Reviewerly/exHarmony huggingface dataset:
Secure IoT Update Using Blockchain

In this study a platform is devised to send automatic remote updates for embedded devices.In this scenario there are Original Equipment Manufacturers (OEMs), Software suppliers, blockchain nodes, Gateways and embedded devices.OEMs and software suppliers are there to keep their software on Inter Planetary File System (IPFS) and send the meta-data and hashes of their software to the blockchain nodes in order to keep this information distributed and ready to be requested and used.There are also gateways which are the members of the blockchain and the IPFS network.Gateways are responsible for asking for a specific update for specific devices from IPFS database using the meta-data kept on the blockchain, and they will send those hashed secure updates to the devices.In order to provide a traceable data keeping platform, gateway update operations are handled as transactions in a second blockchain network which is the clockchain of the gateways.The system was implemented as of the two separate blockchain networks and it has been shown that, despite the calculation overhead of the member devices, by separating the functions between the two blockchain networks a more reliable and secure platform can be achieved.
Accelerated Robot Skill Acquisition by Reinforcement Learning-Aided Sim-to-Real Domain Adaptation

Since robot learning takes a lot of time and might be too time-consuming to perform on real robots, the learning process is often done in simulation. Bridging the sim-to-real gap, however, still requires real-world effort that might impose severe limitations due the still large number of the required iterations. In this paper we explore how to accelerate real-world skill learning with sim-to-real transfer learning. We propose a new methodology based on dimensionality reduction with deep autoencoders, followed by domain adaptation. The latter combines re-training of the complete deep neural network with real data and adaptation of a part of the network parameters with reinforcement learning. The results of our experiments demonstrate that a considerable acceleration of real-world learning is achieved by approach when learning complex robot skills.
A Feature Selection Method Based on Hybrid Natural Inspired Algorithms

Data mining is one of the fastest-growing research domains in the information industry as a result of the wide availability of numerous data. Feature selection is the important preprocessing step to affect the performance of data mining. Ttimum. Besides that, hybrid algorithms maintain the advantages of each single-mode algorithm and avoid its weakness effectively. In this work, we propose a parallel model for feature selection named THDWL, which is a combination of Differential Evolution (DE), Whale Ohe natural-inspired algorithm is one of the most effective methods for feature selection, but there are some limitations in the single-mode algorithm such as slow efficiency and local optimization Algorithm (WOA), and Lightning Attachment Procedure Optimization (LAPO). In THDWL, the operators of these algorithms are independently implemented on their subpopulations and communicate at the end of each iteration to get the global best solution. Comparisons are conducted between single-mode algorithms and THDWL to verify the performance of the proposed method. The simulation results show that THDWL effectively improves the classification accuracy and convergence speed comparing with the single-mode algorithm.
Waveform Level Intelligent Multi-Task Receiver With BiLSTM

Power of deep neural networks (NNs) has enabled tremendous effective applications for communication receiver design. In this letter, we demonstrate the possibility of constructing merely one NN to straightforwardly recover bit messages from waveform sequences of unknown modulation schemes without an additional timing synchronization module. The typical bidirectional LSTM (BiLSTM) structures are employed to tackle the continuous transmission issue. Moreover, complementary modulation classification layers are trained to sieve the valid bits of multiple modulation schemes. Thus, the whole process of our method can be deemed as multi-task learning. Simulation results reveal that the NN receiver can approach the bit error rate (BER) performance of theoretical BER values in the ideal channel. We further extend the method to harsh conditions with poor transceiver parameters and find that a certain gain can be obtained compared to traditional phase-lock-loop (PLL) based methods. The whole architecture is an effective joint synchronization and detection method, eliminating the complicated selection of appropriate algorithms for multiple modulation schemes, which further enhances the communication receiver’s general intelligence level.
Intelligent Change Operators for Multi-Objective Refactoring

