Reviewerly/exHarmony huggingface dataset:
Complete <i>j</i>-MDP Convolutional Codes

Maximum distance profile (MDP) convolutional codes have been proven to be very suitable for transmission over an erasure channel.In addition, the subclass of complete MDP convolutional codes has the ability to restart decoding after a burst of erasures.However, there is a lack of constructions of these codes over fields of small size.In this paper, we introduce the notion of complete j-MDP convolutional codes, which are a generalization of complete MDP convolutional codes, and describe their decoding properties.In particular, we present a decoding algorithm for decoding erasures within a given time delay T and show that complete T -MDP convolutional codes are optimal for this algorithm.Moreover, using a computer search with the MAPLE software, we determine the minimal binary and non-binary field size for the existence of (2, 1, 2) complete j-MDP convolutional codes and provide corresponding constructions.We give a description of all (2, 1, 2) complete MDP convolutional codes over the smallest possible fields, namely F13 and F16 and we also give constructions for (2, 1, 3) complete 4-MDP convolutional codes over F128 obtained by a randomized computer search.
A Subject-Sensitive Perceptual Hash Based on MUM-Net for the Integrity Authentication of High Resolution Remote Sensing Images

Data security technology is of great significance to the application of high resolution remote sensing image (HRRS) images. As an important data security technology, perceptual hash overcomes the shortcomings of cryptographic hashing that is not robust and can achieve integrity authentication of HRRS images based on perceptual content. However, the existing perceptual hash does not take into account whether the user focuses on certain types of information of the HRRS image. In this paper, we introduce the concept of subject-sensitive perceptual hash, which can be seen as a special case of conventional perceptual hash, for the integrity authentication of HRRS image. To achieve subject-sensitive perceptual hash, we propose a new deep convolutional neural network architecture, named MUM-Net, for extracting robust features of HRRS images. MUM-Net is the core of perceptual hash algorithm, and it uses focal loss as the loss function to overcome the imbalance between the positive and negative samples in the training samples. The robust features extracted by MUM-Net are further compressed and encoded to obtain the perceptual hash sequence of HRRS image. Experiments show that our algorithm has higher tamper sensitivity to subject-related malicious tampering, and the robustness is improved by about 10% compared to the existing U-net-based algorithm; compared to other deep learning-based algorithms, this algorithm achieves a better balance between robustness and tampering sensitivity, and has better overall performance.
Privacy Issues of AI

This chapter sheds light on how private data is systematically collected, stored and analysed with the help of artificial intelligence. We discuss various forms of persistent surveillance at home and in public spaces. While massive data collection raises considerable ethical concerns, it is also the basis for better performance for AI systems.
An Integration Model Based on Graph Convolutional Network for Text Classification

Graph Convolutional Network (GCN) is extensively used in text classification tasks and performs well in the process of the non-euclidean structure data. Usually, GCN is implemented with the spatial-based method, such as Graph Attention Network (GAT). However, the current GCN-based methods still lack a more reasonable mechanism to account for the problems of contextual dependency and lexical polysemy. Therefore, an improved GCN (IGCN) is proposed to address the above problems, which introduces the Bidirectional Long Short-Term Memory (BiLSTM) Network, the Part-of-Speech (POS) information, and the dependency relationship. From a theoretical point of view, the innovation of IGCN is generalizable and straightforward: use the short-range contextual dependency and the long-range contextual dependency captured by the dependency relationship together to address the problem of contextual dependency and use a more comprehensive semantic information provided by the BiLSTM and the POS information to address the problem of lexical polysemy. What is worth mentioning, the dependency relationship is daringly transplanted from relation extraction tasks to text classification tasks to provide the graph required by IGCN. Experiments on three benchmarking datasets show that IGCN achieves competitive results compared with the other seven baseline models.
An Adaptive Embedding Strength Watermarking Algorithm Based on Shearlets’ Capture Directional Features

