Reviewerly/exHarmony huggingface dataset:
Dynamic Reconfiguration of Cloud Composite Services Using Event-B

One of the key characteristics of the Cloud environment is hight dynamicity; constantly new services are added and others are deleted due to unavailability, faulty behaviour or performance degradation. Cloud services can be packaged together and offered as a composite service to overcome the atomic service’s shortage or respond to specific user demand. In this context, a faulty component service must be dynamically replaced in order to preserve the functional and non-functional correctness of the composite service. This work presents a formal approach to verify the dynamic behaviour of the Cloud composite service. We mainly focus on the correctional dynamic such as the substitution of faulty or unavailable services and resources. The proposed Event-B approach ensures the preservation of the composite service consistency, reliability and efficiency after the substitution of faulty or unavailable components.
An Interactive Voice Assistant System for Guiding the Tourists in Historical places

This paper examines the imminent of a voice empowered route aide for an individual client in an obscure landscape. This paper opens up the chance of the route framework for the individual clients to give the exact area data in the new landscape. It is probably going to grip a substantially more critical blow in broad daylight places, for example, clinics, transport stations, railroad stations, air terminals and shopping-shopping centres. This paper utilizes Google Map API to give data. This framework is a self-overseeing program which is cost ideal and permits the local clients to get to this program. Local clients who don't know about the exact area in the tremendous territory will discover this administration as the most useful one. The essential target in the plan of this paper depends on the Human-Machine communication. This route framework utilizes Text-to-Speech to aid the route administration through voice. Right now, consider the sound-related contribution from an individual and produce the discourse which is proportionate with watchwords accessible in the database and react utilizing already stored messages to guide the client to the ideal goal. The headings are offered to the client through sound signs. The positions are spoken to as longitude and scope focuses by the GPS collector. By giving the fitting directions the client can get the reaction through the method of sound sign. The created framework likewise supplements the local client with an illustrative help for headings to the goal.
Classification System Of Toddler Nutrition Status using Naïve Bayes Classifier Based on Z- Score Value and Anthropometry Index

Abstract The system of nutritional status assessment for a toddler is crucial to monitor the growth of a toddler. This present study was carried out to build a classification system for determining the assessment of toddler nutritional status using naive bayes classifier based on value the z-score and index of Anthropometry. The data was used to perform classification include gender, age, height and weight. The data was calculated using the z-score to get nutritional status based on anthropometric indices-weight-for-age, height-for-age, weight-for-height for classified use Naive Bayes Classifier . This study used 225 data of toddlers. Testing system used 55 data as training and 175 data as testing with 100% accuracy. The results of this study was a system that could be used to perform classification of nutritional status based on a combination of three anthropometric indices using the Naive Bayes Classifier . Naive Bayes Classifier performed classification interpretation of nutritional toddler consisting of malnutrition, normal and over-nutrition. This study showed that the classification of nutritional status was on 175 data generating the highest percentage was malnutrition of 44.58%.
A federal cubature Kalman filter for IMU-UWB indoor positioning

The tightly coupled IMU-UWB integration introduces high nonlinearity to the state and measurement equation of the Kalman filter so that the commonly used Extended Kalman Filtering method will produce a large truncation error, resulting in inaccurate fusion results. This paper proposes a new algorithm, called Federated Cubature Kalman Filtering (FCKF) method, by implementing the Cubature Kalman Filtering algorithm under the federated filtering framework. By implementing the proposed FCKF method, the observations of the UWB and the IMU are effectively fused, where the IMU is continuously calibrated by UWB so that it does not generate cumulative errors. In addition, it requires less computational burden than the classical Cubature Kalman Filtering method. Finally, the effectiveness of the proposed algorithm is verified by carrying out numerical simulations on two systems with different orders.
A Cryptanalysis of Trustworthy Electronicvoting using Adjusted Blockchain Technology

The voting process and results and the trust of voters are very important issues in current modern society. When voters could trust that the voting results are valid, people are willing to trust this voter will serve for them dealing with issues such as national security, education, and economics in the future. Electronic voting is an efficient method that could help people who could not vote in a limited period. Besides, it also solves the problem of long voting time due to many voters or more complicated voting steps. Although the voting machine could perform a record of votes and voting calculations, it does not require humans to handle a lot of above voting works. Nowadays, electronic voting has become an important research. However, there are too many assumptions and institutions which are often added in this research area. It might become to be unpractical when this voting research is applied to real society. Hence, we thought that blockchain might be a good solution for electronic voting research. Nevertheless, how to apply blockchain to protect people's privacy, anonymity, and voting rights still needs to be discussed in current days. In this paper, we proposed cryptanalysis on a trustworthy electronic voting scheme with blockchain and find out that there are some problems in their proposed scheme.
A Summary of Research on Intelligent Dialogue Systems

