Reviewerly/exHarmony huggingface dataset:
Synthesis of context‐aware business‐to‐business processes for location‐based services through choreographies

Abstract Modern technologies and emerging wireless communication solutions in the Information and Communications Technology (ICT) world are empowering the spread of the most disparate ready‐to‐use software services distributed over the globe and accessed by an increasing number of users. This state of affairs encourages the development of systems based on the reuse of existing services through composition approaches, notably choreographies. Also Public Administrations are driven towards a digitalization process which exploits composition approaches to build complex and interoperable systems that can be remotely accessed by citizens and authorities. However, an automatic support is needed in order to realize the service composition and the distributed coordination logic that enforces the correct choreography realization. Moreover, the need for building dynamic and user‐centered systems calls for the realization of choreographies capable to adjust their behavior to the surrounding context. This work presents our proposal for addressing the choreography realization problem, by describing an automated process for the synthesis of choreography‐based systems. The synthesized systems are location‐aware and able to adapt the services' interaction according to the user's needs and context conditions. We show and evaluate our approach at work on a real use case scenario in the Public Administration domain.
Multi-modal feature fusion for 3D object detection in the production workshop

3D object detection technology is of great significance to realize intelligent perception and ensure the production safety of a workshop. Existing 3D object detection relies on large-scale, high-quality 3D annotation data and is unsuitable for actual workshop scenes’ perception. This paper proposes a multi-modal feature fusion 3D object detection method (MFF3D) for a production workshop. The design of MFF3D includes the following steps: (1) Improved YOLOv3 attains the 2D prior region of an object, and RGB-D saliency detection obtains the object image pixels in that region. (2) Depth image pixels corresponding to the object are projected to generate the object’s frustum point cloud, and a multi-modal feature fusion strategy simplifies the object’s frustum point cloud, so as to remove outlier points and reduce the number of point clouds. This can replace the 3D object reasoning process based on deep neural networks; (3) An axis-aligned bounding box algorithm is used to generate the object’s 3D bounding box, and principal component analysis algorithm (PCA) is used to calculate the object’s pose information. MFF3D is applied in the workshop, and experiments verify the feasibility and detection accuracy. We set up a production workshop object dataset (PWOD) for experimental evaluation. In the case of a small amount of 2D annotation data and no 3D annotation data, experimental results show that when the threshold value of intersection over union of 3D object (IoU3D) is 0.5, the mean average precision value of 3D object (mAP3D) reaches 60.31, and the detection speed reaches 3 FPS. MFF3D does not rely on 3D annotation data and can effectively detect objects of a production workshop.
Towards Practical Post-quantum Signatures for Resource-Limited Internet of Things

A digital signature is an essential cryptographic tool to offer authentication with public verifiability, non-repudiation, and scalability. However, digital signatures often rely on expensive operations that can be highly costly for low-end devices, typically seen in the Internet of Things and Systems (IoTs). These efficiency concerns especially deepen when post-quantum secure digital signatures are considered. Hence, it is of vital importance to devise post-quantum secure digital signatures that are designed with the needs of such constraint IoT systems in mind.
Exploitation Analysis of Buffer Overflow in SL-Mail Server

Buffer overflows are known as the most widely used and the oldest forms of attacks used by attackers to gain access for remote code execution and similar attacks. Being the most common form of attack methodologies, these vulnerabilities are still being exploited in the current scenario. Buffer overflows are the vulnerabilities which are exploited due copying function which aren’t set to be bounded. Buffer overflows being a code or system vulnerability has been dominating the field of network and remote execution vulnerabilities. One of its attacks being the SL-Mail server enables attacker to insert remote code into the application and perform numerous attacks, this scenario of exploitation and step by step execution of buffer overflow, which can be used for further study for developing more secure applications. Eliminating buffer overflow vulnerability effectively, a great number of threats would also be eliminated. These vulnerabilities are generally exploited when a general program is provided with a larger data than it can normally accept. Most common defense mechanisms included writing application code in languages that perform bound check such as pascal or java, thus lacking data manipulation for some applications; hence this mitigation technique was not efficient. Several classification techniques have been introduced. Vulnerabilities resulting from buffer overflows are used as testing techniques that indicates the tracking of memory buffers and perform several checks for determining boundary and further conditions. An efficient detection method for severe vulnerability like buffer overflow needs to be discovered as current techniques are flawed and need a greater classification and a greater study of exploiting as well as identifying exploits has to be conducted. Several defense mechanisms along with a detailed study of exploitation of buffer overflow are discussed in this paper. This paper concentrates on these respective exploitation techniques and detection and prevention mechanisms. A quantitative comparison between analysis techniques of buffer overflow detection and analysis of different forms of exploitation methods have been compared for effectiveness for exploiting every aspect of buffer overflow in the source code as well as prevention technique effectiveness has been studied.
Tamper video detection and localization using an adaptive segmentation and deep network technique

