Reviewerly/exHarmony huggingface dataset:
A Secure Privacy Preserving Cloud-based Framework for Sharing Electronic Health Data

There exists a need for sharing user health data, especially with institutes for research purposes, in a secure fashion. This is especially true in the case of a system that includes a third party storage service, such as cloud computing, which limits the control of the data owner. The use of encryption for secure data storage continues to evolve to meet the need for flexible and fine-grained access control. This evolution has led to the development of Attribute Based Encryption (ABE). The use of ABE to ensure the security and privacy of health data has been explored. This paper presents an ABE based framework which allows for the secure outsourcing of the more computationally intensive processes for data decryption to the cloud servers. This reduces the time needed for decryption to occur at the user end and reduces the amount of computational power needed by users to access data.
A Time Interval based Blockchain Model for Detection of Malicious Nodes in MANET Using Network Block Monitoring Node

Mobile Ad Hoc Networks (MANETs) are infrastructure-less networks that are mainly used for establishing communication during the situation where wired network fails. Security related information collection is a fundamental part of the identification of attacks in Mobile Ad Hoc Networks (MANETs). A node should find accessible routes to remaining nodes for information assortment and gather security related information during route discovery for choosing secured routes. During data communication, malicious nodes enter the network and cause disturbances during data transmission and reduce the performance of the system. In this manuscript, a Time Interval Based Blockchain Model (TIBBM) for security related information assortment that identifies malicious nodes in the MANET is proposed. The proposed model builds the Blockchain information structure which is utilized to distinguish malicious nodes at specified time intervals. To perform a malicious node identification process, a Network Block Monitoring Node (NBMN) is selected after route selection and this node will monitor the blocks created by the nodes in the routing table. At long last, NBMN node understands the location of malicious nodes by utilizing the Blocks created. The proposed model is compared with the traditional malicious node identification model and the results show that the proposed model exhibits better performance in malicious node detection.
Entropy squeezing for a V-type three-level atom interacting with a single-mode field and passing through the amplitude damping channel with weak measurement*

The entropy squeezing of a V-type three-level atom interacting with a single-mode field and passing through the amplitude damping channel is investigated in detail. Our results show that when coupled to the single-mode field, the atom in appropriate initial states can not only generate obvious entropy squeezing but also keep in the optimal squeezing state, while passing through the amplitude damping channel, the atom can generate entropy squeezing under the control of the weak measurement. Besides, it is proved again that as a measurement method for atomic squeezing, the entropy squeezing is precise and effective. Therefore our work is instructive for experiments in preparing three-level system information resource with ultra-low quantum noise.
Advantages and Disadvantages of Automatic Speaker Recognition Systems

Automatic speaker recognition systems use the machines to recognize an individual via a spoken sentence. Those systems recognize a specific individual or confirm an individual’s claimed identity. The most common type of voice biometrics is the Speaker Recognition. Its task focused on validation of a person’s claimed identity, using features that have been obtained via their voices. Throughout the last decades a wide range of new advances in the speaker recognition area have been accomplished, but there are still many problems that need solving or require enhanced solutions. In this paper, a brief overview of speech processing is given firstly, then some feature extraction and classifier techniques are described, also a comparative and analysis of some previous research are studied in depth, all this work leads to determine the best methods for speaker recognition. Adaptive MFCC and Deep Learning methods are determined to be more efficient and accurate than other methods in speaker recognition, thus these methods are recommend to be more suitable for practical applications.
A Virtual Fuzzy Actuator for the Fault-tolerant Control of a Rescue Vehicle

This paper intends to present a novel and promising application field for fuzzy logic: the realization of fuzzy virtual actuators. In the field of control and diagnosis, virtual sensors are successfully applied since several years. This kind of sensor applies a mathematical model and different sources of information, for instance the state of actuators or information from already existing sensors, in order to generate virtual measurements. Current research activities aim at complementing the concept of virtual sensors with the concept of virtual actuators. Virtual actuators are parts of a fault-tolerant control strategy and aim to accommodate faults and to achieve a safe operation of a faulty plant. So far, only few authors report the application of fuzzy logic to the concept of virtual actuators and this application is limited on a representation of the non-linear system with the Takagi-Sugeno fuzzy model. In the focus of this paper is a novel fuzzy virtual actuator which applies fuzzy rules in order to include experts knowledge. The application example is a rescue vehicle. The application of fuzzy logic rules allows to integrate experts knowledge into the decision making of the virtual actuator in the control system of the vehicle. It enables the accommodation of several possible faults, such as a slippery surface under one of the chains of the rescue vehicle.
Hand Gestures Recognition Model for Augmented Reality Robotic Applications

