Reviewerly/exHarmony huggingface dataset:
Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

With the rapid evolution of Internet technology, fog computing has taken a major role in managing large amounts of data.The major concerns in this domain are security and privacy.Therefore, attaining a reliable level of confidentiality in the fog computing environment is a pivotal task.Among different types of data stored in the fog, the 3D point and mesh fog data are increasingly popular in recent days, due to the growth of 3D modelling and 3D printing technologies.Hence, in this research, we propose a novel scheme for preserving the privacy of 3D point and mesh fog data.Chaotic Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness, deterministic nature, sensitivity to initial conditions, ergodicity, etc.To boost encryption efficiency significantly, in this work, we propose a novel Chaotic Cat map.The sequence generated by this map is used to transform the coordinates of the fog data.The improved range of the proposed map is depicted using bifurcation analysis.The quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate entropy.We also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical attack.The experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.
PREDICTING CHANGES IN TEACHER PREPARATION FOR INTERCULTURAL INTERACTION IN REGIONS

Introduction. The article shows the relevance of the problem of changes in the preparation of teachers for intercultural interaction, taking into account the specifics of regions on the territory of Russia. Furthermore, the authors have identified the main tasks, the solution of which will contribute to the development of a predictive model of teacher preparation in the future. Aim. To present an argument for the stages of development of a predictive model of teacher preparation for intercultural interaction. Materials and Methods. The leading methods are the analysis of theoretical literature on the topic of the selected study. The choice of such research approaches as structural and structural-functional allows us to characterize the preparation process of teachers for intercultural interaction as multi-component, multistage and multi-phase in the context of interaction between the regional education system and the system of higher education in the Russian Federation. Results. The steps to develop a model for predicting the content of teacher preparation programs for intercultural interaction are proposed, the essence of the concept of Teachers Preparation and Changes in Preparation is concretized, and strategies for organizing teacher preparation for intercultural interaction are identified (overcoming existing linearity, practice orientation and continuity, and strategies for proactive preparation). Clarification of the concept of Changes in Preparation and determination of the meaning of the predicted changes will allow us to identify in the future the factors influencing the process of teacher preparation for intercultural interaction: socio-political factors determine a new (possibly different) understanding of the strategy for the development of higher pedagogical education in the context of strengthening ethnocultural processes (e.g., legislation in the field of development of regional policy, the position of the state in the field of regulation of ethnic processes and dissemination of ideas on preservation of ethnic cultures; the growth of ethnocultural processes); regional factors - analysis of the processes that characterize the level of development of regional development (e.g., the demographic structure of the population, social mobility, dynamically changing requirements of the labor market); personal factors - adaptation of existing and definition of new goals of pedagogical higher education established and emerging in a particular university to preserve national culture and maintain ethnocultural diversity (e.g., teachers’ readiness for intercultural interaction; pedagogical initiatives, a framework program to prepare teachers for intercultural interaction). Conclusion. Based on the study, it was suggested that the next study should lead to the development of components of a predictive model for teacher preparation for intercultural interaction.
Who Moderates on Twitch and What Do They Do?

Volunteer moderators are an increasingly essential component of effective community management across a range of services, such as Facebook, Reddit, Discord, YouTube, and Twitch. Prior work has investigated how users of these services become moderators, their attitudes towards community moderation, and the work that they perform, largely through interviews with community moderators and managers. In this paper, we analyze survey data from a large, representative sample of 1,053 adults in the United States who are active Twitch moderators. Our findings -- examining moderator recruitment, motivations, tasks, and roles -- validate observations from prior qualitative work on Twitch moderation, showing not only how they generalize across a wider population of livestreaming contexts, but also how they vary. For example, while moderators in larger channels are more likely to have been chosen because they were regular, active participants, mods in smaller channels are more likely to have had a pre-existing connection with the streamer. We similarly find that channel size predicts differences in how new moderators are onboarded and their motivations for becoming moderators. Finally, we find that moderators' self-perceived roles map to differences in the patterns of conversation, socialization, enforcement, and other tasks that they perform. We discuss these results, how they relate to prior work on community moderation across services, and applications to research and design in volunteer moderation.
Aerial image defogging method based on nonlocal feature structure tensor by UAV cameras with three-channel RGB cameras

