Reviewerly/exHarmony huggingface dataset:
Data Fusion Enabled Approach for Sleep-Aware Applications

In the big data era, thousands of hundreds of devices play the role of data producer as well as data consumer. However, wireless devices bear the power exhausted problem in various situations. How to balance the trade-off between data processing speed and power efficiency is meaningful to be researched. In this study, we propose a Sleep Data Fusion Networks module (SFDN) which has a star topology Bluetooth network to fuse data of sleep-aware applications basing on our designed application protocol. In the network, a center Bluetooth device fuses data generated from target node Bluetooth devices. Due to the low power consumption of Bluetooth as well as connection safety, our method is a better choice for a long lifetime and non-real time sleep data processing and fusion tasks then the Wi-Fi-based approach used in EAST and Smart-Alarm sleep-aware applications.
Isolation Guarantee for Efficient Virtualized Network I/O on Cloud Platform

Virtualized Network I/O (VNIO) is an enabling technology in cloud computing environment as it provides virtual machines (VMs) the network connectivity. Among existing VNIO implementations, vHost-User para-virtualized solution has been commonly adopted by the industry as it achieves a good trade-off between performance and flexibility. In vHost-User, the packet transferring procedure is implemented by a user space virtual Switch (vSwitch) process, which is granted to access all VMs' memory without restrictions. This kind of memory sharing model brings significant isolation risk, and numerous reinforcement strategies have been proposed. In this work, we first discuss the limitations of these existing reinforcement solutions. Then we propose Secure-vHost, an isolation guaranteed VNIO design that moves the packet transferring procedure from vSwitch to the hypervisor. We redesign the memory sharing model and thread scheduling strategy in SecurevHost, to enhance the efficiency of packet transferring and achieve a good trade-off between isolation and performance. We implement and evaluate the Secure-vHost solution on the QEMU/KVM virtualization platform. The experimental results show that, comparing with the vHost-User solution, the SecurevHost implementation can achieve comparable performance with better VM memory isolation.
Multi-Level Co-Occurrence Graph Convolutional LSTM for Skeleton-Based Action Recognition

Human action recognition plays an important role in e-health applications, such as surgical skill analysis, patient monitoring, and automatic nursing systems. Recently, skeleton-based action recognition gains massive attention. It is an essential yet challenging task that requires effectively modeling the intra-frame skeleton representation and inter-frame temporal dynamics. Traditional Long Short-Term Memory (LSTM) based methods mainly capture long-term action context information from global level, yet they cannot fully model the relationship between different joints or persons to mine crucial co-occurrence features from different levels. To overcome this drawback, we propose a general end-to-end Multi-level Co-occurrence Graph Convolutional LSTM (MCGC-LSTM). By incorporating graph convolutional networks (GCN) into LSTM, our model can not only better exploit body structural information from skeletons but also enhance the multi-level co-occurrence feature learning. Specifically, we first devise multi-level co-occurrence (MC) memory units coupled with GCN to automatically model the spatial relationship between joints, and simultaneously capture the co-occurrence features from different joints, persons, and frames. Then we construct aggregated features of multi-level co-occurrences (AFMC) from MC memory units to better represent the intra-frame action context encoding, and leverage a concurrent LSTM (Co-LSTM) to further model their temporal dynamics for action recognition. Experiments show that our proposed model significantly outperforms mainstream methods on NTU RGB+D 120 dataset and Northwestern-UCLA dataset.
Optical form measurement employing a tiltable line scanning, low coherence interferometer for annular subaperture stitching interferometry

Abstract This contribution presents experimental and simulation results of a tiltable line scanning low coherence interferometer applied for form measurement of spherical and aspherical objects with a diameter of up to 300 mm. The region of interest is sampled by multiple annular subapertures that are realigned employing stitching algorithms based on Cartesian- and Zernike polynomial fittings. The paper addresses common challenges in the reduction and modeling of displacement errors associated with the motion of the interferometric sensor between subaperture measurements and compares the topography deviations of the experimental results with those simulated by a Monte Carlo based model.
Hierarchical clustering via mutual learning for unsupervised person re-identification