Augmented reality (AR) is a research promising field. Its main idea is to integrate and merge the virtual world with the real world. Augmented reality could improve or enhance our perception of the real world by integrating virtual objects. The existing hand gesture applications related to augmented reality can detect hand motion or track it in addition to the ability to construct a 3D model of tracked hand using markers and motion sensing devices like Kinect, LeapMotion, and AR/VR instruments. In this paper, a hand gesture recognition model based on deep convolutional neural network is proposed to be used in 3D virtual environments for robotic teleportation. This model is tested on HTC VIVE Pro AR/VR instruments using the VIVE eye and on a Kinect v2 to control an industrial manipulator in real time using only the hand movements in both online and offline control modes.
Accelerometer-based Human Fall Detection Using Fuzzy Entropy

Falls are considered as one of the greatest risks and a fundamental problem in health-care for older adults living alone at home. The number of older adults living alone in their own homes is increasing worldwide due to the high expense of health care services. Therefore, it is important to develop an accurate system with the ability to detect human falls during daily activities. The focus of this study is to distinguish and detect human falls in Activities of Daily Living (ADL) based on data acquired from an accelerometer device. In this paper, a novel method based on Fuzzy Entropy measure is investigated to detect and distinguish human fall from other activities with a high degree of accuracy. The proposed method is tested and evaluated based on a publicly available URFD dataset. The experimental results show that Fuzzy Entropy achieved a sensitivity and specificity of 100% and 97.8%, respectively. Comparisons with other methods have also provided further support to the proposed method.
Rain streaks removal from single image based on texture constraint of background scene

Removing rain streaks from single image is mathematically ill-defined and the known strategies tend to over-smooth the background and remove image details. To tackle this problem, this paper proposes a novel two-stage system. Firstly, in view of the frequency and direction of rain streaks, Gabor filter is first used to suppress rain streaks. Based on the bases extracted from natural images, several Gabor filtered images extracted from the input rain image are fused by a new proposed method, and the fused result provides a texture constraint for the non-rain part. Secondly, a low-rank model is adopted to describe rain streaks as they take on similar and repeated patterns in an imaging scene. By combining the texture constraint and the low-rank model, a global cost function is constructed. Finally, an optimization strategy is proposed based on the split Bregman technique, which solves the function with high performance. A large number of experimental results show the competitive performance and efficiency of the proposed method in comparison with the state-of-the-art methods, making it more applicable in real practices.
Local Binary Pattern Based Feature Extraction with KELM for Face Identification

This paper emphasizes a novel methodology for robust face identification system, which is unification of holistic and local feature extraction technique and non-iterative learning algorithm. Here, Local Binary Pattern (LBP) is utilized to locate and summarize the local features corresponding to micro-regions of face image. The obtained features are classified (intra-class) by concatenating and correlating with corresponding local-statistics. After the classification of images with in same class using LBP operator, the inter-class classification is performed using fast and accurate kernel based extreme learning machine (KELM), learning algorithm. The efficacy of proposed LBP based KELM machine learning algorithm is tested and compared with other state-of-art methodologies. The experimental results evaluated on facial image databases evidently confirmed the supremacy of proposed approach.
A Water Surface Moving Target Detection Based on Information Fusion Using Deep Learning

Abstract An unmanned surface vehicle (usually referred to as USV) is an unmanned surface ship that has been developed in recent years. Surface unmanned boats can be used for a variety of civil and military missions, such as marine environmental monitoring, personnel search, anti-mine mines; island map mapping, offshore facility maintenance, and hydrographic surveys. Therefore, the research on unmanned surface vehicles has important theoretical significance and practical application value. The optical image information collected by the camera and the point cloud information collected by the laser radar are used for information fusion, and the optical image is processed by the deep neural network to obtain the target type, the detection confidence and the target position. At the same time, the point cloud information of the lidar image is used to obtain the distance and orientation information of the target. Finally, the two information is combined to obtain the type of surface target or obstacle, the confidence of the detection, and the distance and bearing information from the unmanned surface vehicles.
VLSI Implementation of Blowfish Algorithm for Secure Image Data Transmission

