Reviewerly/exHarmony huggingface dataset:
HARaaS: HAR as a service using wifi signal in IoT-enabled edge computing

Human activity recognition (HAR) is an important component in context awareness IoT applications such smart home, smart building etc. With the proliferation of WiFi-integrated devices, researchers exploit WiFi signals to recognize various human activities. In this work, we introduce a HAR as a Service (HARaaS) model for activity recognition services applied in IoT areas. HARaaS proposes a novel edge computing model in the concept of the Sensing as a Service (S2aaS) architecture to offer accurate and real-time activities recognition services with good energy efficiency. HARaaS distributes the resource-hungry computing workload i.e. training recognition model to edge terminals, and exploits the built-in intelligence of IoT devices. A WiFi-based activity recognition service is designed following the HARaaS architecture, and the lightweight machine learning and deep learning model are incorporated in the service for accurate activity recognition. Experiments are conducted and demonstrate the service achieves an activity recognition accuracy of 95% with extremely low latency and high energy efficiency.
MULTI-LEVEL TASKS IN THE THEME "GRAPHIC INFORMATION MODELS"

The article describes the main features of studying the theme "Graphic information models" in the school informatics course. Microsoft Visio is used as an environment for creating graphical information models. We recommend to organize study of Microsoft Visio based on the videos and basic training articles offered by Microsoft on the official website in the training section. Links to these materials are presented in the form of QR codes. The article offers examples of multi-level tasks in the process of studying the theme "Graphic information models" according to the levels of learning material identified in the methodological literature. Examples of tasks for informational, instructional, basic, expert and creative levels are given. A variant of the worksheet is considered, which is advisable to use when studying the theme "Graphic information models".
Physical I.D.: Development of artificial skin that is physically difficult to duplicate

In recent years, digital transformation has attracted attention. In such a situation, since the exchange of data between manufacturing equipment and control equipment increases with the adoption of IoT, there is a possibility of security problems occurred by duplication of manufacturing robots in factories. Therefore, the management method of the robot is not based on data like QR code, but is studied by attaching a unique physical pattern. In this study, we focused on the fact that a tuning pattern can be obtained by using two soft materials with different curing times, and produced a Physically Unclonable Artificial Skin (PUAS) with various surface patterns by changing the mixing ratio. As a result, the characteristics of the patterns by mixing ratio were found.
Feature Maps Channel Augmentation for Object Detection

Taking advantage of feature maps channels is one of the key challenges in object detection. In this study, we propose an optimization solution to further utilize the inter-channel relationship of feature maps. According to the exploration experiments, we have proposed a new Feature Maps Channel Augmentation module (FMCA) that can be inserted into mainstream structures to better establish the strong and weak dependency relationship between channels. We used comparison and ablation experiments and found that the FMCA module achieved a certain improvement under the circumstances that the detection speed was less affected. On the COCO dataset, our FMCA with ResNet-50 backbone has achieved relatively better results.
An RDF based approach for integrating data at different levels of abstraction

The growth and diversity of data sources have been a challenge for data interoperability. Although there are several initiatives to face this challenge, there are still difficulties especially when data are at different levels of abstraction. In this sense, this article proposes INTEGRA, an RDF-based approach, which takes advantage of the graph flexibility. It allows the integration of heterogeneous data sources through interconnections between schemas, between instances, and between instance-schema. Additionally, we present a case study in the Software Development Management scenario, which shows INTEGRA feasibility and its benefits.
PETA-System – A Piano Expression Teaching Aid System

In order to provide good musical performance to the piano, the wrist movement is very important during the performance of experienced players. However, to acquire proficiency in this technique is not a simple task since it is not simple to find formal representation, such as sheet music, to provide information about body movements. Moreover, piano teachers have to spend lots of effort to demonstrate an exercise to a single student, while many times they have to deal with many students in a classroom. Therefore, learning this approach requires not only observing experienced players but also, try to reproduce their execution to get practice. In this work, we propose PETA-System: a computer aid tool for teaching wrist movements on piano playing. The tool provides an interactive interface on which a tutor can record video musical excerpts to be performed by a learner. To evaluate the system, we carried out tests with actual learners to verify the improvement of the learning experience of wrist movement. A result has shown that the tool provided a stimulating learning environment for the student while reducing teacher efforts since they can share a time monitoring many students at the same time rather than a single one.
MHGAN: Multi-Hierarchies Generative Adversarial Network for High-Quality Face Sketch Synthesis