The discrete wavelet transform (DWT) is unable to represent the directional features of an image. Similarly, a fixed embedding strength is not able to establish an ideal balance between imperceptibility and robustness of a watermarked image. In this work, we propose an adaptive embedding strength watermarking algorithm based on shearlets’ capture directional features (S-AES). We improve the watermarking algorithm in the domain of DWT using non-subsampled shearlet transform (NSST). The improvement is made in terms of coping with anti-geometric attacks. The embedding strength is optimized by artificial bee colony (ABC) to achieve higher robustness under the premise of satisfying imperceptibility. The principle components (PC) of the watermark are embedded into the host image to overcome the false positive problem. The simulation results show that the proposed algorithm has better imperceptibility and strong robustness against multi-attacks, especially those of high intensity.
Human Movement Representation on Multivariate Time Series for Recognition of Professional Gestures and Forecasting Their Trajectories

Human-centered artificial intelligence is increasingly deployed in professional workplaces in Industry 4.0 to address various challenges related to the collaboration between the operators and the machines, the augmentation of their capabilities, or the improvement of the quality of their work and life in general. Intelligent systems and autonomous machines need to continuously recognize and follow the professional actions and gestures of the operators in order to collaborate with them and anticipate their trajectories for avoiding potential collisions and accidents. Nevertheless, the recognition of patterns of professional gestures is a very challenging task for both research and the industry. There are various types of human movements that the intelligent systems need to perceive, for example, gestural commands to machines and professional actions with or without the use of tools. Moreover, the inter class and intra class spatiotemporal variances together with the very limited access to annotated human motion data constitute a major research challenge. In this paper, we introduce the Gesture Operational Model, which describes how gestures are performed based on assumptions that focus on the dynamic association of body entities, their synergies, and their serial and non-serial mediations, as well as their transitioning over time from one state to another. Then, the assumptions of the Gesture Operational Model are translated into a simultaneous equation system for each body entity through State-Space modeling. The coefficients of the equation are computed using the Maximum Likelihood Estimation method. The simulation of the model generates a confidence-bounding box for every entity that describes the tolerance of its spatial variance over time. The contribution of our approach is demonstrated for both recognizing gestures and forecasting human motion trajectories. In recognition, it is combined with continuous Hidden Markov Models to boost the recognition accuracy when the likelihoods are not confident. In forecasting, a motion trajectory can be estimated by taking as minimum input two observations only. The performance of the algorithm has been evaluated using four industrial datasets that contain gestures and actions from a TV assembly line, the glassblowing industry, the gestural commands to Automated Guided Vehicles as well as the Human–Robot Collaboration in the automotive assembly lines. The hybrid approach State-Space and HMMs outperforms standard continuous HMMs and a 3DCNN-based end-to-end deep architecture.
Using Artificial Intelligence for Developing English Language Teaching/Learning: An Analytical Study from University Students’ Perspective

As time passes on, machines are becoming more and more complex, fast-processing and intelligent. Being exactly like humans deducting, inferring and making decisions is still away, however some remarkable gains in the application of Artificial Intelligence (AI) techniques and machine learning have been recently recorded. Therefore, the current study seeks to examine strategies for effectively applying artificial intelligence (AI) applications to teach/learn English according to the university students&amp;rsquo; point of view. The study adopts the analytical descriptive approach in order to study and analyze the literature, to describe AI and the strategies of its employment for teaching/learning English. A 40-item questionnaire was used. It covers the following fields: AI strategies and its suitable applications for teaching/learning English, the effectiveness of these applications, their practical use, and the requirements for using them in the fields of teaching/learning English. Measuring the validity and reliability of the questionnaire revealed a Cronbach&amp;rsquo;s alpha of 0.931. &#x0D; &#x0D; The study sample consisted of 44 randomly selected male students from the English language stream at Northern Border University. A set of study instruments was applied. The results revealed a group of strategies suitable for employing AI for teaching/learning English. The results also indicated a very low level of employment of these strategies for teaching/learning English, and pointed out to their effectiveness if used in this field. The study has identified the training requirements from the study sample&amp;rsquo;s point of view. A suggested plan has been envisioned that includes the basics, objectives, content, processors, and evaluation methods for the employment of AI applications in the field of English education.
Opinion Analysis for Emotional Classification on Emoji Tweets using the Naïve Bayes Algorithm