Nowadays, almost every person in the world uses the Internet to communicate to each other. For various applications, different types of images are transmitted over the Internet. Such photos normally contain either private or confidential data. Ensuring the confidentiality, honesty, authentication and nonrepudiation of images during transmission is therefore a prerequisite for image transmission. Blowfish is an open source algorithm thus making it available for anyone to use this algorithm for their desired purpose. Because of the fewer number of rounds and the operations used in the rounds being less complex, blowfish is considered relatively a fast block cipher after the key schedule has been completed. In this project, implementation of blowfish algorithm is done in MATLAB version 2019a. Verilog implementation is also done.
A Novel Approach to Reduce Bandwidth Cost and Balance Network and Server Level Load in Intra Data Center Network

These days, cloud computing is very popular because of its on demand utility like services. To mitigate the huge demand generated by the consumers of different level from individual to enterprise, the operational cost is also increasing. Furthermore, to retain delay within tolerable limit for sensitive applications, particularly during sudden surge of traffic is a great challenge. Data Center Network (DCN) is the underlying physical infrastructure of cloud computing. Therefore, to reduce the operational cost and delay, a framework is required to optimally manage the resources within a data center. In this paper, we consider the requests from enterprise clients mapped to a virtual network that requires both bandwidth and processing assets by interfacing it from an entry point to at least one server of a DCN. We present an efficient traffic engineering scheme based on a Mixed Integer Linear Programming model. This scheme from the ingress entry point to egress server is used in DCN to minimize the cost of establishing links between an entry point and a server while maintaining load balancing in both the link and server level. Load balancing helps to reduce delay. The performance of our model is discussed by conducting a trade-off analysis between different objectives of the composite objective function. We also investigate how various network parameters like bandwidth cost, maximum link and server resources utilization changes by setting different level of priority on different objectives.
SSP-Net: Scalable sequential pyramid networks for real-Time 3D human pose regression

In this paper we propose a highly scalable convolutional neural networks, end-to-end trainable, for real-time 3D human pose regression from still RGB images. We call this approach Scalable Sequential Pyramid Networks (SSP-Net) as it is trained with refined supervision at multiple scales in a sequential manner. Our network requires a single training procedure and is capable of producing its best predictions at 120 frames per second (FPS), or acceptable predictions at more than 200 FPS when cut at test time. We show that the proposed regression approach is invariant to the size of feature maps, allowing our method to perform multi-resolution intermediate supervisions and reaching results comparable to the state-of-the-art with very low resolution feature maps. We demonstrate the accuracy and the effectiveness of our method by providing extensive experiments on two of the most important publicly available datasets for 3D pose estimation, Human3.6M and MPI-INF-3DHP. Additionally, we provide relevant insights about our decisions on the network architecture and show its flexibility to meet the best precision-speed compromise.
Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding

Recent advances in natural language generation have introduced powerful language models with high-quality output text. However, this raises concerns about the potential misuse of such models for malicious purposes. In this paper, we study natural language watermarking as a defense to help better mark and trace the provenance of text. We introduce the Adversarial Watermarking Transformer (AWT) with a jointly trained encoder-decoder and adversarial training that, given an input text and a binary message, generates an output text that is unobtrusively encoded with the given message. We further study different training and inference strategies to achieve minimal changes to the semantics and correctness of the input text. AWT is the first end-to-end model to hide data in text by automatically learning -- without ground truth -- word substitutions along with their locations in order to encode the message. We show that our model is effective in largely preserving text utility and decoding the watermark while hiding its presence against adversaries. Additionally, we demonstrate that our method is robust against a range of local changes and denoising attacks.
Development Status, Trends and Challenges in the Field of “Blockchain and Education”

Abstract Blockchain, as the underlying technology of bitcoin, has gradually attracted the attention of the industry. At present, it has been used in many fields such as the financial industry and the service industry, and has great potential for application in the field of education. This article briefly introduces the blockchain technology, and at the same time explores and analyzes the development of the blockchain technology. This article starts with the relevant core literature in the field of “blockchain + education” in China, and understands the development trends of the blockchain technology. The research hotspots in the education field focus on the in-depth analysis of the research on blockchain technology in the field of education resource sharing and education information management, so as to grasp its current research status. Finally, based on the implementation of the blockchain technology in the implementation of the project, it discusses the facing challenges and proposes a new path for innovation and development looking forward to provide a reference for the deep integration of blockchain and education.
Energy Efficient and Adaptive Analog IC Design for Delay-Based Reservoir Computing