Face sketch synthesis has made significant progress in the past few years. Recently, GAN-based methods have shown promising results on image-to-image translation problems, especially photo-to-sketch synthesis. Because the facial sketch has a hyper-abstract style and continuous graphic elements, compared with other image styles, its local details are easier to expose small artifacts and blur. The existing face sketch synthesis methods lack models for specific facial regions and usually generate face sketches with coarse structures. To synthesis high-quality sketches and overcome the blurs and deformations, this paper proposes a novel Multi-Hierarchies GAN, which divides the face image into multiple hierarchical structures to learn different regions’ features of the face. It includes three modules: a local region module, mask module, and fusion module. The local region module can learn the detailed features of different local regions of the face by GAN. The mask module can generate a coarse facial structure of a sketch and uses the facial feature extractor to enhance the high-level image and learn the latent spaces’ feature. The fusion module can generate the final sketch by combining fine local regions and coarse facial structure. Extensive qualitative and quantitative experiments illustrate that the proposed method outperforms the state-of-the-art methods on the CUFS and CUFSF standard datasets and photos on the internet.
Comparison of Text Mining Classification Algorithms in Interbank Money Transfer Application

Abstract Funds transfer is a series of orders from the sender whose purpose is to move money from the sender to the recipient. The high interbank transaction fees imposed on each bank makes people use an interbank money transfer application, interbank money transfer transactions such as the Flip application are much in demand by the public because there are no administrative fees imposed on users. Opinion of the users of the application is processed using a text mining classification algorithm, namely the Naïve Bayes Algorithm and k-NN, the two algorithms are compared to produce which algorithm has high accuracy in processing the opinion of the flip money transfer application. Based on this matter, researchers conducted a sentiment analysis of the Flip Application, K-Nearest Neighbor (k-NN). After conducting research on sentiment analysis of Flip Applications, the Naïve Bayes classification algorithm has an accuracy of 91.25% and an ROC curve with an AUC value of 0.500. Whereas K-Nearest Neighbor has an accuracy of 85.25% and an ROC curve with an AUC value of 0.937. The Naïve Bayes algorithm can be said to be ”good classification” and the public can make the decision to use the Flip Application.
Dynamic Modelling and State Estimation of a High Speed Racing Drone

Autonomous drone racing has taken a turn for the better in recent years. Drones are becoming faster and implementing better state-of-the-art control techniques to overcome different challenges. With advancements in the fields of computer vision, machine learning, and artificial intelligence, the final goal of autonomous drones is to be quicker than human-piloted racing drones. Increasing the speed of autonomous drones increases the risks associated with flying them. Time-optimal control algorithms have been identified as a method of implementing aggressive maneuvers to fly drones at high speeds throughout the course of the race. These methods require precise state-estimates. This research work identifies a model for the rate controller. The work also includes an implementation of a state estimation model with drag compensation, also merging a pre-existing refined thrust model with Coriolis effects. With the idea of developing a state estimation model for a racing drone, the model is improved to include flight envelopes involving motor saturations.
Stochastic neural network based data analysis‐related talent recruitment optimization via <scp>CDN</scp> server

The competition for talent is always intense, especially in the era of big data, the data analysis‐related talent recruitment plays an important role in promoting the development of enterprises. Regarding resume screening, it is considerably significant during the process of talent recruitment. Consider that Neural Network (NN) has the strong stability on the prediction result and is given a lot of attention, this paper uses Stochastic NN (SNN) to optimize the data analysis‐related talent recruitment. Meanwhile, SNN is used to train resume‐related dataset and make resume screening. In addition, in order to accelerate the training process and decrease the training complexity, this paper uses Locally Linear Embedding (LLE) to do dimension reduction in terms of the extracted data features. Furthermore, in order to guarantee the on‐line resume screening, this paper does resume screening under Content Delivery Network (CDN) scenario, where CDN has two functions, that is, data storage and hot resumes recommendation. The experiments are made based on the collected 7000 resumes, and the result show that this paper has good optimization effect on data analysis‐related talent recruitment.
Applications of Successive Convexification in Autonomous Vehicle Planning and Control

In this paper, we present the application of successive convexification methods to autonomous driving problems borrowed from recent aerospace literature. We formulate two optimization problems within the successive convexification framework. Using arc-length parametrization in the vehicle kinematic model, we solve the speed planning and model predictive control problems with a range of constraints and obstacle configurations. In addition, we show a simple application of logical state-trigger constraints in a continuous formulation of the optimization by including an evasion maneuver in the simulations section.
Secure Wipe Out in BYOD Environment

