Reviewerly/exHarmony huggingface dataset:
A Secure Steganography Model Using Random-Bit Select Algorithm

Communication does the exchange of information from one point to another point. Recent developments in internet has benefited the user to send any amount of data to any remote place in the world. The User is having the privileges of high data rate, remote connectivity and access but there is lack of data confidentiality. Hence Data security acts as a critical role in data communication. Two techniques that provide data security they are Cryptography and Steganography. This paper intends to give a secure steganography model using Random-Bit Select algorithm. The secret message is embedded into the carrier called as cover image based on randomly generated numbers and how many amount of bits to be replaced in each pixel can be governed through the user, so that user can define the stego image resolution based on the situation. The algorithm also provides security key which helps with dual level security and embedding the message in cover image randomly.
Research on trajectory planning method of dual-arm robot based on ROS

Taking into account the intuitiveness and high efficiency of the robot development process, the robot development method based on ROS is adopted. Firstly, the kinematics modeling of the dual-arm robot is performed, and the robot kinematics is solved. Secondly, the basic principles of linear trajectory planning and circular arc trajectory planning are introduced. The trajectory planning method based on Cartesian space is used to plan the trajectory of the end of the manipulator. Finally, the robot model file is imported into MoveIt!, and the robot trajectory planning control program is written through the user API for motion planning. At the same time, RViz can visualize the robot motion process, which verifies the correctness of the trajectory planning. ROS can also obtain robot motion data, display the robot motion process in multiple dimensions, and provide powerful data support for other subsequent tasks. The results show that the robot development method based on ROS is feasible.
No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality-predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real-world applications on video broadcasting and 3D multimedia industry.
A secure blockchain-based solution for harnessing the future of smart healthcare

The advent of information communication technologies (ICTs) has contributed enormously to the expansion of healthcare amenities worldwide. The retrieval and delivery of healthcare information over digital platforms have increased scope for inter- and intracare facilities among the healthcare industry. This comprises exchanging hypersensitive information pertinent to the patients' health records. Thus a highly risk-free approach is required to transmit the information over several remotely distributed platforms. In this chapter, we provide a secure architecture for acquisition and distribution of healthcare information (possibly acquired through sensory devices) over a large scenario to facilitate inter- and intracare health facilities to the patients. We address some of the pitfalls of existing healthcare services and provide schemes for bridging the inconsistencies observed between these services. We address the issues of electronic health records (EHRs) in adaptively handling the patients' health details, thus providing more interoperability to physicians, caregivers, healthcare clinics, and insurance agencies. We also provide an in-depth discussion regarding several enabling technologies that have led to the success of these smart healthcare services. Through our study, we introduce a blockchain-based architecture, which can address some of the critical care services by adding more ubiquity, reliability, and traceability to healthcare sector that conventional healthcare services failed to achieve.
The state evaluation method of distribution switch cabinet based on improved matter-element extension

In this paper, a matter-element extenics method is adopted to combine the information of each layer and each eigenvalue, so as to realize the evaluation of the current state of switch cabinet. The calculation of correlation degree is improved in the traditional matter-element extenics method. And the actual evaluation effect is optimized. This paper illustrates the evaluation process with practical examples, and compares the current evaluation results with the actual status of switch cabinets and the evaluation results obtained by traditional matter-element extension method. They proved the advance and effectiveness of the proposed method.
MAFIA: Multi-layered Architecture For IoT-based Authentication

Multi-factor authentication (MFA) systems are being deployed for user authentication in online and personal device systems, whereas physical spaces mostly rely on single-factor authentication; examples are entering offices and homes, airport security, and classroom attendance. The Internet of Things (IoT) growth and market interest has created a diverse set of low-cost and flexible sensors and actuators that can be used for MFA. However, combining multiple authentication factors in a physical space adds several challenges, such as complex deployment, reduced usability, and increased energy consumption. We introduce MAFIA (Multi-layered Architecture For IoT-based Authentication), a novel architecture for co-located user authentication composed of multiple IoT devices. In MAFIA, we improve the security of physical spaces while considering usability, privacy, energy consumption, and deployment complexity. MAFIA is composed of three layers that define specific purposes for devices, guiding developers in the authentication design while providing a clear understanding of the trade-offs for different configurations. We describe a case study for an Automated Classroom Attendance System, where we evaluated three distinct types of authentication setups and showed that the most secure setup had a greater usability penalty, while the other two setups had similar attributes in terms of security, privacy, complexity, and usability but varied highly in their energy consumption.
Speech Based Access of Kisan Information System in Telugu Language