In recent years neuromorphic computing has achieved a lot of success due to its ability to process data intensive applications much faster and using less power compared to traditional computer architectures. Recurrent neural network, a type of artificial neural network has deemed to be more efficient in emulating biological neurons. The complexity in training recurrent neural networks is simplified in reservoir computing by training only the readout stage. This work describes an analog circuit implementation of a delay- based reservoir computing system. It is implemented using a Mackey-Glass nonlinearity function, a voltage to current converter, an analog to spike signal converter, a delay line and a spike to analog signal converter for adding feedback. The outputs from the system can be read from the delay line where the weights can be calibrated based on the required application. The design was implemented using TSMC 180nm technology with a maximum supply voltage of 1.8V for the neurons. This design is unique since the delayed feedback network used has persistent memory for data processing and uses analog signals directly without the need for analog-to- digital converters which makes it area and power efficient. The number of neurons in the delay line can be increased or decreased depending on the application allowing user flexibility. The nonlinearity function does not use any external supply and draws in power from incoming analog signals and the total power consumed by the system is 4.6mW.
Zero knowledge Proofs for Cloud Storage Integrity Checking

With the wide application of cloud storage, cloud security has become a crucial concern. Related works have addressed security issues such as data confidentiality and integrity, which ensure that the remotely stored data are well maintained by the cloud. However, how to define zero-knowledge proof algorithms for stored data integrity check has not been formally defined and investigated. We believe that it is important that the cloud server is unable to reveal any useful information about the stored data. In this paper, we introduce a novel definition of data privacy for integrity checks, which describes very high security of a zero-knowledge proof. We found that all other existing remote integrity proofs do not capture this feature. We provide a comprehensive study of data privacy and an integrity check algorithm that captures data integrity, confidentiality, privacy, and soundness.
On the Equivalence of Information Retrieval Methods for Automated Traceability Link Recovery

At ICPC 2010 we presented an empirical study to statistically analyze the equivalence of several traceability recovery methods based on Information Retrieval (IR) techniques [1]. We experimented the Vector Space Model (VSM) [2], Latent Semantic Indexing (LSI) [3], the Jensen-Shannon (JS) method [4], and Latent Dirichlet Allocation (LDA) [5]. Unlike previous empirical studies we did not compare the different IR based traceability recovery methods only using the usual precision and recall metrics. We introduced some metrics to analyze the overlap of the set of candidate links recovered by each method. We also based our analysis on Principal Component Analysis (PCA) to analyze the orthogonality of the experimented methods. The results showed that while the accuracy of LDA was lower than previously used methods, LDA was able to capture some information missed by the other exploited IR methods. Instead, JS, VSM, and LSI were almost equivalent. This paved the way to possible integration of IR based traceability recovery methods [6].
IoT based dual-axis solar tracking system

Abstract This paper discusses the implementation of IoT (Internet of Things) for maneuvering and remote monitoring the orientation of the solar panel from anywhere. Solar energy is clean, abundant, eco-friendly and it is a renewable type of energy. In order to maximize the efficiency of the solar panel, it should be perpendicular to the solar rays emitted from the sun. It can be achieved by rotating the solar tracker along with the sun throughout the day. Sun’s position is determined by its Azimuth and Elevation angles. Nowadays the world is moving towards a new era called the Internet of Things and everything can be found on the internet. These angle data can be also found on the Internet, which makes this solar tracker precise, cheap, and accurate for tracking. It also makes monitoring the solar tracker position from anywhere at any time.
Human Action prediction based on skeleton data

Human behavior prediction is an interdisciplinary research direction, involving image processing, computer vision, pattern recognition, machine learning, and artificial intelligence, which is one of the important research topics in the field of computer vision. This paper introduces a model for predicting human skeletal motion sequence, which is composed of LSTM main network and structured prediction layer. We have verified its performance on h3.6m dataset, and this structure has achieved good results in the short-term prediction of human motion.
A New Watermarking Method for Video Authentication with Tamper Localization

In this paper, a new method for video authentication is proposed. The method is based on construction of watermark images, which serve as a secondary carrier for the binary sequence. A unique watermark image is embedded into the coefficients of Discrete Wavelet Transform of each video frame. The analysis of images extracted from video allows to detect spatial attacks, and the sequence carried by the extracted images provides the ability to determine the type of temporal attack and localize the frames, which are tampered. The experimental study on the method quality and efficiency is conducted. According to the results of experiments, the method is suitable for solving authentication tasks. Furthermore, the method is robust to compression and format re-encoding.