Bring Your Own Device (BYOD) is a new trend where employees use their personal devices to connect to their organization networks to access sensitive information and work-related systems. One of the primary challenges in BYOD is to securely delete company data when an employee leaves an organization. In common BYOD programs, the personal device in use is completely wiped out. This may lead to the deletion of personal data during exit procedures. Due to performance and deletion latency, erasure of data in most file systems today results in unlinking the file location and marking data blocks as unused. This may suffice the need of a normal user trying to delete unwanted files but the file content is not erased from the data blocks and can be retrieved with the help of various data recovery and forensic tools. In this paper, we discuss: (1) existing work related to secure deletion, and (2) secure and selective deletion methods that delete only the required files or directories without tampering personal data. We present two per-file deletion methods: Overwriting data and Encryption based deletion which erase specific files securely. Our proposed per-file deletion methods reduce latency and performance overheads caused by overwriting an entire disk.
A Novel Two-path Backbone Network for Object Detection

In the field of deep learning, almost all backbone networks have a single bottom-up path and use the highest level feature to predict the results. Such a structure does not utilize low-level features, which limits its accuracy. To alleviate the problem, we propose a residual feature module (RFM) to extract features from a shallow layer and apply a fusion module (FM) to fuse the obtained feature with the deep feature. By adopting the RFM and FM, we introduce a novel two-path backbone network that improves the performance of the backbone with a single bottom-up path. On the COCO detection benchmark, experimental results demonstrate that our models can achieve significant improvements over the baseline backbone without adding too many computations.
The Practical Aapplication of the YOLOv3 Model from the Perspective of Machine Learning in Pedestrian Detection

Abstract Target detection and tracking technology is particularly important in the field of computer vision. With the needs of social production and the development of science and technology, the research of video pedestrian detection and tracking technology based on mobile platforms also needs to be put on the agenda. In this paper, the mobile robot is based on the Gmapping method to map the unknown environment, based on the A* and Dijistra algorithm to complete the safety inspection in the known environment, collect, record and save the video during the inspection process,and based on YOLOv3, the pedestrian target in the video for detection, the Deep_Sort target tracking algorithm establishes tracking of the dectected target, and creates a learning sample data set 2019.
Development and Application of Privacy-preserving Distributed Medical Data Integration System

The privacy protection technology is crucial to utilize big data in the medical field. Privacy-preserving Distributed Data Integration (PDDI) is one of the encryption techniques to match and integrate medical records held in multiple organizations while preserving privacy. The PDDI method is superior to other existing methods in terms of computational complexity and data versatility. This article provides an overview of the PDDI protocol. We also introduce newly developed Web-based applications that make it easier for researchers and healthcare professionals to use the PDDI protocol.
Predicting Places of Revelation of Quran’s Verses

Classical scholars mostly relied on the topics discussed in the verses of Quran to identify their places of revelation. They didn't have access to today's computing facilities. That's why they didn't and even couldn't efficiently use numeric properties like word length, word count, letter count, etc. to classify verses of the Quran. To the best of our knowledge, these properties haven't been fully exploited for this purpose till today. In this work, we tried to fill this gap. We wrote a Java program that creates a feature-matrix consisting of 64 features for each verse by analyzing its contents. We split this feature matrix into a training set and a testing set and used the training set to train seven classifiers in WEKA. Then we applied the trained model on the testing set. We found that with both 80% and 90% split as well as with 10-fold cross-validation, Decision Table classifier performed the best (with an accuracy around 98%) in classifying verses for both types of vectorization methods we employed. When we removed the chapter-number attribute from our feature-matrix, the highest accuracy dropped to 80-82% for each (split, vectorization-method) combination and Random Forest classifier turned out to be the best performer. When we ignored both chapter number and verse number, Random Forest remained the best performer in almost every case but its accuracy dropped slightly to 78-80%. We also applied five clustering algorithms for the same purpose and found that Canopy algorithm achieves the highest accuracy (around 74%) in every experiment. Each classification and clustering algorithm took less than two seconds to run. Together, these results suggest that it is possible to efficiently and quite accurately classify verses of Quran using only their constituent characters and other features. These results suggest that the place of revelation of the verses of Quran have high correlation with the numeric properties of their constituents.
Coded hierarchical dictionary strategy for face recognition efficiency

Face recognition is the most studied topic in the pattern recognition research field. This is probably due to the multiple useful applications which can be developed for important domains. Such deployed research efforts produced a huge number of methods, techniques and algorithms with different characteristics according to their simplicity, efficiency, robustness and speed. Present work investigates the performances of a simplified technique using a hierarchical classification scheme based on a constructed multi parts dictionary. The elementary features of the constructed dictionary were obtained using the well-known cross-correlation operator applied to the original images and their transformed images known as integral images and Discrete Cosine Transform. Hierarchical classification scheme is used to overcome the fact that this operator has high consumption time cost. The proposed strategy was implemented and tested on the images of the well known ORL and YALE database sets. Practical results demonstrate largely recognizable efficiency and speed characteristics.
Distributed Access Control for Cross-Organizational Healthcare Data Sharing Scenarios

