Reviewerly/exHarmony huggingface dataset:
Balanced Multi-Region Coverage Path Planning for Unmanned Aerial Vehicles

Nowadays, Unmanned Aerial Vehicles (UAVs) are playing increasingly important roles in agriculture, rescuing and surveillance due to their small size, low cost and strong adaptability. Coverage Path Planning (CPP) is a fundamental problem for UAV applications, which means to find a path covering all the targets or regions of interest. Researches on CPP in a single region have been studied for decades, but rare to be devoted to covering multiple scattered regions of multiple UAVs. This paper proposes an attempt to solve this problem in a short time and take the task balance of multiple UAVs into account meanwhile. This work constructs a model for multiple UAVs to cover scattered regions firstly. In view of the high computational complexity of the precise solution, we keep innovating a heuristic measure based on the model to make the solving process feasible. To settle the problem of imbalanced time consumption caused by super regions, we improve the previously built model furthermore. A series of experiments validated that the presented approaches exhibit the effectiveness and balance of time consumption in diverse scenarios.
Automatic Gun Detection From Images Using Faster R-CNN

Reducing the incidence of gun violence has become a priority nowadays. Using Deep Learning models to automatically detect guns from security camera can contribute in reducing these incidences and save lives. The proposed system in this paper is an Automatic Gun Detection system using Faster R-CNN model. Since it is possible to change the CNN architecture used as a feature extractor in Faster R-CNN, Inception-ResNetV2, ResNet50, VGG16 and MobileNetV2 have been used separately as feature extractors. Intensive experiments have been conducted in order to evaluate the proposed architectures and compare them with YOLOv2. Promising results have been obtained with Faster R-CNN that is using Inception-ResNetV2. However, in terms of training and testing time, YOLOv2 has the shortest time, followed by VGG16, MobileNetV2, ResNet-50, and coming last Inception-ResNetV2.
Unified deep neural networks for end-to-end recognition of multi-oriented billet identification number

In this study, a novel framework for the recognition of a billet identification number (BIN) using deep learning is proposed. Because a billet, which is a semi-finished product, could be rolled, the BIN may be rotated at various angles. Most product numbers, including BIN, are a combination of individual characters. Such product numbers are determined based on the class of each character and its order (or the positioning). In addition, the two pieces of information are constant even if the product number is rotated. Inspired by this concept, the proposed framework of deep neural networks has two outputs. One is for the class of an individual character, and the other is the order of an individual character within BIN. Compared with a previous study, the proposed network requires an additional annotation but does not require additional labor for labeling. The multi-task learning for two annotations has a positive role in the representation learning of a network, which is shown in the experiment results. Furthermore, to achieve a good performance of the BIN identification, we analyzed various networks using the proposed framework. The proposed algorithm was then compared with a conventional algorithm to evaluate the performance of the BIN identification.
AdamOptimizer for the Optimisation of Use Case Points Estimation

Use Case Points is considered to be one of the most popular methods to estimate the size of a developed software project. Many approaches have been proposed to optimise Use Case Points. The Algorithmic Optimisation Method uses the Multiple Least Squares method to improve the accuracy of Use Case Points by finding optimal coefficient regressions, based on the historical data. This paper aims to propose a new approach to optimise the Use Case Points method based on Gradient Descent with the support of the TensorFlow package. The significance of its purpose is to conduct a new approach that might lead to more accurate prediction than that of the Use Case Points and the Algorithmic Optimisation Method. As a result, this new approach outweighs both the Use Case Points and the Algorithmic Optimisation Methods.
Implementation of Quantum Gates based Logic Circuits using IBM Qiskit

Quantum computing is an emerging field that depends upon the basic properties of quantum physics and principles of classical systems. This leads a way to develop systems to solve complex problems that a classical system cannot do. In this article, we present simple methods to implement logic circuits using quantum gates. Logic gates and circuits are defined with quantum gates using Qiskit in Python. Later, they are verified with quantum circuits created by using IBM Quantum. Moreover, we propose a way of instantiating the basic logic circuits to design high-end logic expressions. As per our knowledge, the proposed simple approach may be helpful to solve the complex logical problems in near future.
Efficient Verification of Continuous-Variable Quantum States and Devices without Assuming Identical and Independent Operations