In a developing country like India, agriculture provides large scale employment in rural areas, thus serving as the backbone of an economic system. For farmers it is important to decide which crop to grow, what Government schemes benefit them the most and what is the best selling price of the crop. In this paper we described the Speech to Speech interaction between farmer and government through an application using speech recognition system for Telugu language that will form the interface to the webpage providing information about the government schemes and commodity prices through voice. The proposed Kisan Information System (KIS) is integration of Speech recognition, Dialogue Manager, and Speech Synthesis modules. The performance of the KIS is better compared with existing one for Telugu language.
Battlefield Situation Information Recommendation Based on Recall-Ranking

With the rapid development of information technology, battlefield situation data presents the characteristics of “4V” such as Volume, Variety, Value and Velocity. While enhancing situational awareness, it also brings many challenges to battlefield situation information recommendation (BSIR), such as big data volume, high timeliness, implicit feedback and no negative feedback. Focusing on the challenges faced by BSIR, we propose a two-stage BSIR model based on deep neural network (DNN). The model utilizes DNN to extract the nonlinear relationship between the data features effectively, mine the potential content features, and then improves the accuracy of recommendation. These two stages are the recall stage and the ranking stage. In the recall stage, the candidate set of situation information is generated, and the massive situation information is reduced to a small candidate subset that is highly relevant to the commanders. In the ranking stage, the situation information in the candidate set is accurately scored and then sorted, and the situation information with a high score is eventually recommended to the commanders. Finally, we use the historical date from the situation management of real combat training information systems as the dataset and verify the effective-ness of the algorithm through experiments, and analyze the effects of the depth and width of the neural network on the performance of the algorithm.
An Interpretable Data Embedding Under Uncertain Distance Information

A common assumption in embedding methodologies is the availability of exact pairwise distances. In this paper, we propose a 2D embedding that overcomes this limitation. It can operate on distances that are represented as a range of lower and upper bounds. Such bounds are typically available when objects are compressed, whence our approach is highly applicable in the case of big compressed datasets. We establish linear convergence (i.e., exponential decay of distance to optimality) for the proposed scheme, with a rate characterized by the topology of the data graph. We compare with prevalent embedding methodologies (ISOMAP, t-SNE, MDS) and illustrate that our approach can provide fidelitous preservation of distances, correlations, and object ranks, even in the presence of inexact distance information.
Voice Attacks to AI Voice Assistant

Everything goes to the fact that our communication with technology will soon become almost exclusively oral. It is natural for a person to ask for something out loud and hear the answer: see how children are at ease with voice assistants. However, with new technologies - and voice control is no exception - new threats are emerging. Cybersecurity researchers tirelessly seek them out so that device manufacturers can secure their creations before potential threats turn into real attacks. However, in this paper we are going to talk about different voice attacks, which so far will hardly be able to find practical application, but the protection against which should be thought over now.
Service Humanoid Robotics: Review and Design of a Novel Bionic-Companionship Framework

At present, industrial robotics focused more on motion control and vision; whereas Humanoid Service Robotics (HSRs) are increasingly being investigated among researchers’ and practitioners’ field of speech interactions. The problematic and quality of human-robot interaction (HRI) has become one of the hot potatoes concerned in academia. This paper proposes a novel interactive framework suitable for HSRs. The proposed framework is grounded on the novel integration of Trevarthen [24 Companionship Theory and neural image generation algorithm in computer vision. By integrating the image-to-natural interactivities generation, and communicate with the environment to better interact with the stakeholder, thereby changing from interaction to a bionic-companionship. In addition, the article also reviews the research of neural image generation algorithms and summarizes the application cases of the algorithm structure in the field of robotics from a critical perspective. We believe that the new interactive bionic-companionship framework can enable HSRs to further develop towards robot companions.
3D Human Motion Reconstruction in Unity With Monocular Camera

This paper using a 3D pose estimator to predict human 3D poses. By combining the pose sequence information as a motion capture, we could reconstruct the human motion in Unity with any appearance. A potential application is collecting a compact human 3D activity dataset.
Image multi-label learning algorithm based on label correlation