In this work we have explored the hybrid deep learning architecture for recognizing the tampering from the videos. This hybrid architecture explores the features from the authentic videos to categorize the tampered portions from the forged videos. Initially, the process begins by compressing the input video using the Discrete cosine transform (DCT) based double compression approach. Then, the filtering process is carried out to improve the quality of compressed frame using the bilateral filtering. Then, the modified segmentation approach is applied to segment the frames into different regions. The features from these segmented portions are extracted and fed into hybrid DNN-AGSO (deep neural network- Adaptivf RELATED WORKSe Galactic Swarm Optimization) using Gabor wavelet transform (GWT) technique. Three different datasets are used to evaluate the overall performance they are, VTD, MFC-18, and VIRAT by MATLAB platform. The recognition rate achieved by VTD, MFC-18, and VIRAT datasets are 96%, 95.2%, and 93.47% respectively.
Research on Artificial Intelligence Machine Learning Character Recognition Algorithm Based on Feature Fusion

Abstract With the continuous progress of science and information technology, people begin to study in the field of intelligence, and machine learning is one of the key contents. At present, human beings have made some progress in intelligent robot, speech recognition and network search. The method of character recognition based on machine learning is of great significance to information technology. In this paper, an improved CRNN algorithm based on feature fusion is proposed, which combines Gabor features and Zernike moment features into a new feature vector, and then uses generalized K-L transform to compress the new feature dimension to remove redundant information. After testing, the accuracy of CRNN based on feature fusion on training data set and test data set is as high as 0.99, which shows that the neural network model can perfectly fit the training set of Chinese character recognition.
Improving the Performance of Semantic-Based Phishing Detection System Through Ensemble Learning Method

Technology is evolving at an exponential rate, and so are human minds. The innovativeness in technology has both its advantages and disadvantages. It has made general work easier and comfortable by building cyberspace but it has also led to cybercrimes. One of the cybercrimes is phishing attacks. Traditional anti-phishing techniques which use blacklists to iterate and check if the URL is legitimate or phishing is not very useful as the phishers can attack using new URLs. Therefore, Machine learning algorithms can be used to train models to learn the semantic differences between legitimate and phishing URLs. To perform classification of legitimate and phishing URLs, eight ML algorithms which are Random Forest, Decision tree, Naive Bayes, AdaBoost, KNN, XGBoost, Support Vector Machines (SVM) and Logistic Regression are trained and tested. To improve the standard of the classification model, an ensemble model is built using the above-mentioned machine learning algorithms. From the results observed, amid the machine learning algorithms, XGBoost achieved the highest accuracy and the ensemble model achieved an accuracy higher than all individual machine learning models.
End-to-End control of USV swarm using graph centric Multi-Agent Reinforcement Learning

The Unmanned Surface Vehicles (USVs), which operate without a person at the surface, are used in various naval defense missions. Various missions can be conducted efficiently when a swarm of USVs are operated at the same time. However, it is challenging to establish a decentralised control strategy for all USVs. In addition, the strategy must consider various external factors, such as the ocean topography and the number of enemy forces. These difficulties necessitate a scalable and transferable decision-making module. This study proposes an algorithm to derive the decentralised and cooperative control strategy for the USV swarm using graph centric multi-agent reinforcement learning (MARL). The model first expresses the mission situation using a graph considering the various sensor ranges. Each USV agent encodes observed information into localized embedding and then derives coordinated action through communication with the surrounding agent. To derive a cooperative policy, we trained each agent's policy to maximize the team reward. Using the modified prey-predator environment of OpenAI gym, we have analyzed the effect of each component of the proposed model (state embedding, communication, and team reward). The ablation study shows that the proposed model could derive a scalable and transferable control policy of USVs, consistently achieving the highest win ratio.
Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm

