived and implemented. We adopt and implement a compressed squaring, which efficiently computes the squares to the final exponentiation of Optimal-Ate pairing. Optimal-Ate pairing on Cocks-Pinch curves in Affine coordinates using the above efficient method is found to be more efficient than previous Optimal-Ate pairing on Cocks-Pinch curves in Affine coordinates.
Implementation of Geographic Information System for Road Maintenance Management Application in Bandung District

Transportation is an important facility and infrastructure that connects activity and service centers, so that they are connected to each other. Good transportation can trigger economic, social, political development and the mobility of a region that grows together in various fields and sectors. The handling of road network maintenance is prioritized on the realization of good road performance and quality conditions to improve services, both services and distribution in advancing strategic sectors in Bandung District. So far, the Department of Public Works and Public Works of Bandung District for Road Sector is to determine and carry out road maintenance, at each UPT conducting surveys and screening issues/complaints on the road network, which are then used as the basis for budgeting maintenance. From this description, it is necessary to conduct research on the management of road network maintenance based on Geographic Information Systems (GIS) in Bandung District. The method used is a Geographic Information System which is a spatially based system that can prepare a management system for the maintenance and improvement of the road network in Bandung District efficiently and effectively which is expected to facilitate evaluation and can be updated regularly and continuously.
A Federated Learning Approach to Traffic Matrix Estimation using Super-resolution Techniques

Network measurement and telemetry techniques are central to the management of modern computer networks. Traffic matrix estimation is a popular technique that supports several applications. Existing approaches use statistical methods, which often make invalid assumptions about the structure of the traffic matrix. Data-driven methods, instead, leverage detailed information about the network topology that may be unavailable or impractical to collect. In this work, we propose a super-resolution technique for traffic matrix estimation that can infer fine-grained network traffic. In our experiment, we demonstrate that the proposed approach with high precision outperforms existing data interpolation techniques. We also expand our design by employing a federated learning model to address scalability and improve performance. We find that our model increases the accuracy of the inference with respect to its centralized counterpart.
Decentralized Federated Learning Strategy with Image Classification using ResNet Architecture

The rapid growth of both the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) results in a high demand for AI applications in devices. To achieve high levels of accuracy, AI applications typically require a large amount of annotated data. Accessing such data is challenging in various applications such as healthcare, finance and information security. Federated learning (FL) is one of the strategies that was proposed to overcome this challenge. Specifically, FL enables the AI model in the centralized system to be trained without any prior knowledge of the information on the devices. Recent FLs have the disadvantage that they are dependent upon a centralized system, and thus are susceptible to single points of failure. This paper proposes a strategy that employs FL in a decentralized environment where devices can communicate with each other to increase the accuracy of the AI model in each device. Furthermore, we evaluate the proposed strategy in the image classification task with the ResNet50 architecture and the CIFAR-10 dataset. The evaluation shows that the ResNet50 model trained in the decentralized environment can achieve comparable results to the model trained in the centralized environment.
Transfer Learning Based Efficient Traffic Prediction with Limited Training Data

Efficient prediction of internet traffic is an essential part of Self Organizing Network (SON) for ensuring proactive management. There are many existing solutions for internet traffic prediction using machine and deep learning techniques. But designing individual predictive models for each service provider in the network is challenging due to data heterogeneity, scarcity, and abnormality. Moreover, the performance of the deep sequence model in network traffic prediction with limited training data has not been studied extensively in the current works. In this paper, we investigated and evaluated the performance of the deep transfer learning technique in traffic prediction with inadequate historical data leveraging the knowledge of our pre-trained model. First, we used a larger real-world traffic dataset for source domain prediction based on five different deep sequence models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM Encoder-Decoder (LSTM_En_De), LSTM_En_De with Attention layer (LSTM_En_De_Atn), and Gated Recurrent Unit (GRU). Then, two best-performing models, LSTM_En_De and LSTM_En_De_Atn, from the source domain with an accuracy of 96.06% and 96.05% are considered for the target domain prediction. Finally, four smaller traffic datasets, collected for four different sources and destination pairs, are used in the target domain to compare the performance of the standard learning and transfer learning in terms of accuracy and execution time. According to our experimental result, transfer learning helps to reduce the execution time for most cases, while the model's accuracy is improved in transfer learning with a larger training session.
MobiDeep: Mobile DeepFake Detection through Machine Learning-based Corneal-Specular Backscattering