In this paper, we propose intelligent change operators and integrate them into an evolutionary multi-objective search algorithm to recommend valid refactorings that address conflicting quality objectives such as understandability and effectiveness. The proposed intelligent crossover and mutation operators incorporate refactoring dependencies to avoid creating invalid refactorings or invalidating existing refactorings. Further, the intelligent crossover operator is augmented to create offspring that improve solution quality by exchanging blocks of valid refactorings that improve a solution’s weakest objectives. We used our intelligent change operators to generate refactoring recommendations for four widely used open-source projects. The results show that our intelligent change operators improve the diversity of solutions. Diversity is important in genetic algorithms because crossing over a homogeneous population does not yield new solutions. Given the inherent nature of design trade-offs in software, giving developers choices that reflect these trade-offs is important. Higher diversity makes better use of developers time than lots of incredibly similar solutions. Our intelligent change operators also accelerate solution convergence to a feasible solution that optimizes the trade-off between the conflicting quality objectives. Finally, they reduce the number of invalid refactorings by up to 71.52% compared to existing search-based refactoring approaches, and increase the quality of the solutions. Our approach outperformed the state-of-the-art search-based refactoring approaches and an existing deterministic refactoring tool based on manual validation by developers with an average manual correctness, precision and recall of 0.89, 0.82, and 0.87.
Towards a Cyber Aware Chatbot Service

This paper presents the case proposal for a cyber-aware cloud operated chatbot service. The paper has in terms of its contributions (i) a design and (ii) a possible framework that detects as well as help to prevent the malware behavior from spreading on the user’s machine. The perspective encapsulates the human understanding of what this malware behavior exhibits, and how the chatbot aids the human user within that context. The Chatbot malicious behavior is modeled based on the vulnerabilities observed within the Open Web Application Security Project (OWASP) top ten [1] which outlines the security vulnerabilities of the web. This work becomes important in the context of chatbots becoming mainstream since the pandemic and the plethora of uses particularly in mission critical enterprises.
Analysis of the Effectiveness of SOFTWARE LIBERAL EDUCATION in a Non-Face-To-Face Environment

Purpose: Based on the previous studies, in this study, and in order to confirm the effect of the university s software liberal education in a contactless environment, as for the specific factors influencing the satisfaction of learning and academic achievement, the quality of classroom instruction, motivation for learning, convenience of time, and the immersion for learning were derived as the major factors according to the details of classroom instruction and learning contents. In this study, it is intended to examine the effectiveness of the university s basic software education in a contactless environment via the relationship between such factors and their influence on the satisfaction of learning and academic achievement. Method: In order to validate the effect of the university s basic software education in a contactless environment, this study has analyzed the differences between the subjects general characteristics, quality of classroom instruction, motivation for learning, convenience of time, immersion for learning, satisfaction of learning, and academic achievement by using the descriptive statistics of error, percentage, mean and standard deviation. The correlation of the quality of classroom instruction, motivation for learning, convenience of time, immersion for learning, satisfaction of learning, and academic achievement was analyzed by the Pearson s correlation, and the effect of each factor on the satisfaction of learning and academic achievement was analyzed by performing the multiple regression. Results: It was evaluated that the satisfaction of learning enhanced as the quality of classroom instruction improved and the immersion for learning worked well. It was also evaluated that, as the high quality details of classroom instruction and learning contents were provided, and the higher the motivation for learning, the higher the academic achievement, and the higher the convenience of time, the lower the academic achievement. Even in a contactless environment, the quality of classroom instruction is very important, and it may be said that it is the variable which has the largest influence on the effectiveness of the classroom instruction. Conclusion: It was confirmed that it has had a positive effect on the students satisfaction of learning and academic achievement by causing high quality classroom instruction, motivation for learning, and active immersion for learning in a contactless environment. Based on such results of this study, it is expected that assistance may be provided for developing and promoting various online education programs.
Federated Learning of User Mobility Anomaly Based on Graph Attention Networks

In recent years, more and more applications require low latency and high processing capacity. The computing capacity of mobile devices limits their performance to support compute- intensive applications. Opportunistic computation offloading reduces computing burden and prolong battery life of mobile devices. However, the computation resource usage on edge servers becomes unbalanced due to user mobility. Therefore, it is important to detect user mobility anomaly in order to improve computation resource usage efficiency on edge servers.In this paper, user mobility anomaly detection is investigated in the scenario of radio access network. Considering the impact of user mobility privacy and communication overhead on learning data, an algorithm is proposed named Graph Attention Network based User Mobility Anomaly Representation and Detection based on Federated Learning (GUMARD-FL). The simulation results show that the accuracy of GUMARD-FL is close to that of GUMARD-CL, and the best neighborhood value ξ is less than 5.
Pseudo View Representation Learning for Monocular RGB-D Human Pose and Shape Estimation