Continuous-variable quantum information, encoded into infinite-dimensional quantum systems, is a promising platform for the realization of many quantum information protocols, including quantum computation, quantum metrology, quantum cryptography, and quantum communication. To successfully demonstrate these protocols, an essential step is the certification of multimode continuous-variable quantum states and quantum devices. This problem is well studied under the assumption that multiple uses of the same device result in identical and independently distributed (i.i.d.) operations. However, in realistic scenarios, identical and independent state preparation and calls to the quantum devices cannot be generally guaranteed. Important instances include adversarial scenarios and instances of time-dependent and correlated noise. In this Letter, we propose the first set of reliable protocols for verifying multimode continuous-variable entangled states and devices in these non-i.i.d scenarios. Although not fully universal, these protocols are applicable to Gaussian quantum states, non-Gaussian hypergraph states, as well as amplification, attenuation, and purification of noisy coherent states.
Secure Multiparty computation enabled E-Healthcare system with Homomorphic encryption

Abstract With the spread of Covid–19 pandemic it is difficult for many patients to physically visit the Hospitals and get treatment. The Improved technology enable such patients to contact the concern doctors and get diagnosis information online. The security of the sensitive data of the Patients is vulnerable when the information is shared across the network. To address this, we are proposing a novel approach with the Privacy ensured self-care health management schema using Secure Multiparty computation (MPC). Through this approach the patient can share the sensitive data to the Hospital server through the online mode, the data will be shared in the encrypted format which will be matched with the existing data at the Hospital records and the best relevant match based on the smart Index of disease. The privacy preserving is key aspect in this model as the data is shared in the sensitive mode so the Homomorphic Encryption (HE) approach is used to perform computations on the Patient’s sensitive data in the encrypted mode and ensure the confidentiality from Intruders to access the information. This model also proposes a novel approach which can overcome many security threats. The patient’s data will be shared and used in a more secured manner so the patients specifically the elder who are not advised to visit the public places in this pandemic can also receive better treatment through online.
SnapMove: Movement Projection Mapping in Virtual Reality

We present SnapMove a technique to reproject reaching movements inside Virtual Reality. SnapMove can be used to reduce the need of large, fatiguing or difficult motions. We designed multiple reprojection techniques, linear or planar, uni-manual, bi-manual or head snap, that can be used for reaching, throwing and virtual tool manipulation. In a user study (n=21) we explore if the self-avatar follower effect can be modulated depending on the cost of the motion introduced by remapping. SnapMove was successful in re-projecting user's hand position from e.g. a lower area, to a higher avatar-hand position-a mapping which can be ideal for limiting fatigue. It was also successful in preserving avatar embodiment and gradually bring users to perform movements with higher cost energies, which have most interest for rehabilitation scenarios. We implemented applications for menu interaction, climbing, rowing, and throwing darts. Overall, SnapMove can make interactions in virtual environments easier. We discuss the potential impact of SnapMove for application in gaming, accessibility and therapy.
Identifying animate stones and sacred landscapes: Twenty-five years of native pipestone-quarries research in the American midcontinent

This paper assesses our current understanding of the native use of the major midcontinental United States pipestone quarries based on over two decades of research. Our studies indicate that combining chemical and mineralogical techniques such as shortwave infrared spectroscopy (SWIS), thin-section petrography, and X-ray diffraction (XRD) have identified pipestones with similar chemical compositions containing distinctive mineralogical suites (often including berthierine, kaolinite, diaspore, muscovite, and pyrophyllite). This research has identified unique mineral compositions at known quarries such as catlinite, Ohio Feurt Hill, Baraboo, and Barron pipestones, as well as identifying previously unknown quarries of Sterling Illinois pipestone, Cahokia Missouri flint clay, and Portsmouth Ohio Claystone. These discoveries have led to a major shift in interpretations of Cahokian and Hopewell pipe exchange. Further examination of native ethnographic quarry use identified the primary attraction of pipestone quarries lay in the perceived ritual and spiritual power of the stone rather than in its economic utility.
Proof of Blockchain Work on The Security of Academic Certificates

The Proof of Work (PoW) consensus mechanism used in Bitcoin, along with other concepts, makes it increasingly popular. In this case, researchers introduce a new quantitative framework to analyze the security and performance implications of the various Consensus and network parameters of the Proof of Work Blockchain. Emerging innovative technology can be called Blockchain. Blockchain is a distributed digital ledger where transactions are recorded after verification. Transactions in the General ledger that have been verified by multiple clients in the Peer to Peer network using one of the many varied consensus algorithms to resolve problems within the network involving multiple nodes that cannot be used. The algorithm to be used is Proof of Work (PoW), a protocol that has a function to prevent cyber-attack activities (DDos) that can weaken a computer system resources. The idea behind the PoW algorithm is that certain jobs are done to block transactions before they are proposed to the entire network. For that, using the PoW consensus algorithm certificate on the Blockchain is certainly the creation of a real certificate and cannot be manipulated.
Random Finite Set Tracking for Anomaly Detection in the Presence of Clutter