Aiming at the problem of image multi-label classification, the number of sample label categories is large, the output space of corresponding multi-label classification increases exponentially, and the training data is lacking. This paper proposes an image multi-label learning algorithm based on the label correlation residual network-tree model. The algorithm is based on the residual network-tree model for each label category in the sample corresponding to a branch, and independently trains a classifier; the semantic correlation between the labels in the sample is used to select training data for the classifier, and avoid the interference of the missing labels in the sample to the classifier, while at the same time train with the residual network-tree model. The experiment was conducted on the large-scale multi-label data set: Pascal VOC 2007 images. And the results showed that the algorithm proposed in the article was superior to mainstream multi-label classification algorithms in the classification effect of experimental data sets.
Simultaneously Track and Detect Using Auxiliary Multiple-Model Particle Filter

Particle filters are widely used in performing tracking tasks under non-Gaussian noise environment or nonlinear dynamic and measurement models, as in the case of track-before-detect (TBD) problems. Compared to other algorithms, particle filter based methods are recursive and computationally efficient. In this paper, we proposed an Auxiliary Multiple-Model particle filter (AUX-MMPF) that can solve TBD problem. The performance of considered algorithm is tested in a single target tracking scenario. Simulation result shows that AUX-MMPF is superior than the other two algorithms compared.
Optimized Authentication Algorithm on FPGA for Space-Air-Ground Integrated Network

The increasing trend of integration of devices for extending the area of coverage for Communication, Navigation and Surveillance (CNS) services has compelled researchers to integrate devices in space, air, and ground which is known as Space-Air-Ground Integrated Network (SAGIN). With the evolution of technology, the speed of communication is increasing; which compelled researchers to develop high throughput implementations of data security algorithms for communication. Different devices (such as in space) have different security algorithms recommended. Besides, a single authentication algorithm is required to be used for all devices for providing security services. In this work, an authentication algorithm is proposed based on the Advanced Encryption Standard (AES) using parallel architecture. Furthermore, different optimizations are proposed for lightweight implementation, catering radiations in space, and high throughput implementation. The authentication algorithm is employed on FPGA for hardware realization and comparisons is provided with previous work. Moreover, the proposed algorithm shows effectiveness against recent attacks.
Editorial for Special Issue “Industrial Minerals”

Industrial minerals play an important role in keeping our society running, as they are used in a wide range of industrial and domestic applications [...]
Real-time Action Unit Intensity Detection

We present a real-time system for action unit intensity detection. We train a convolutional neural network, with images from DISFA+, to detect the intensities of 12 action units that are commonly found in the literature. Along with real-time capabilities, the system is also able to detect intensities on static images, and in the wild videos. While the focus of this work is on the detection of action unit intensities, we are able to implicitly detect the occurrence of them as well. This is done by setting all action units with an intensity greater than 0 as active. In doing this, we calculate the F1-micro and F1-binary scores for the DISFA+ dataset.
Non-Boolean quantum amplitude amplification and quantum mean estimation

This paper generalizes the quantum amplitude amplification and amplitude estimation algorithms to work with non-boolean oracles. The action of a non-boolean oracle $U_\varphi$ on an eigenstate $|x\rangle$ is to apply a state-dependent phase-shift $\varphi(x)$. Unlike boolean oracles, the eigenvalues $\exp(i\varphi(x))$ of a non-boolean oracle are not restricted to be $\pm 1$. Two new oracular algorithms based on such non-boolean oracles are introduced. The first is the non-boolean amplitude amplification algorithm, which preferentially amplifies the amplitudes of the eigenstates based on the value of $\varphi(x)$. Starting from a given initial superposition state $|\psi_0\rangle$, the basis states with lower values of $\cos(\varphi)$ are amplified at the expense of the basis states with higher values of $\cos(\varphi)$. The second algorithm is the quantum mean estimation algorithm, which uses quantum phase estimation to estimate the expectation $\langle\psi_0|U_\varphi|\psi_0\rangle$, i.e., the expected value of $\exp(i\varphi(x))$ for a random $x$ sampled by making a measurement on $|\psi_0\rangle$. It is shown that the quantum mean estimation algorithm offers a quadratic speedup over the corresponding classical algorithm. Both algorithms are demonstrated using simulations for a toy example. Potential applications of the algorithms are briefly discussed.
Lightweight Post-quantum Key Encapsulation for 8-bit AVR Microcontrollers