The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system (IT2-FLS) is a challenging task in the presence of uncertainty and imprecision. Grasshopper optimization algorithm (GOA) is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature, which has good convergence ability towards optima. The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS. The antecedent part parameters (Gaussian membership function parameters) are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm. Tuning of the consequent part parameters are accomplished using extreme learning machine. The optimized IT2-FLS (GOAIT2FELM) obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and prices. The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm. Analysis of the performance, on the same data-sets, reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.
A Deep Learning-Based Intelligent Receiver for OFDM

Artificial intelligence technology can be used to solve some problems that are difficult to be solved by traditional wireless communication. OFDM system has been widely used at present, but the resource consumption of pilot module is very high. Therefore, this paper reviews the OFDM communication system from the point of view of signal processing. At the receiving end of OFDM system, the deep learning method is adopted to jointly optimize each communication module of the receiving end. A intelligent receiver of OFDM communication system based on the Densenet neural network structure is designed and by optimizing the structure of Densenet neural network, the intelligent receiver is realized. This method can recover information at the receiving end and avoid complex pilot operation and signal error accumulation. The simulation results show that the intelligent receiver improves the receiver performance of OFDM communication system.
Automatic Generation of High-Resolution Facial Expression Images with End-to-End Models Using Pix2Pix and Super-Resolution Convolutional Neural Network

Recently, the means to see human face images have increased owing to the spread of smartphones and social networking services. Therefore, research on facial image generation, such as facial expression transformation, has been actively conducted. Especially, in the field of face images, the generation of face images using facial expression transformation has already been realized using generative adversarial networks (i.e., pix2pix). However, in the conventional models, only low-resolution images can be generated owing to limited computational resources, and the generated images are blur or aliasing. To solve this problem, we improved the resolution of generated images by training the Pix2Pix and super-resolution convolutional neural network methods as one model end-to-end instead of training them separately. Using the peak signal-to-noise ratio as an evaluation index, image quality was improved by 0.391 dB compared with the conventional model.
HyperPlan: A Framework for Motion Planning Algorithm Selection and Parameter Optimization

Over the years, many motion planning algorithms have been proposed. It is often unclear which algorithm might be best suited for a particular class of problems. The problem is compounded by the fact that algorithm performance can be highly dependent on parameter settings. This paper shows that hyperparameter optimization is an effective tool in both algorithm selection and parameter tuning over a given set of motion planning problems. We present different loss functions for optimization that capture different notions of optimality. The approach is evaluated on a broad range of scenes using two different manipulators, a Fetch and a Baxter. We show that optimized planning algorithm performance significantly improves upon baseline performance and generalizes broadly in the sense that performance improvements carry over to problems that are very different from the ones considered during optimization.
Customized Acoustic Model using Low-Resource Indonesian Speech Dataset for Short Command Speech Recognition System

The deep neural network technique recently is one of the most state-of-art approaches to train speech recognition systems. In most cases, the neural network needs a huge audio corpus and many speakers to perform well, especially for an acoustic model. One of the examples is the English speech recognition system which has shown accurate performance in many applications. However, the low resource dataset cannot boost performance while using the deep neural network to build an acoustic model. It remains challenging to investigate the usage of hyperparameters in the deep neural network to train the acoustic model. This paper explores proper hyperparameters for low-resource Indonesian audio datasets to gain acceptable performance using time-delay neural networks and convolutional neural networks. The dataset was collected from students using 56 short commands suitable for intelligence assistant applications. In our experiment, the time-delay neural networks can obtain 1.09 &#x0025; WER. Finally, the best performance is achieved by stacking convolutional neural networks layer after input layer and tuning hyperparameters.
Hardware and software co-verification from security perspective in SoC platforms

Attacks which combine software and hardware vulnerabilities are emerging security problems. Although runtime verification or remote attestation can determine the correctness of program states on a processor core, existing methods rarely consider the software states on the whole System on Chip (SoC). Also, there are SoC level solutions focusing on addressing the threat in the RISC-V architecture, which provides an open Instruction Set Architecture (ISA) of the processor. In this paper, we propose a comprehensive software and hardware co-verification method to protect the entire RISC-V based SoC platform at runtime. The proposed method adopts the Dynamic Information Flow Tracking (DIFT) framework to implement a new Verifier and Prover security architecture for supporting runtime software and hardware co-verification. This framework also considers the security states of third-party IPs (3PIPs). The framework is implemented as a coprocessor, new bus and DIFT supported IP wrapper which does not change the architecture of the main processor core. The new security architecture can be integrated with other RISC-V processors. We implement a FPGA prototype on the Rocket-Chip, an RISC-V open-source processor core. The hardware overhead is 10% LUTs and 9.4% Flip-Flops and a performance overhead is 3%.
A hybrid architecture for resolving Cryptographic issues in internet of things (IoT), Employing Quantum computing supremacy