In this paper, a sequential Bayesian framework is proposed to address the task of joint anomaly detection and tracking for surveillance applications in the presence of clutter. This is achieved by modeling the anomaly as a switching unknown control input which goes into action by modifying the expected dynamics of a target and ceases its activity (becomes nonexistent) under nominal behavior. Random Finite Sets (RFS) make it possible to represent the switching nature of the object anomalous behavior and derive a hybrid Bernoulli filter (HBF) that sequentially updates the joint posterior density of a Bernoulli RFS for the unknown velocity input and the object kinematic state. In addition, the proposed HBF has been customized for maritime anomaly detection by using a piecewise Ornstein-Uhlenbeck (OU) stochastic process as dynamic model of vessels. We illustrate the effectiveness of the proposed filter, implemented in Gaussian-mixture form, and compare its performance in a maritime surveillance example with the Interacting Multiple Model Probabilistic Data Association Filter (IMM - PDAF) for different levels of clutter.
Distance education based on smart-technology

Abstract This article explores a number of problems of implementing Smart-education for training of students of correspondence department. Its effectiveness for the system of modern higher education is shown. A number of problems of introducing Smart-education for teaching extramural students have been investigated. Its effectiveness for the modern education system is shown. However, the use of Smart-technologies in itself does not allow raising the level of education; it can only be used as a lever that will simplify and speed up the process of communication between the participants in the learning process. E-learning does not allow, in general, to solve the problem of adapting the educational process to the requirements that employers impose on graduates of higher educational institutions, but at the same time it allows the training to be optimized and brought to a higher level. Smart-technologies, in our opinion, are a promising modern technology that penetrates deeply into various types of human activity, and into education. The main distinguishing feature of smart technologies is the ability to quickly adapt quickly to changes in the external environment.
A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications

The rapid development of mobile internet technology and the better availability of GPS have made mobile online social networks (mOSNs) more popular than traditional online social networks (OSNs) over the last few years.They necessitate fundamental social operations such as establishing friend relationship, location sharing among friends, and providing location-based services.As a consequence, security and privacy issues demands the utmost importance to mOSNs users.The first stream of existing solutions adopts two different servers to store locations-based and social network-based information separately, thereby sustaining large storage and communication overhead.The second stream of solutions aims at integrating the social network server and the location-based server into a single entity.However, as these approaches exploit only one single server, they may face several performance issues related to server bottlenecks.Moreover, such schemes are found to be vulnerable to various active and passive security attacks.In this paper, we propose a privacy preserving, secure and efficient location sharing scheme for mOSNs, which shows both efficiency and flexibility in the location update, sharing, and query of social friends and social strangers.The security of the proposed scheme is validated using random oracle based formal security proof and Burrows-Abadi-Needham (BAN) logic based authentication proof, followed by informal security analysis.Additionally, we have used ProVerif 1.93 to verify the security of the system.The efficiency and practicability of the proposed scheme are demonstrated through experimental implementation and evaluation.
Detecting entanglement of unknown continuous variable states with random measurements

Abstract We develop a scheme for the detection of entanglement in any continuous variable system, by constructing an optimal entanglement witness from random homodyne measurements. To this end, we introduce a set of linear constraints that guarantee the necessary properties of a witness and allow for its optimisation via a semidefinite program. We test our method on the class of squeezed vacuum states and study the efficiency of entanglement detection in general unknown covariance matrices. The results show that we can detect entanglement, including bound entanglement, in arbitrary continuous variable states with fewer measurements than in full tomography. The statistical analysis of our method shows a good robustness to statistical errors in experiments.
Context-based consensus for appendable-block blockchains

Blockchain technology has been applied to various applications (e.g., smart buildings and smart cities) that typically run in an environment of smart devices, known as Internet-of-Things (IoT). To support these applications, different blockchain architectures, data structures and consensus algorithms have been proposed, tailored to IoT. One such proposal, appendable-block blockchain, is a promising blockchain framework for use in IoT environments. It provides a scalable data structure that allows parallel insertions between independent nodes. However, it has some limitations, in particular related to the possible eclipse attack by malicious gateways and the lack of consensus for transactions insertion. To solve these issues, we propose a new consensus mechanism for appendable-block blockchains, called context-based consensus. Using context-based consensus, information can be inserted in parallel across devices (called context) while ensuring that light-weight consensus is performed to guarantee that a transaction is well-formed and it is placed in the correct order. We implemented context-based consensus and show that using multiple contexts reduces latency and increases the throughput of transaction insertions when compared to consensus without contexts or using single transaction consensus.
Cuepervision: self-supervised learning for continuous domain adaptation without catastrophic forgetting