Person re-identification (re-ID) aims to establish identity correspondence across different cameras. State-of-the-art re-ID approaches are mainly clustering-based Unsupervised Domain Adaptation (UDA) methods, which attempt to transfer the model trained on the source domain to target domain, by alternatively generating pseudo labels by clustering target-domain instances and training the network with generated pseudo labels to perform feature learning. However, these approaches suffer from the problem of inevitable label noise caused by the clustering procedure that dramatically impact the model training and feature learning of the target domain. To address this issue, we propose an unsupervised Hierarchical Clustering via Mutual Learning (HCML) framework, which can jointly optimize the dual training network and the clustering procedure to learn more discriminative features from the target domain. Specifically, the proposed HCML framework can effectively update the hard pseudo labels generated by clustering process and soft pseudo label generated by the training network both in on-line manner. We jointly adopt the repelled loss, triplet loss, soft identity loss and soft triplet loss to optimize the model. The experimental results on Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT unsupervised domain adaptation tasks have demonstrated the superiority of our proposed HCML framework compared with other state-of-the-art methods.
Two systems of epistemic logic

Drawing on the results of chapters 2 to 4, two non-normal, multimodal axiomatic systems for both knowledge (<italic>k</italic>) and being in a position to know (<italic>K</italic>) are introduced—an idealized system and a weaker, more realistic system. Both share important theorems governing the complex operators ‘¬<italic>K</italic>¬<italic>K</italic>’ and ‘¬<italic>K</italic>¬<italic>K</italic>’, whose availability will be of crucial importance in later chapters. Unlike the realistic system, the idealized system requires subjects to be logically omniscient and must therefore ultimately be rejected in favour of the realistic system. A semantic characterization of the idealized system is devised that shows it to be sound and allows us to invalidate principles we previously found unacceptable for independent reasons. Since the realistic system is weaker, this result implies that it too has these features. Both systems imply that each of ⌜¬<italic>K</italic>¬<italic>K</italic>φ‎⌝, ⌜<italic>K</italic>¬<italic>K</italic>φ‎⌝, and ⌜¬<italic>K</italic>¬<italic>K</italic>φ‎⌝ encodes a luminous condition. The scenario of the unmarked clock presents a prima facie case against this implication. It is shown that the relevant anti-luminosity argument presupposes the principle that being in a position to know (<italic>K</italic>) distributes across provable conditionals—a principle that has been shown to be deeply problematic and that the realistic system is designed to flout.
Development of Intelligent Planning &amp; Integrated Office Management System for Drilling Operations in South Azadagan

Abstract Due to the distance limitation between the drilling department in Iran and the head office in China, it is necessary to improve the management level of the south Azadagan drilling department in the south of Iran. According to the drilling operation characteristics of Iran south Azadagan department, we design and develop an Intelligent Planning &amp; Integrated Office Management System (IPIOMS) including design management, work report, planning and cost control, operation management, statistical query and logistics support.
Batch Image Processing in Facial Detection Applications

Batch image processing for facial detection involves running a facial detection algorithm, in parallel, on batches containing multiple images rather than serially on a sequence consisting of single images. Batch image processing is crucial in live-video facial detection applications where the real-time processing of many frames is required. The performance of a facial detection application can be drastically improved when facial detection is done in parallel on batches containing multiple images. In this work, we analyze the performance gain due to running a GPU-based facial detection algorithm, in parallel, on batches of images versus the performance of running the GPU-based facial detection algorithm serially on a sequence of single images. We vary the number of images in which faces are to-be detected from 128 images to 1024 images. For each of the prior mentioned image sets, we measure the performance when the number of images per detection batch is varied from 1 image per batch (sequential) to 1024 images per batch in multiples of 2. We find that the technique of batch image processing improves the performance of a face detection application by approximately 10-11x on to-be-detected image sets consisting of 128, 256, and 1024 images. This performance improvement is attributed to a reduction in the communication overhead between the host CPU and GPU occurring on the PCI bus. Moreover, the technique of batch image processing enables the utilization of more of the GPU's resources that are left underutilized when GPU-based facial detection is done serially on a sequence of single images.
Realization of shock accelerometer sequence-to-sequence calibration based on deep learning