Abstract Dialogue systems have always been an important research direction of natural language processing. In recent years, the potential and commercial value of dialogue systems have attracted people’s attention, and technologies such as intelligent customer service and chatbots have gradually become popular. First, it outlines the concept and classification of the dialog system; then introduces the constituent modules of the dialog system, and mainly studies the key technologies and mainstream ideas; finally, summarizes the problems encountered at this stage and looks forward to the future research direction.
GenSpec: A File Fragment Classification Approach

Collection and analysis of data are at the heart of digital forensics. However, in real-life situations, the data of interest is often found in files that were partially erased or otherwise tampered with. Consequently, identification of the file types of those fragmented files and the data format of the text contained in each file are deemed necessary. While there are already many techniques for file fragment classification, their performance has to be improved in order to be truly useful for a large number of file fragments. This paper proposes a technique that first classifies file fragments based on the type of their content rather than on the format of the file they are coming from, and second, the fragments are further classified in narrower categories corresponding to their actual file type. The proposed technique is evaluated for its performance by benchmarking it to some existing techniques.
Incremental SVD-Based Collaborative Filtering Enhanced with Diversity for Personalized Recommendation

Along with the rapid rise of the internet, an e-commerce website brings enormous benefits for both customers and vendors. However, many choices are given at the same time makes customers have difficulty in choosing the most suitable products. A rising star solution for this is the recommender system which helps to narrow down the amount of suitable and relevant products for each customer. Matrix factorization is one of the most popular techniques used in recommender systems because of its effectiveness and simplicity. In this paper, we introduce a matrix factorization-based recommender system using Singular Value Decomposition (SVD) with some improvements in collaborative filtering and incremental learning. The SVD-based collaborative filtering methods can help generate personalized recommendations by combining user profiles. Moreover, the recommendation lists generated by the system are enhanced with diversity, which might attract more customer interests. Amazon's Electronic data set is used to evaluate our proposed framework of the SVD-based recommender system. The experimental results show that our framework is promising.
Lurking Eyes: A Method to Detect Side-Channel Attacks on JavaScript and WebAssembly

Side-channel attacks are a group of powerful attacks in hardware security that exploit the deficiencies in the implementation of systems. Timing side-channel attacks are one of the main categories that employ the time difference of running an operation in different states. In recent years, many types of timing side-channel analysis are proposed under the name of cache attacks. The limitation of such attacks is the requirement of running a spy program locally on the targeted device. Various studies have tried to overcome this limitation by implementing timing side-channel attacks, specially cache attacks, remotely on JavaScript and WebAssembly. There are some countermeasures proposed by previous works at three levels of hardware, operating system, and software. The main approach in most of previous works is to prevent timing side-channel attacks by disabling the essential features of JavaScript. In this paper, we weight the pros and cons of the previous countermeasures and propose a novel detection-based approach, namely Lurking Eyes. The proposed approach has the least performance reduction in JavaScript and WebAssembly. The evaluation results show that the Lurking Eyes has an accuracy of 0.998, precision of 0.983, and F-measure of 0.983. Considering the evaluation results and fewer limitations compared to previous works, Lurking Eyes method can be introduced as an effective way to counter timing side-channel attacks on JavaScript and WebAssembly.
Empirical evaluation of the active learning strategies on software defects prediction

Software defect prediction is a popular technical method in software engineering. In order to reduce the cost of a software defects, problems existing in the software are found by testing software products. Software defect prediction often uses machine learning techniques to improve the performance of software testing but requires enough labeled data when training the model. Because the cost of obtaining data is different from the label, the data is easy to obtain, but the label is cumbersome and expensive. In order to demonstrate software defect prediction, after the data obtained active learning algorithm is introduced to query the data, and the most valuable data is selected for expert annotation and then put into the model for training. However, it is not clear which active learning query strategy to choose the most effective in the software defect prediction model. We use different active learning strategy software defect prediction models for comparison. Experiment on the NASA dataset, using Naive Bayes and SVM, Linear Regression as the classifier. Comprehensive research results show that the Density-weighted strategy has a significant effect on the data set.
Pakistani Phrasal Sign Language Classification using Surface Electromyography