Perception systems, to a large extent, rely on neural networks. Commonly, the training of neural networks uses a finite amount of data. The usual assumption is that an appropriate training dataset is available, which covers all relevant domains. This abstract will follow the example of different lighting conditions in autonomous driving scenarios. In real-world datasets, a single source domain, such as day images, often dominates the dataset composition. This poses a risk to overfit on specific source domain features within the dataset, and implicitly breaches the assumption of full or relevant domain coverage. While applying the model to data outside of the source domain, the performance drops, posing a significant challenge for data-driven methods. A common approach is supervised retraining of the model on additional data. Supervised training requires the laborious acquisition and labeling of an adequate amount of data and often becomes infeasible when data augmentation strategies are not applicable. Furthermore, retraining on additional data often causes a performance drop in the source domain, so-called catastrophic forgetting. In this paper, we present a self-supervised continuous domain adaptation method. A model trained supervised on the source domain (day) is used to generate pseudo labels on the samples of an adjacent target domain (dawn). The pseudo labels and samples enable to fine-tune the existing model, which, as a result, is adapted into the intermediate domain. By iteratively repeating these steps, the model reaches the target domain (night). The results, of the novel method, on the MNIST dataset and its modification, the continuous rotatedMNIST dataset demonstrate a domain adaptation of 86.2%, and a catastrophic forgetting of only 1.6% in the target domain. The work contributes a hyperparameter ablation study, analysis, and discussion of the new learning strategy.
<scp>Word2Sent</scp>: A new learning sentiment‐embedding model with low dimension for sentence level sentiment classification

Abstract Word embedding models become an increasingly important method that embeds words into a high dimensional space. These models have been widely utilized to extract semantic and syntactic features for sentiment analysis. However, using word embedding models cannot be sufficient for sentiment analysis tasks because they do not contain sentiment features. Therefore, word embedding models do not adequately meet the comprehensive needs of sentiment analysis applications that rely on recognizing the polarity of a sentence. In this paper, we propose a sentiment embedding model (Word2Sent model) to tackle the weaknesses of the existing word embedding models for sentiment analysis applications. We developed this model based on the Continuous Bag‐of‐Words model and SentiWordNet lexicon to learn sentiment embedding for each word from its surrounding context words. It preserves semantic and syntactic features and captures implicitly sentiment ones. Besides, it can predict sentiment features in a very low sentiment embeddings dimension than traditional ones. The proposed method provides an improved sentiment classification performance and lowers the computational complexity. Both the accuracy performance and processing time results obtained indicate that the proposed model is particularly promising.
A Real-Time Trajectory Prediction Method of Small-Scale Quadrotors Based on GPS Data and Neural Network

This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produced better prediction results than the baseline models for all scenarios of the testing datasets. It was also found that the proposed model can converge to a stable state faster than the traditional gated recurrent unit model. Moreover, various types of training samples were applied and compared. With the same randomly selected test datasets, the performance of the prediction model can be improved by selecting the historical trajectory samples of the quadrotors close to the weight or volume of the target quadrotor for training. In addition, the performance of stable trajectory samples is significantly better than that with unstable trajectory segments with a frequent change of speed and direction with large angles.
An Empirical Study of Utilization of Imperative Modules in Ansible

In recent years, a configuration management tool is adopted to manage complicated and huge systems such as bare-metal servers, cloud computing resources and our personal computers. Such a tool makes the operations to deploy services more efficient and eliminates dependencies on the specific system operators. The operations are required to be idempotent for reproducible deployment. However, the imperative modules whose operations may not be idempotent are used frequently to execute user-defined scripts on the target system; it is unclear why and how they are used, though using them frequently is believed to be a bad practice. In this paper, we studied why and how imperative modules are used in a configuration management tool, Ansible. We found that imperative modules are mainly used to perform operations that are not supported by Ansible, and about 45% of imperative modules are replaceable by other modules; the replaceable modules might be idempotent. We, therefore, recommend developers to look at replaceable modules before using imperative modules since replaceable modules might make their operations idempotent.