Abstract The sensitivity parameters or mathematical model of sensor are obtained through traditional shock accelerometer under calibration test, and then the measurement signal is restored through compensation and correction. As the shock signal with complex components is used in a harsh environment, it is very difficult to accurately restore the measured signals. In the meantime, the deconvolution process of signal recovery is an inverse problem in mathematical physics, which has ubiquitously ill-posed problems, thereby bringing great challenges to the accuracy and precision of the solution. Thus, we propose a depth calibration network based LSTM, which can be used to learn the mapping relationship between the calibrated sensor signal and the standard signal. The measured signal can be restored through a data-driven sequence-to-sequence calibration network, which is trained and verified through the exclusive open-source data set of shock signals. The test results proved the superior performance of the network in shock signal calibration.
Deep Imitation Learning for Safe Indoor Autonomous Micro Aerial Vehicle Navigation

Drones or MAVs are cyber-physical systems that have become ideal platforms for many applications in outdoor settings. Autonomous navigation in these outdoor settings has been effectively done using global positioning systems (GPS). Many human MAV pilots have demonstrated skills in controlling MAVs to maneuver in narrowed spaces indoors. However, this skill is hard to automate. Global positioning systems are unreliable indoors and in cluttered and confined environments. SLAM, being the most widely used analytical method, addresses the navigation problem by rendering a spatial map of an environment in which the Agent navigates while simultaneously localizing the Agent relative to this map. The main downside of SLAM is that rendering an entire map requires a large amount of computation, and rule-based techniques often lose their robustness in corner cases or situations that were not accounted for in developing the rules. Hence, learning directly from human demonstrations could produce improved results for complex tasks, particularly in sensor-limited systems. In this study, a policy that safely navigates a MAV through an indoor environment is learned through deep imitation learning. To effectively learn a policy that is robust to the domain or environment shifts, an ideal combination of monocular depth estimate and dense optical flow was determined to serve as state representation. Three different deep convolutional neural network architectures, namely, CNN, LSTM-RNN, and 3D CNN, were explored and developed to encode the navigation policy from expert demonstrations. The performance of these policies was then tested in a real environment. Results show that the CNN and 3D CNN policies successfully navigated the MAV around the obstacle set in the test environment while the LSTM-RNN did not. The Success Rate for CNN, LSTM-RNN, and 3D CNN were 90%,0%, and 90%, respectively.
Knowledge-Enhanced Collaborative Meta Learner for Long-Tail Recommendation

Long-tail effect is common in recommender systems, meaning that a tiny number of users have lots of interaction with items, while the majority of users have extremely little interaction. Most of existing approaches to recommender systems, especially methods based on collaborative filtering, suffer severely from long-tail problems due to the low resource issue. In order to handle the problem of long-tail recommendation, we utilize knowledge graph to enrich the representation of users and items. As auxiliary prior information source, knowledge graph has been popular technology for recommender systems nowadays, while it is rarely exploited with respect to the low resource issue. In this paper, we propose a knowledge-enhanced collaborative meta learner, which combines the priors in knowledge graph and collaborative information between head users to promote long-tail recommendation performance. We conduct several experiments on two real world datasets for long-tail recommendation. The results show that our approach outperforms several commonly used recommendation methods in the long tail scenario.
Segmented Encryption: A Quality and Safety Supervisory Model for Herbal Medicine Based on Blockchain Technology

The quality of herbal medicine has an important impact on human health. In this paper, we proposed a blockchain-based herbal quality and safety supervisory model for the current frequent herbal counterfeiting phenomenon. We manage the production, processing, and trading processes of herbal medicines by exploiting the blockchain's immutable and traceable properties. We proposed a segmented encryption method for information to encrypt the private information of enterprises. We use shared cloud storage to reduce waste of local storage space, and we proposed a verifiable random chain cutting mechanism based on the verifiable random function to handle the redundant blocks of the chain. Our article addressed the problem of herbal source falsification. Automated recording of key factors such as soil and temperature that affect the quality of herbal medicines is done from the seedling stage. Our herbal quality and safety supervisory model used blockchain technology to increase control over the production and distribution of herbal products, reduce herbal counterfeiting, and improve the efficiency of the system.
Internet of Things Design on Chili Plants

Abstract This study aims to develop system application in chili plants. Chili is a fruit commodity that cannot be released in daily needs. This plant is widely used to meet the need for vitamins and minerals necessary for growth and health. Many factors influence the development of plant cultivation, for example temperature, soil pH, humidity, the need for irradiation or light intensity used, and other factors. All of these are a combination that must be known in order to produce good chili plant growth. This study aims to produce a system design for monitoring the growth of chili plants based on the internet of things. The method used in this study uses a waterfall. The results of the study resulted in an IoT design on chili plants that is ready to be tested on a large scale.
A Quantum Multi-Agent Blind Signature Scheme with Unordered Signatures