Electromyogram (EMG) is the electrical measure of the muscular activity which can vary depending upon motion is being performed. This varied muscular activity is picked by the EMG which can be used to distinguish different muscular activity. Electromyogram (EMG) is the electrical measure of the muscular activity which can vary depending upon what motion is being performed. This phenomenon forms the basis of this research which is the detection of various hand gestures that are performed during sign language communication. Pakistani sign language (PSL) data were acquired using the BIOPAC system. A total of 550 signals were collected. The signals were preprocessed using Empirical mode decomposition, which is succeeded by feature extraction giving feature vector consisting of statistical, spectral, time domain, and Local Ternary Pattern (LTP). These features were fed to support vector machines, which gives optimized results of 85.4% of accuracy, 85.36% of sensitivity, and 85.81 of specificity.
Automotive Marketing Research and Virtual Reality: A Systematic Literature Review

Virtual Reality (VR) is becoming a fast-growing technology and has been applied and studied in several fields in different ways. Nowadays, the quality of VR hardware and software provide the possibility to replace physical analysis in industry. In the automotive business, it has been used in product development by engineers and designers in the last years and further applications are being explored. Research has shown that VR application may reduce development cost and improve timing to market for new vehicles. This study aims to identify opportunities and limitations to apply VR in automotive product development marketing research. Based on a systematic review of the literature related to VR application on automotive business and in marketing research the results indicated that there are opportunities for this application such as cost reduction, proximity to customers, flexibility in interactions, model transportations avoidance, model complexity analysis, as well as limitations such as depth and haptic perception, motion tracking, lack of physical collision, color and texture definition, sound feedback, lack of product interaction and manipulation, visual spatial, graphic quality, gesture recognition, intuitiveness, customer aging, cybersecurity and cybersickness. On this basis, it is identified that VR in automotive product development marketing research may be used but considerations such as hardware and software definition, stimulus quality, research objectives, Mixed Reality applicability among others, must be taken in account. Further research is needed to identify other factors that could strengthen the effectiveness of this application in the sector.
An Automated Machine Learning Platform for Non-experts

With successful applications of machine learning to various domains, there have been large demands on developing machine learning-based applications. Automated machine learning is crucial to meet the demand because there are not sufficiently many expert machine learning developers to support such various demands. This paper presents an automated machine learning platform which gets some basic information about a task from nonexpert developer and examines several candidate models to develop an effective machine learning model. To choose some candidate machine learning pipeline for the given task, the platform makes use of the HTN-based plans to describe the machine learning plans along with its application conditions. The prototype system has been developed to mainly support machine learning models for tabular data including time-series data.
Research on decision support system of ship planning management based on data warehouse

Abstract This paper discusses the basic principle, model construction and theoretical framework of a new decision support system based on data warehouse, and applies this method to the data extraction process of shipyard plan management. The practice shows that the method is applicable and reliable in complex engineering systems such as shipyard planning management.
ELM: A Low-Latency and Scalable Memory Encryption Scheme

Memory encryption (ME) with authentication is becoming a key security feature of modern processors, as evident by the adoption of ME by Intel's SGX.Recently ME is actively studied from the viewpoint of system architecture.This paper studies ME from the viewpoint of symmetric-key cryptographic designs, with a primal focus on latency.A significant progress in such a direction can be observed in the SGX Integrity Tree (SIT).Using a variant of AES-GCM, SIT achieves an excellent latency.However, it has a scalability issue.By carefully examining SIT, we develop a new ME scheme dubbed ELM.We present an AES-based instantiation of ELM, and show that ELM significantly reduces latency from SIT for large memories, and achieves the provable security and equivalent hardware-protected (on-chip) area.We also present preliminary hardware implementations to substantiate our advantages.
Analysis of Federated Learning as a Distributed Solution for Learning on Edge Devices