Opinion Analysis is a research study needed to social media, since the content could become a trending topic and has a significant impact on social life. One of the social media that have a big contribution to cyberspace and information development is Twitter. In the Twitter application, users can insert images that represent emotions, facial expressions, or icons. Emoji is a graphic symbol in the form of an image to express a thing, with the Emoji, a text can be read and understood according to its meaning because the image represents it. Of the several things that have been mentioned then, the researchers conducted research on the classification of tweet content based on the use of Emojis. This study aims to determine the emotional uses of Twitter in one period. Every tweet on the Twitter timeline, which contains both text and Emojis, will be classified according to several categories. The algorithm used was Naïve Bayes. It calculated the probability of Emoji tweet to obtain the text classification with Emojis. The results of the classification of emotions are grouped with three categories, namely "angry," "joy," and "sad," it showed that the category "joy" had become the emotional trend of Twitter users where Emojis (x1f60a) dominate the most. Meanwhile, the accuracy of the algorithm used to reach 90% with a 70:30 holdout technique.
Geometric aspects of analog quantum search evolutions

We use geometric concepts originally proposed by Anandan and Aharonov [Phys. Rev. Lett. 65, 1697 (1990)] to show that the Farhi-Gutmann time-optimal analog quantum search evolution between two orthogonal quantum states is characterized by unit efficiency dynamical trajectories traced on a projective Hilbert space. In particular, we prove that these optimal dynamical trajectories are the shortest geodesic paths joining the initial and the final states of the quantum evolution. In addition, we verify they describe minimum uncertainty evolutions specified by an uncertainty inequality that is tighter than the ordinary time-energy uncertainty relation. We also study the effects of deviations from the time-optimality condition from our proposed Riemannian geometric perspective. Furthermore, after pointing out some physically intuitive aspects offered by our geometric approach to quantum searching, we mention some practically relevant physical insights that could emerge from the application of our geometric analysis to more realistic time-dependent quantum search evolutions. Finally, we briefly discuss possible extensions of our paper to the geometric analysis of the efficiency of thermal trajectories of relevance in quantum computing tasks.
Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems

In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm.
Joint QKD-Post-Quantum Cryptosystems

To extend the transmission distance and/or improve secret-key rate of QKD protocols, we propose to employ the joint QKD-post-quantum cryptosystems in which QKD is used for raw-key transmission while the post-quantum cryptography (PQC) subsystem to transmit parity bits for information reconciliation. We also describe a run-time configurable spatially coupled (SC)-LDPC code, derived from template quasi cyclic (QC)-LDPC, suitable for use in both information reconciliation and McEliece crypto-subsystem. For twin-field (TF)-QKD subsystem, the proposed joint cryptosystem, which takes the complexity of algorithm used to break the PQC subsystem into account, is able to achieve record distance of 1238 km over ultra-low-loss fiber.
SENSOR FUSION, GIS AND AI TECHNOLOGIES FOR DISASTER MANAGEMENT

Abstract. Modern Disaster Management Systems are based on several columns that combine theory and practice, software, and hardware being under technological advance. In all parts, spatial data is key in order to analyze existing structure, assist in risk assessment and update the information after a disaster incident. This paper focus on technological advances in several fields of spatial analysis putting together the advantages, limitations and technological aspects from well-known or even innovative methods, highlighting the huge potential of nowadays technologies for the field of Disaster Risk Management (DRM).A focus then is lying on GIS and Remote Sensing technologies that are showing the potential of high-quality sensors and image products that are getting easier to access and captured with recent technology. Secondly, several relevant sensors being thermal or laser-based are introduced pointing out the application possibilities, their limits, and potential fusion of them. Emphasis is further driven to Machine Learning techniques adopted from Artificial Intelligence that improve algorithms for auto-detection and represent an important step forwards to an integrated system of spatial data use in the Disaster Management Cycle. The combination of Multi-Sensor Systems, new Platform technologies, and Machine Learning indeed creates a very important benefit for the future.
Exploring Annotations and Hand Tracking in Augmented Reality for Remote Collaboration

Collaboration among remotely distributed professionals is often required in a maintenance context. Professionals need mechanisms with adaptive capabilities to enable knowledge transfer, since the necessary experience and expertise are usually spread among different professionals. To provide a shared understanding, Augmented Reality (AR) has been explored. In this paper, which is part of ongoing research using a human-centered design approach with partners from the industry sector, we describe a framework using annotations to improve the shared perceived realities of different professionals. The framework allows manually freezing the on-site professional context and sharing it with a remote expert to create annotations. Then, the on-site professional can visualize instructions through aligned and anchored annotations, using a see-through Head Mounted Display (HMD). In addition, annotations based on real-time video stream from a remote expert are also available. Hand tracking is used to manipulate the annotations, enabling the adjustment of their position and scale in the real-world according to the context, thus enriching the on-site professional experience and improving visualization of information while conducting maintenance procedures suggested by a remote expert.
THE CONCEPT OF “ENVIRONMENTAL CONSCIOUSNESS” IN THE MODERN PSYCHOLOGICAL SPACE