DeepFake has accomplished notable advancement with the AI-leveraged production and manipulation techniques of fictitious human facial images. Despite many benign and fun applications, the generated fake images can negatively influence the authenticity of online information by originating deception, manipulation, persecution, and seduction, defying societal quality and human rights, which becomes critical security and privacy threat in social networks. Hence, real-time DeepFake detection and limitation technologies on the mobile platform are essential to building a controlled, harmless DeepFake ecosystem. This paper presents a real-time, cloudless, lightweight mobile app for human visual DeepFake detection using machine learning technologies named MobiDeep (Mobile DeepFake Detection through Machine Learning-based Corneal-Specular Backseat-tering). MobiDeep stems from a hypothesis that the existing DeepFake creation methods, including replacement, editing, and synthesis, lack the ensemble with the reflective objects. Focusing on the most reflective area of a human face, corneal-specular backscatter images of eyes, we seek the similarity and consistency with multiple surrounding environment features, including color components, shapes, and textures. We have implemented a cross-platform mobile application to evaluate the performance using various input parameters and lightweight Deep Neural Network (DNN) architectures. The empirical results show that MobiDeep achieves high accuracy (98.7%) and rapid detection speed (less than 200 ms) in detecting sophisticated DeepFake images within a subsecond.
An Unsupervised Detection Approach for Location Attacks in Satellite-Based Navigation Data

Autonomous vehicles rely on environmental data from their in-built sensors. One critical piece of information is localization data from GPS signals. Unfortunately, adversaries have exploited specific vulnerabilities behind these signals. This work provides an unsupervised approach for detecting location change attacks to help security professionals identify compromised routes. This proposal is based on the k-means algorithm and discloses the generation of meta-features based on statistical features of navigation trajectories. Initial results show the proposed solution's suitability for large-scale anomalous events detection.
Disaggregated Micro Data Center: Resource Allocation Considering Impact of Network on Performance

In a disaggregated micro data center <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mu \mathbf{DDC})$</tex> , network resources have a large impact on the performance of applications. Thus, we propose a resource allocation method for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu \mathbf{DDC}$</tex> that allows it to efficiently utilize network resources. In this method, we model the impact of the allocated resources on the performance of the application. Then, we allow multiple applications to share links if all of the processes are expected to be completed within the acceptable time. To accommodate more applications, if multiple candidate resources exist, this method avoids allocating those that will be requested by applications in the future based on their importance. In this paper, we evaluate our method by simulating the allocation of continually generated resource requests and demonstrate that it can accommodate more applications without affecting the required performance.
Exploring Pseudo-Analog Video Transmission for Edge-AI Vision Applications

Edge-AI offloading is a promising solution for real-time object detection with resource-constrained edge devices. However, current edge architecture, which uses conventional digital video compression and modulation/coding, cannot provide stable object detection due to the scarcity of wireless resources and the explosive increase of edge devices. We employ pseudo-analog video transmission for Edge-AI offloading systems to cope with the issue. Although pseudo-analog video transmission cannot provide clear images, it has a substantial advantage for Edge-AI offloading systems, i.e., it can transmit higher-resolution video with limited bandwidth in lower Signal-to-Noise Ratio (SNR) links and do not need to control resolution according to wireless channel quality. This paper explores the impact of pseudo-analog video transmission on object detection accuracy.
Outlier Detection in IoT data for Elderly Care in Smart Homes

IoT-enabled innovative elderly healthcare facilitated by machine learning (ML) can address the challenges pertaining to the global aging population. For instance, it can enable the early detection of debilitating conditions such as Alzheimer's and dementia. This paper addresses this challenge by developing IoT and ML-based methods to recognize changes in long-term activities of daily living (ADLs) that may lead to the conditions mentioned above. In particular, we gather real-world long-term (approx. three years) data from 6 real-life single-resident elderly smart homes in Sweden, equipped with motion sensors in each room; and use unsupervised ML methods incorporating K-means clustering and local outlier factor to recognize changes in long-term behaviour efficiently. Our results have shown that K-means show similar performance in identifying outliers over all datasets while local outlier factorization fluctuates more but is more sensitive to identify small changes in living conditions. We foresee that our methods to detect long-term behaviour changes can support caregivers in carrying out their assessment for discovering the early onset of health conditions, thereby preventing further progression and providing timely treatment.
Prospecting Methods, Prediction Models, and Metallogenic Prediction

Through the geological survey of sandstone-type uranium deposits in recent years and on the basis of comprehensive and integrated solid mineral exploration related specifications and nuclear industry standards and technical requirements, by relying on the innovative idea of secondary development of coalfields and petroleum exploration drilling data.
Deep Multi-Scale Hashing for Image Retrieval (DMSH)

In recent years, with the great success of deep learning, deep networks-based hashing has become a leading approach for image retrieval. Most existing deep hashing methods extract the semantic representations only from the last layer, resulting in structure information being ignored which contains additional semantic details that are useful for hash learning. To enhance the image retrieval accuracy by exploring the semantic information and the structure information (local information), We propose a new method of deep hashing called Deep Multi-Scale Hashing (DMSH). This is achieved, firstly, by extracting multiscale features from multiple convolutional layers. Secondly, the features extracted from the convolutional layers are fused to generate more robust representations for efficient image retrieval. The experiments on the CIFAR10 and NUS-WIDE datasets show the superiority of our method.
A Progressive Deep Transfer Learning for the Diagnosis of Alzheimer’s Disease on Brain MRI Images