Sharing information between healthcare organizations is a must for the next-generation healthcare systems. Despite the huge advantages that have been lately associated with it, organizations are still reluctant to its adoption due to the related privacy issues. In this work we propose a distributed access control system that relies on Garbled Circuits to perform a XACML-like policy evaluation, where attributes required for the evaluation are spread across organizations, ensuring that no attributes of one organization can be learned by others. Since Garbled Circuits is used as underlying protocol, an analysis of the complexity of the proposed method is provided in terms of Non-XOR gates. Some examples of the cost of using this method with different policy sizes is also provided with an estimation of the achievable amount of evaluations per second. The obtained results are promising but further research is still needed to determine which real-world scenarios would be suited for the proposed system.
An Optimization Method for Pattern Formation in Dynamic Environment*

Existing methods cannot effectively optimize the pattern formation in the dynamic environment. Therefore, this paper presents algorithms to employ the convex quadratic programming to minimize the distance to obtain the optimal pattern parameters based on the dynamic environment information and the given pattern shape and then plan the collision-free path of each robot to the goal considering a preferred velocity. Simulation results verified the effectiveness of the proposed methodology for scenarios of letter formation.
A Neurofuzzy Function Approximator Approach to Nonlinear Systems State-Estimation

A novel fuzzy inference system is introduced with desirable approximation properties for highly nonlinear systems that can be expressed in linear parameter varying form. This fuzzy inference system uses a hashing function to eliminate unnecessary computations and is compared with existing fuzzy inference systems. Furthermore, an application to nonlinear systems state estimation is provided, and the result is compared with that of the Extended Kalman filter. The main benefit of this novel fuzzy inference system is its suitability to resource-constrained embedded control and estimation applications.
A Practice-Informed Conceptual Model for a Combined Approach of Agile, User-Centered Design, and Lean Startup

Organizations worldwide have been adopting software development approaches that deviate from common agile methods in order to overcome some of their shortcomings. It has been reported that combining agile methods with Lean Startup and User-Centered Design results in a very powerful development approach, and academic research has developed some high-level process models for it. However, this combined approach is not well-documented, making it hard for inexperienced professionals to start using it. A grounded conceptual model of the combined approach enables the development of further instruments to help in adopting the approach, but such a model does not currently exist yet. We aim to showcase an initial conceptual model based on the empirical study of two software development teams that use the combined approach. We performed a case study where we investigated their day-to-day work using daily observations, semi-structured interviews, and focus group sessions; and built a conceptual model of the activities, techniques, and work products that both teams use daily. We reflect on how the conceptual model was conceived and the next steps in refining it, namely having it augmented with concepts sourced from literature.
Digital forensics process

Facilitated by increasing storage capacities and the falling prices of storage devices, the exponentially growing volume of data is regarded by many commentators as the single greatest challenge to digital evidence. These observations are supported by Moore’s Law (the number of transistors on an integrated circuit doubling every 18–24 months) and Kryder’s Law (storage capacity doubles every 12 months), which, inferentially, means there is an ever widening gap between the generation of stored data and the ability to process it. Consequentially, there are substantial cost implications to the storage and management of the entirety of the seized data. The cost implications can be balanced against the benefits to be gained from comparisons between cases over the short term and long term if the data are stored on a networked solution and are accessible for operational information and investigational purposes.
Calculation of Chinese-Thai Cross-Language Similarity Based on Sentence Embedding

In view of the poor accuracy, efficiency and scalability of the existing cross-language sentence similarity calculations, the Chinese- Thai cross-language sentence similarity is less studied. A new method is proposed. First, preprocess the corpus for Chinese-Thai parallel sentences, the sentence embedding model is used to obtain the Chinese-Thai sentence embedding matrix, and the sentence embedding is normalized. Then, the cross-language mapping model is used to embed the sentence. The conversion matrix is obtained through processing, and the orthogonal optimization of the conversion matrix is performed. Finally, the Chinese sentence embedding is mapped to the Thai sentence embedding space, and the Chinese- Thai cross-language sentence similarity is obtained by calculating the cosine of the two vectors, which provides a new idea for the Chinese-Thai cross-language sentence similarity calculation. The experimental results show that the method has good accuracy.