The importance of the rational, efficient and secure use of our planet's ecosystem resources is a major factor for development of an individual’s self-awareness as an agent of the outside world. It is a manifestation of an individual’s consciousness and self-consciousness in the ecosystem. Scientists emphasize the need to focus on the role of an individual’s activity in the ecological system. The study purpose is to perform literature review, illuminate the problems of psychology of consciousness and determine theoretical approaches to the “consciousness” concept that help develop the concept of “environmental consciousness”. Thus, the factors of future environmental professionals’ environmental consciousness formation could be identified. Therefore, approaches, forms, components forming the “environmental consciousness” were identified. In the modern psychological space, the concept of “consciousness” is studied by many psychologists, philosophers and sociologists and is understood as a higher form of reality reflection, common to society and associated with an individual’s worldview and thinking, as well as self-control and prediction of one’s own behaviour. For today, the following forms of consciousness have been formed: scientific, environmental, professional, philosophical, religious, pedagogical, moral, aesthetic, legal, political, etc. The following theoretical approaches, indicating the origin and development of the “consciousness” concept, were defined: cognitive, regulatory, hierarchical, structural, integrative. The “ecological consciousness” concept was singled out from the modern psychological space. The researchers who examine this concept usually use the cognitive and regulatory approaches. We analyzed various approaches to the study of “ecological consciousness” and reconstructed its evolution. The article authors focused on the motivational-value approach, which is effective for the formation of future environmental professionals’ environmental awareness. Ecological consciousness is a basic element of the worldview and the basis, with which an individual begin to understand their own relations with the nature in the evolutionary process. The formed ecological consciousness allows an individual to perceive the surrounding world and themselves in this world as an element of a single ecosystem. That is, during conscious activity, in the context of relations with the nature, an individual forms different substructures of ecological consciousness. The purpose of human interactions with the nature is to satisfy as much as possible the needs of society and the requirements for environmental conservation. &nbsp
Sensor scheduling for ground maneuvering target tracking in presence of detection blind zone

Continuous and stable tracking of the ground maneu- vering target is a challenging problem due to the complex terrain and high clutter.A collaborative tracking method of the multisensor network is presented for the ground maneuvering target in the presence of the detection blind zone (DBZ).First, the sensor scheduling process is modeled within the partially observable Markov decision process (POMDP) framework.To evaluate the target tracking accuracy of the sensor, the Fisher information is applied to constructing the reward function.The key of the proposed scheduling method is forecasting and early decisionmaking.Thus, an approximate method based on unscented sampling is presented to estimate the target state and the multi-step scheduling reward over the prediction time horizon.Moreover, the problem is converted into a nonlinear optimization problem, and a fast search algorithm is given to solve the sensor scheduling scheme quickly.Simulation results demonstrate the proposed nonmyopic scheduling method (Non-MSM) has a better target tracking accuracy compared with traditional methods.
A Survey of Parallel Clustering Algorithms Based on Spark

Clustering is one of the most important unsupervised machine learning tasks, which is widely used in information retrieval, social network analysis, image processing, and other fields. With the explosive growth of data, the classical clustering algorithms cannot meet the requirements of clustering for big data. Spark is one of the most popular parallel processing platforms for big data, and many researchers have proposed many parallel clustering algorithms based on Spark. In this paper, the existing parallel clustering algorithms based on Spark are classified and summarized, the parallel design framework of each kind of algorithms is discussed, and after comparing different kinds of algorithms, the direction of the future research is discussed.
Blind Information Reconciliation With Polar Codes for Quantum Key Distribution

We suggest a new protocol for the information reconciliation stage of quantum key distribution based on polar codes. The suggested approach is based on the blind technique, which is proved to be useful for low-density parity-check (LDPC) codes. We show that the suggested protocol outperforms the blind reconciliation with LDPC codes, especially when there are high fluctuations in quantum bit error rate (QBER).