This work studies the problem of estimating human pose and shape from monocular RGB-D images. Depth information in the RGB-D input allows accurate 3D human reconstruction. However, the limited sizes of the RGB-D datasets restrict the generalization ability of the existing RGB-D based methods. In this letter, we propose a novel architecture, View Render Net (VRNet), that exploits the underlying 3D structure in the RGB-D input and utilizes additional RGB datasets for training. VRNet synthesizes pseudo multi-view representations via a novel feature render approach. The synthetic multi-view feature maps are aggregated with Multi-stage Multi-view Fusion (MMF) to impose the 3D structure constraints. We show that VRNet can naturally adopt the mixed RGB/RGB-D training and inference technique, which improves the model generalization ability. Comprehensive experiments are conducted to study the effectiveness of VRNet. As an illustrative example, the proposed VRNet improves MPJPE and PA-MPJPE by 15.2 mm and 4.9 mm on the Human3.6M dataset.
A Practical Method based on MSVL for Verification of Social Network

With the rapid development of the Internet, the study of properties in social network becomes more and more popular. A practical method based on MSVL for verification of social network is proposed, which is driven by properties and a modeling tool XML2MSVL. First, properties in social network are classified. Then, properties are selected and described with PPTL (Propositional Projection Temporal Logic) formulas. Next, the data related to properties is acquired through web crawlers and converted into an XML file. In addition, the XML2MSVL is used to translate the XML file into an MSVL (Modeling, Simulation and Verification Language) program. Finally, the program with formulas is executed in the compiler called MC to verify whether properties are satisfied. A case of Sina Weibo illustrates how this practical method works.
Backward Iterations for OFIR Filtering in Discrete-Time State-Space

In this paper, the optimal finite impulse response (OFIR) filter is re-derived using a batch form, which is convolution-based and computationally inefficient. To overcome this issue, we further represent the bath filter using iterations to get a computationally efficient iterative algorithm. We show, as yet unknown, an alternative recursions for iterative OFIR filtering algorithm, which are not the same as the Kalman filter (KF) recursions but can provide the same estimation performance statistically. A numerical performance verification of the OFIR filter, the KF, and unbiased FIR filter the performances is provided. It is shown that the OFIR filter occupies an intermediate place between the more robust UFIR filter and less robust KF.
Architecture for Orchestrating Containers in Cloud Federations

Abstract Containerization technology helps achieving not only better portability and interoperability but also better performance and efficiency on various cloud computing arrangements. Such technology is expected to empower cloud federations by enhancing portability and scalability across the federation. In this paper, we propose an architecture by adding two subcomponents to the NIST reference architecture for identifying resources and managing container orchestration in cloud federation environments. The architecture adds two subcomponents to the NIST reference architecture. The proposed two new sub-components enable resource identification and container orchestration across cloud federation members. These names of the two subcomponents are the Resource Identifier and the Container Orchestrator, respectively. The Resource Identifier component identifies the appropriate federated member for allocating tasks based on previous experience and current status. The Container Orchestrator facilitates the management and orchestration of containers at the federation level. We also identified several techniques, which can be used for resource identification. Among those, linear regression technique is selected for resource provisioning and identification of federation members. Further, these techniques are also expected to learn from log files from previous executions and prioritize resources based on the current resource status and previous experience.
Deep Learning Based Image Segmentation Directly in the JPEG Compressed Domain

Image segmentation is a widely explored research problem in the field of computer vision and image processing. Most of the existing techniques carry out segmentation in the pixel or uncompressed domain. More and more images and image-frames (videos) are archived and accessed in compressed form to provide better storage and transmission efficiency in recent years. As a result, the amount of compressed data has drastically increased and warrants novel techniques for automatically processing compressed data in the compressed domain without decompressing any data. Therefore here in this research paper we propose a novel idea of performing the segmentation straight from the JPEG compressed data by feeding the compressed DCT coefficients into the deep neural network. The proposed model is constructed by modifying an existing model Eff-UNet, where the first 3 layers are removed. The segmentation performance is improved and achieved state-of-the-art accuracy when the proposed model is tested on two benchmark datasets Oxford-IIIT and IDD-lite. One more observation is that the proposed model has achieved the fast encoding of input when it is fed with DCT representation
An Image Registration Method Based on Improved TLD and Improved ORB for Mobile Augmented Reality