Abstract With the development of information society, network security problems are becoming more and more serious, and digital signature as an authentication method is widely used in many fields such as information security and e-commerce, mainly to solve security problems such as forgery, repudiation, impersonation and tampering [1]. The latest quantum technology can overcome a series of difficult problems of classical digital signatures in a very short time. In this paper, we propose a quantum multiproxy blind signature scheme, in which the original signer is unable to sign the document himself, and delegates multiple proxy signers to sign and verify the document, and the signature order of the proxy signers is not restricted. The scheme adopts techniques such as Unitary transformation and Hadamard gate, and uses quantum key distribution protocol to ensure its security, which improves the efficiency of signature, ensures the security of signature, and has certain anti-attack capability.
Multi-Granularity Matching Network for Multi-Paragraph Machine Reading Comprehension

Abstract Multi-paragraph aims to allow machines to read multiple paragraphs and infer the answer to the given question. Usually, the model needs to use the selector to narrow the range of candidate paragraphs, and then use the reader to find the answer. For the selector, the previous work generally matches the question and the paragraph on the word level and the paragraph level. In this paper, we study the encoding and matching algorithm at the chunk level, which is supposed to make our model more accurate. Combining it with gated self matching mechanism, we design a multi-granularity matching network as the selector. The experimental results show that our model achieves competitive performance on Quasar-T.
Design and Application of Automatic Test System for Series Compensation Protection

The series compensation protection is different from conventional protection and lacks reliability, so it needs to be tested frequently. Due to the wide variety of series compensation protection, it is laborious to test it. This article describes an automatic test system consisting of a controller and several protection testers. The controller can communicate with the series compensation protection device and send instructions to the protection tester at the same time, which can realize the simultaneous detection of multiple series compensation protection and automatically generate test results. The application in actual engineering proves that this system can greatly improve the test efficiency of series compensation protection.
EXPERIMENTAL EVALUATION OF THE OPERATIONAL CHARACTERISTICS OF INFORMATION PROTECTION SYSTEMS

Objective . The article is devoted to solving the practical problem of evaluating the operational indicator of the quality characteristics of the information security system usability. Methods . Evaluation of the operational characteristics of software systems can be performed theoretically and by measurement. Since the theoretical assessment of operational characteristics has some disadvantages and limitations, an experimental assessment is advisable. Simultaneously, it is advisable to use the "Complexity" indicator of a typical operation performed by a security administrator, which expresses the average time of its execution as the primary indicator of the quality of the functioning of the information security system usability. Measurement evaluation of the operational characteristics of the information security system was carried out using the eye and mouse movement tracking methods. Results. The article provides an assessment of the "Complexity" indicator of all typical operations performed by the security administrator during the Sentinel NT 3.0 ISS operation following the program documentation. Conclusion . The obtained values of the "Complexity" indicator can be used in the formation of a work plan for the operation and maintenance of protected automated systems, in particular, with the installed information security system, when evaluating the timeliness of the performance of the listed works, as well as when justifying the structure of the units responsible for information protection and their quantity.
METAHACI: Meta-learning for Human Activity Classification from IMU Data

In the digital era, time-series data is pervasive to various applications and domains such as robotics, healthcare, finance, sport, etc. The inertial measurement unit (IMU) sensor is one of the popular devices collecting time-series data. Together with deep neural network implementation, this results in facilitating advancement in time series data analysis. However, the classical problem for the deep neural network is that it requires a vast amount of data which causes difficulty in the development and analysis process. Thus, we hypothesize that this problem can be avoided by combining data from many subjects that perform the same common tasks to get a larger amount of data. The consequence problem is that this decreases the overall classification accuracy. To tackle this, optimization-based meta-learning algorithms were selected, which are Reptile and model-agnostic meta-learning (MAML). The differences between Reptile and MAML are they use different rules for update gradient descent and differentiating through the optimization process. Leveraging their pre-existing meta-parameter weight before fine-tuning results in preferable accuracy for the existing amount of data, the figure is 89.65% for MAML and 78.38% for Reptile. To our knowledge, this is the first time comparing joint training with the meta-learning model, specifically, Reptile, and MAML for human activity classification from IMU sensors data. Experiment results show the superiority of classification accuracy from METAHACI proposed compare to joint training and simple CNNs.