The Internet of Things (IoT) is shaping the world of every economy and client. By 2023, clients, companies, and governments will be able to install a total of 40 billion IoT devices worldwide. The 2021's challenges simply confirmed the necessity to secure all four elements of the IoT Model: Sensors, Networks, Cloud, and Applications. Therefore, it is important to be pragmatic regarding the extreme threats and high worth of attacks on secrecy in IoT systems. Although the concept of convergence of IoT and Quantum Computing is not a standby topic, it had been mentioned in several works of literature and coated by many researchers, however, nothing is getting ready for sensible applications thus far. Quantum Computing is not prepared nevertheless, it is so far from preparation on a billboard scale. Quantum computing resolves most of the issues in IoT however it occurs yet below the analysis phase and can take over a couple of years to induce in public obtainable use. During this research, the author will start through a broad introduction to the internet of things and Quantum Computing and carry on with issues that can be featured throughout the convergence of Quantum Computing and IoT. To solve these integration issues in terms of cryptography this research will also provide the answer. At long last, this research will call attention to the hybrid of both fields to existing IoT network design.
Development of Hydraulic Cylinder Excavator Learning Media Based on Augmented Reality with Shapr 3D

Abstract The development process of augmented reality generally requires devices and software with high compatibility so that they are the only obstacles in its development. The purpose of this study was to design and produce an application of learning media on the hydraulic cylinder of excavator based on augmented reality with Shapr3d for prospective students of automotive engineering education and to see the product feasibility level of the test subjects and validators. This learning media development stage consists of five stages: Analysis, Design, Development, Implementation, and Evaluation. For the data method using direct observation and closed questionnaires. The questionnaire of the research results was analysed using Likert scale analysis. The results of the material expert validator with a score of 91.25% are very good category. Expert media design validator 92.5% is included in the very good category with a very proper name. The response to product use from three lecturers of automotive engineering education with a proportion score of 88.34% is in the very good category. The response to the use of the product was from twenty automotive engineering education students with a score proportion of 85.73% in the very good category. This that the application of automatic learning media on excavator hydraulic cylinder based on augmented reality with shapr3d based on Augmented Reality technology that has been developed can be used in elementary schools as a learning media for technical education.
Design of an Automatic English Pronunciation Error Correction System Based on Radio Magnetic Pronunciation Recording Devices

In this paper, a system for automatic detection and correction of mispronunciation of native Chinese learners of English by speech recognition technology is designed with the help of radiomagnetic pronunciation recording devices and computer-aided software. This paper extends the standard pronunciation dictionary by predicting the phoneme confusion rules in the language learner’s pronunciation that may lead to mispronunciation and generates an extended pronunciation dictionary containing the standard pronunciation of each word and the possible mispronunciation variations, and automatic speech recognition uses the extended pronunciation dictionary to detect and diagnose the learner’s mispronunciation of phonemes and provides real-time feedback. It is generated by systematic crosslinguistic phonological comparative analysis of the differences in phoneme pronunciation with each other, and a data-driven approach is used to do automatic phoneme recognition of learner speech and analyze the mapping relationship between the resulting mispronunciation and the corresponding standard pronunciation to automatically generate additional phoneme confusion rules. In this paper, we investigate various aspects of several issues related to the automatic correction of English pronunciation errors based on radiomagnetic pronunciation recording devices; design the general block diagram of the system, etc.; and discuss some key techniques and issues, including endpoint detection, feature extraction, and the system’s study of pronunciation standard algorithms, analyzing their respective characteristics. Finally, we design and implement a model of an automatic English pronunciation error correction system based on a radiomagnetic pronunciation recording device. Based on the characteristics of English pronunciation, the correction algorithm implemented in this system uses the similarity and pronunciation duration ratings based on the log posterior probability, which combines the scores of both, and standardizes this system scoring through linear mapping. This system can achieve the purpose of automatic recognition of English mispronunciation correction and, at the same time, improve the user’s spoken English pronunciation to a certain extent.