This paper proposes an image registration method to address the problem of high registration delay, object matching inaccuracy and even object loss caused by fast camera motion in mobile augmented reality (MAR). Firstly, TLD (Tracking Learning Detection algorithm) is improved by creating the background model for the feature points and their neighborhood to reduce the number of scanning windows and to accelerate object tracking. Then, an improved ORB (Oriented FAST and Rotated BRIEF algorithm) is presented, where Hessian matrix and gray centroid method are adopted to give the scale and direction information to the feature points, Hamming distance is used to match the feature points, and RANSAC (RANdom SAmple Consensus algorithm) further optimizes the matching results. Furthermore, the image registration algorithm based on improved TLD and improved ORB is proposed, which reduces the detection range of feature points, improves the speed of object tracking, and can quickly calculate the transformation matrix between virtual and real scenes. The experimental results show that the proposed image registration algorithm has high accuracy and good robustness in the mobile augmented reality, so it can better meet the application needs of augmented reality in the mobile terminal.
Analysis of Lack of Cohesion in Methods (LCOM): A Case Study

As the need for software has increased, the maintainability of software has become an important issue and the need for the qualitative study of source codes has arisen. For this reason, we used software quality metrics to measure software quality. Moreover, for object-oriented software development projects, cohesion is an important factor. The main purpose of cohesion is to provide the rule that each class should serve a single purpose. Every class should have a single responsibility. In this study, types of Lack of Cohesion in Methods (LCOM) metrics were examined and compared using a sample Java code. As a result, it was observed that the LCOM5 metric gave the best result.
Adapting MaryTTS for Synthesizing Sinhalese Speech to Communicate with Children

The majority of the Sri Lankan population speak Sinhala, which is also the country's mother tongue. Sinhala is a difficult language to learn by children aged between 1–6 years when compared to other languages. Text to speech system is popular among children who have difficulties with reading, especially those who struggle with decoding. By presenting the words auditorily, the child can focus on the meaning of words instead of spending all their brainpower trying to sound out the words. In Sri Lanka, however, computer systems based on the Sinhala language especially for children are extremely rare. In this situation having a Sinhala text-to-speech technology for communicating with children is a helpful option. Intelligibility should be considered deeply in this system because this is specific for children. Recordings of a native Sinhalese speaker were used to synthesize a natural-sounding voice, rather than a robotic voice. This paper proposes an approach of implementing a Sinhalese text-to-speech system for communicating with children using unit selection and HMM -based mechanisms in the MaryTTS framework. Although a work in progress, the intermediate findings have been presented.
More Accurate and Robust PRNU-Based Source Camera Identification with 3-Step 3-Class Approach

Ensuring the legitimacy of image information is one of the critical issues for smooth operation of web systems such as Internet auctions. Linking the image to its source camera would be helpful to detect malicious activities on the web. This paper presents a secure pairing framework that utilizes Photo-Response Non-Uniformity (PRNU), a unique fingerprint of imaging sensors imprinted in a photo, for reliable source camera identification. However, the recent studies have found the image acquisition process of each camera model also leaves its unique pattern in the images, which could disturb the PRNU extraction and lead to the accuracy impairment. This is a serious issue since many people are using the same model smartphones in the real world. To address this issue, we propose to expand the traditional binary Same/Different device classification to a triplet classification by (1) different model (i.e. different device), (2) same model different device, and (3) same model same device. We also present two simple but effective defensive methods to tackle against a fingerprint-copy attack which aims at falsifying the device identification, and an attack with image manipulations. Results on a well-known benchmark dataset prove our proposed method can deliver high device identification accuracy, as well as robustness to spoofing attacks and image forgeries.
DataCat: Attention-based Open Government Data (OGD) Category Recommendation Framework

A data category recommendation framework for Thailand’s open government data portal (ThOGD) is proposed to assist data providers when publishing and registering a new dataset into the portal’s data catalog. However, existing approaches such as a multi-label classification problem, have not adopted the semantic features of data categories sufficiently. Deep learning model for Natural Language Processing has recently demonstrated to achieve high potential in learning the different degrees of semantic feature abstraction because all layers of multi-head attention blocks are provided with different fragments of metadata descriptions and corresponding tags. To obtain a robust recommendation result, this paper proposes DataCat: a Category Recommendation Framework using the attention-based framework through the ThOGD portal. Within this framework, the integrated multi-layers with particular semantic information are directly attached to the output layer of a network to enhance the effectiveness of information retrieval. The results point out that the attention-based framework has a weighted effect on loss of optimization. The performance when looking at the macro average of precision and F1-score improves by 0.664% and 0.557%, respectively. The micro average of those improves by 0.806%, and 0.698%, respectively.