Alzheimer’s Disease (AD) is a serious public health issue that affects elderly people. According to the World Health Organization (WHO), nearly 100,000 people suffer from it in Algeria. Therefore, early diagnosis is vital for the patient’s treatment. In this paper, to improve the precision of early diagnosis, MRI images are employed to extract hidden features using a variety of transfer learning strategies on convolutional neural network models with different fine-tuning levels. The learned features are transferred from a large dataset of natural images to a small dataset of AD MRI images. For this purpose, progressive transfer learning is proposed, using the brain cancer MRI dataset as an intermediate to maintain both general features and domain-specific features relevant to the target domain. Accordingly, our tests showed that the performance results of the proposed approach produce a high accuracy of 99.22%, 98.90%, 98.28%, and 99.37% using respectively the VGG16, MobileNetV2, Xception and Inception models. These results demonstrate that, even with a highly diverse database like ImageNet, selecting the appropriate architecture and level of fine-tuning yields an improved adaptability and specialization of the pre-trained model.
Efficiency and Effectiveness of Teacher-Informed Targeting Testing from Different Perspectives

This chapter explores targeting testing in applications where the main interest is in classifying the test takers into three (or more) ordered proficiency levels. A targeted test consists of several fixed booklets, balanced in content but varying in the overall difficulty. The booklets are assigned to test takers using background information about the ability. Usually it is the teacher who assigns the booklets. Targeting testing can be conceived as a modest form of adaptivity that balances psychometric, substantive, instructional, and practical requirements. Using simulations, we studied the consistency and accuracy of targeted tests for polytomous classifications. A distinction is made between the use of targeting testing for making decisions about individuals and its use for interpreting group-level results. Results are obtained for various number of items per booklet, different booklet compositions, and different optimal or less optimal strategies for assigning booklets to candidates.
English Writing Teaching System Based on Computer-assisted Language Teaching

With the rapid development of modern technology, the use of software and technology in English education is on the rise. Providing abundant resources for school education, making network-based English learning a reality. The purpose of this paper is to design an English writing teaching system based on computer-aided language teaching. In this paper, use a context-based approach to analyze the state of affairs in English learning and management; describe the requirements of an online English learning system (e.g. system goals, core system performance); developed a system suitable for English learning and management in colleges and universities based on the combination of database development tools and technology; and showed the system design according to each functional module; When 600 accounts are logged in at the same time, the client response time was 1.1 Second, the server CPU utilization rate is 14%. Finally, the full text is summarized and the research prospects are put forward.
Virtual Mouse Using Hand Gesture

Several technologies are continually developing in today's technological world. The human-machine interaction is one such innovative concept. For instance, the limit of a wired mouse cannot be raised. Bluetooth hardware and a Bluetooth dongle need to be installed on the computer in order to use a wireless mouse. Such limitations would not apply to the suggested system, which depends on gesture recognition. The technologies for object identification and image processing are primarily employed in this study. The objective is to use only finger motions to move the mouse pointer on the screen, a process known as gesture recognition, rather than using any hardware, like a mouse. We introduce a unique Human-Computer Interaction approach in this work. A live camera controls how the cursor is moved. This paper offers a camera vision-based cursor control framework that catches hand motions on a webcam. The user may use additional hand gestures and draw point-based lines with their hand to move the computer cursor around. The recommended framework involves a low-goal camera as a sensor and can show hued pointbased lines while following a client's hand in two aspects. Python is utilized to carry out the framework. Hand motions are the least difficult and most regular method for imparting.
Artificial Intelligence Technology in Intelligent Recognition System of Translatology Based on Genetic Algorithm

The technology of intelligent recognition in translation has become a very important research field in information science. It is gradually becoming the main technology part of human-computer interaction in computer information technology. In order to solve the shortcomings of the existing intelligent recognition system for translation studies, this paper briefly discusses the translation speech sample corpus and ICTCLAS interface of intelligent recognition system on the basis of the discussion of genetic algorithm, artificial intelligence technology and intelligent recognition for translation studies. The ICTCLAS algorithm and genetic algorithm are also studied for the intelligent recognition system of translation. Finally, through the specific experimental analysis. The results show that the accuracy, callback rate and FI value of the ICTCLAS algorithm and the genetic algorithm are as high as 0.915, 0.926 and 0.902, respectively. Therefore, it is proved that the natural language processing technology in the intelligent recognition system of translation based on genetic algorithm is of high practical value.