Sensors and smart devices are continuously collecting massive amounts of data. Today's state-of-the-art machine learning (ML) techniques, such as deep networks, are typically trained using cloud platforms, leveraging the elastic scalability of the cloud. For such processing, data from various sources need to be transferred to a cloud server. While this works well for some application domains, it is not suitable for all due to privacy and network overhead concerns. Sharing life logging photos and videos from cell phones and wearable devices can cause privacy concerns for users. To respond to the needs of such applications, federated learning (FL) is proposed as a distributed ML solution for learning on edge devices, such as cellphones. In FL, data does not leave users' devices. All users collaboratively train a model without sharing their data. Each user trains a local model with their data and shares the model updates with a FL server to aggregate and build a global model. Such training respects users' privacy while reducing the network overhead for transferring data to a central server. In this paper, we aim to study the FL approach for learning on edge devices. We investigate the performance of the FL model with various parameter settings and compare its accuracy and training time to a traditional learning technique that is trained on a central server. For this purpose, we train a convolutional deep learning network for image recognition in traditional and federated format. The results confirm that it is possible to conduct a complex learning task on distributed devices without sharing their data with a central server, and such federated models can achieve comparable accuracy to central models.
Actor and Action Modular Network for Text-Based Video Segmentation

Text-based video segmentation aims to segment an actor in video sequences by specifying the actor and its performing action with a textual query. Previous methods fail to explicitly align the video content with the textual query in a fine-grained manner according to the actor and its action, due to the problem of semantic asymmetry. The semantic asymmetry implies that two modalities contain different amounts of semantic information during the multi-modal fusion process. To alleviate this problem, we propose a novel actor and action modular network that individually localizes the actor and its action in two separate modules. Specifically, we first learn the actor-/action-related content from the video and textual query, and then match them in a symmetrical manner to localize the target tube. The target tube contains the desired actor and action which is then fed into a fully convolutional network to predict segmentation masks of the actor. Our method also establishes the association of objects cross multiple frames with the proposed temporal proposal aggregation mechanism. This enables our method to segment the video effectively and keep the temporal consistency of predictions. The whole model is allowed for joint learning of the actor-action matching and segmentation, as well as achieves the state-of-the-art performance for both single-frame segmentation and full video segmentation on A2D Sentences and J-HMDB Sentences datasets.
Research on Obstacle Avoidance Motion Planning Technology of 6-DOF Manipulator

As we know, the robot is a mechanical device that integrates mechanical engineering, electronic technology, automatic control, and computer science. With the wide application of robot technology, obstacle avoidance motion planning of manipulator has become one of the hot issues in the field of robot research. This paper presents the research on the obstacle avoidance motion planning of a 6-DOF serial manipulator in a static space environment. This Obstacle avoidance motion planning is carried out by using the method of configuration space motion planning. Firstly, the obstacle avoidance problem is described mathematically, and a new collision detection method based on a capsule model envelope is proposed. Then RRT algorithm is improved from sampling space and sampling strategy to generate a collision-free motion path from the initial position to target position. The simulation experiment of the multi-obstacle working environment in MATLAB® verifies the feasibility of the improved algorithm. This study shows that the motion planning method of the improved RRT algorithm is not only feasible in theory, but also can meet the needs of practical application and ensure that there is no collision between the manipulator and the obstacles.
Anytime Dynamic Heuristic Search for Suboptimal Solution on Path Search

Path search is designed to find a path by traversing the state spaces from the initial state to the target state. Given enough memory and run time, the A* algorithm can find an optimal solution, but it expends much time to distinguish similar paths. Therefore, many scholars have proposed variants of the A* algorithm that find a suboptimal solution to speed up the searching efficiency. In this paper, the A* algorithm is improved and a new anytime dynamic heuristic search algorithm (ADHS) is proposed. It can find a solution quickly and then continuously optimize the quality of the solution to find the suboptimal solution until the end of time. The ADHS includes two stages, in the exploration stage, given an arbitrary cost bound, the solution is quickly obtained; in the update stage, where no setting parameters are required, reuses the previous search results. According to the cost of the latest solution, the dynamic weight factor w is introduced, which is half of the error between the current cost bound and the current solution. The next cost bound is dynamically adjusted, and the suboptimal solution is output. We tested the performance of the ADHS on the grid maps, and the experiments demonstrated that the performance of the ADHS was better than other algorithms.
High Precision Measurement of Dynamic Angular Rate for Turntable in the Calibration Application of Airborne Inertial Sensor

In the calibration application of airborne inertial sensor, turntable based angular rate measurement is mainly focused on static and constant angular rate test. Relatively little research has been done on the dynamic performance testing under changes in angular rate and angular acceleration rate. A wavelet analysis-based turntable dynamic angular rate measurement method and device are proposed, which can accurately acquire the instantaneous angular rate under turntable speed variation. The dynamic rate experiments were validated for three different angular acceleration situations. The results show that the measurement accuracy of the dynamic angular rate is better than 0.38% during the rapid variation of the velocity, and reaches 0.03% during the steady phase of the rotational speed.