Recent progress in quantum computing has increased interest in the question of how well the existing proposals for post-quantum cryptosystems are suited to replace RSA and ECC. While some aspects of this question have already been researched in detail (e.g. the relative computational cost of pre- and post-quantum algorithms), very little is known about the RAM footprint of the proposals and what execution time they can reach when low memory consumption rather than speed is the main optimization goal. This question is particularly important in the context of the Internet of Things (IoT) since many IoT devices are extremely constrained and possess only a few kB of RAM. We aim to contribute to answering this question by exploring the software design space of the lattice-based key-encapsulation scheme ThreeBears on an 8-bit AVR microcontroller. More concretely, we provide new techniques for the optimization of the ring arithmetic of ThreeBears (which is, in essence, a 3120-bit modular multiplication) to achieve either high speed or low RAM footprint, and we analyze in detail the trade-offs between these two metrics. A low-memory implementation of BabyBear that is secure against Chosen Plaintext Attacks (CPA) needs just about 1.7 kB RAM, which is significantly below the RAM footprint of other lattice-based cryptosystems reported in the literature. Yet, the encapsulation time of this RAM-optimized BabyBear version is below 12.5 million cycles, which is less than the execution time of scalar multiplication on Curve25519. The decapsulation is more than four times faster and takes roughly 3.4 million cycles on an ATmega1284 microcontroller.
Applying Neural Network on Wheelchair Control System using Eye Blink and Movement Command

Some people with quadriplegia which cannot move their part of the body from hand to feet need an assistive device to support their mobility. An assistive device in form of an electric wheelchair can be developed using eye activity signals. The Artificial Neural Network (ANN) is proposed to be implemented on wheelchair control system based the user eye movement command. The system consists of the eye movement electrode, data collection, signal filtering and pre-processing, and the decision model. There are four commands that is involved on this study. The commands are glances left, glances right, blink, and double blink for command turn left, turn right, stop, and going forward, respectively. The hold out used for validation model by splitting data into 80% training set and 20% testing set. The test result shows that the Mean Absolute Percentage Error (MAPE) of the decision model is 1.55%. This result is a good result and the model can be implemented on the system.
Probabilistic Meta-Learning for Bayesian Optimization

Transfer and meta-learning algorithms leverage evaluations on related tasks in order to significantly speed up learning or optimization on a new problem. For applications that depend on uncertainty estimates, e.g., in Bayesian optimization, recent probabilistic approaches have shown good performance at test time, but either scale poorly with the number of data points or under-perform with little data on the test task. In this paper, we propose a novel approach to probabilistic transfer learning that uses a generative model for the underlying data distribution and simultaneously learns a latent feature distribution to represent unknown task properties. To enable fast and accurate inference at test-time, we introduce a novel meta-loss that structures the latent space to match the prior used for inference. Together, these contributions ensure that our probabilistic model exhibits high sample-efficiency and provides well-calibrated uncertainty estimates. We evaluate the proposed approach and compare its performance to probabilistic models from the literature on a set of Bayesian optimization transfer-learning tasks.
Multi-Turn Response Selection in Retrieval Based Chatbots with Hierarchical Residual Matching Network

Abstract Response selection in retrieval-based chatbot aims to find the most relevant response in a candidate repository given the conversation context. A key technique to this task lies in how to measure the matching degree between conversation context and response at rich semantic information. In this paper, we propose a hierarchical residual matching network (HRMN) to fully extract and make use of the rich semantic information in the conversation history and response for themulti-turn response selection task. We empirically verify HRMN on two benchmark data sets and compare against advanced approaches. Evaluation results demonstrate that HRMN outperforms strong baselines and has a distinct improvement in response selection.
Vietnamese Facebook Posts Classification using Fine-Tuning BERT

With the development of social networks in the age of information technology explosion, the classification of social news plays an important role in detecting the hot topics being discussed on social networks over a period of time. In this paper, we present a new model for effective Facebook's posts classification and a new dataset which is labeled for the corresponding subject. The dataset consists of 5191 Facebook user's public posts, which is divided into 3 subsets: training, validation and testing data sets. Then, we explore the effectiveness of fine-tuning BERT model with three truncation methods compared with other machine learning algorithms on our dataset. Experimental results show that the fine-tune BERT models outperform other approaches. The fine-tune BERT with “head + tail” truncation methods achieves the best scores with 84.31% of Precision, 84.12% of Recall and 84.15% of F1-score.