The application of unmanned aerial system (UAV) remote sensing technology as a unique means of spatial data collection in various fields is based on high-resolution images captured by UAVs. However, due to the influence of atmospheric water vapor, haze, and other factors, the quality of UAV aerial images is limited. Considering that the model-based image fogging method developed according to the dark channel theory is widely used, the image edge after fogging incurs a “halo” effect owing to the excessive coarseness of the transmission map. We take advantage of the nonlocal structure tensor of the image to better protect the important details of the image edge while removing noise. The images we used are obtained from the public multi real world fog image dataset. Experiment results demonstrate that the proposed method can effectively eliminate the halo effect that occurs at the edges of an image after demisting.
Efficient detection of crossing pedestrians from a moving vehicle with an array of cameras

We describe a method for detecting crossing pedestrians and, in general, any object that is moving perpendicular to the driving direction of the vehicle. This is achieved by combining video snapshots from multiple cameras that are placed in a linear configuration and from multiple time instances. We demonstrate that the proposed array configuration imposes tight constraints on the expected disparity of static objects in a certain image region for a given camera pair. These regions are distinct for different camera pairs. In that manner, static regions can generally be distinguished from moving targets throughout the entire field of view when analyzing enough pairs, requiring only straightforward image processing techniques. On a self-captured dataset with crossing pedestrians, our proposed method reaches an F1 detection score of 83.66% and a mean average precision (MAP) of 84.79% on an overlap test when used stand-alone, being processed at 59 frames per second without GPU acceleration. When combining it with the Yolo V4 object detector in cooperative fusion, the proposed method boosts the maximal F1 scores of this detector on this same dataset from 87.86% to 92.68% and the MAP from 90.85% to 94.30%. Furthermore, combining it with the lower power Yolo-Tiny V4 detector in the same way yields F1 and MAP increases from 68.57% to 81.16% and 72.32% to 85.25%, respectively.
Survey of methods and evaluation of Retinex-inspired image enhancers

Spatial color algorithms (SCAs) are computer vision procedures widely used for image enhancement and human vision modeling. The main characteristic of SCA family is that they mimic the behavior of the human vision system (HVS), achieving in this way robustness and the capability to adjust their effect according to the image content. Here, we review 35 different, popular Retinex-inspired SCAs discussing and providing a set of measures for their evaluation in terms of image quality. To this purpose, we also introduce SCA-30, a real-world color image dataset made publicly available. The algorithms considered here include and spread from well-known Retinex implementations, Retinex variants, Milano–Retinex and related inspired enhancers, illumination/decomposition approaches, and deep learning-based techniques. Data and code used for the evaluation are made freely available to the community, to pursue further analysis and comparisons.
Cross-modality consistency learning for visible-infrared person re-identification

Visible-infrared (IR) person re-identification is a technology that matches the identity of the same person in two modalities. The main challenge is to discover the differentiations between different identities and the similarities between the two modalities. To solve this problem, we propose a cross-modality consistency learning network, which jointly considers cross-modal learning and distillation learning. It consists of two associated components: the feature adaptation network (FANet) and the modality learning module (MLM). The FANet combines global and local information to extract more discriminative features on the same identity images, and MLM is used to alleviate modal differences between visible and IR images. Our model could adaptively select the high-quality person image according to the potential contribution of each image, to avoid negative knowledge transfer. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the superiority of our approach over the current state-of-the-art technologies.
Noise Reduction in Qubit Readout with a Two-Mode Squeezed Interferometer

Fault-tolerant quantum information processing with flawed qubits and gates requires highly efficient, quantum nondemolition qubit readout. In superconducting circuits, qubit readout using coherent light with fidelity above 99% has been achieved by using quantum limited parametric amplifiers such as the Josephson parametric converter (JPC). However, further improvement of such measurement faces many challenges, including the vacuum fluctuation of the coherent light that limits measurement fidelity. In this work we demonstrate dispersive qubit readout with a two-mode squeezed light interferometer formed by two JPCs. The first JPC generates two-mode squeezed vacuum at its output, which is coherently recombined by the second JPC after one branch is phase shifted and displaced by its interaction with the qubit-cavity system on that arm of the interferometer. We observe a 31% improvement in the power signal-to-noise ratio (SNR) of projective readout after replacing vacuum fluctuation in the readout cavity with two-mode squeezed vacuum containing on average one photon. This demonstrates a more efficient way of improving SNR than by increasing readout power. Furthermore, we investigate the quantum properties of the two-mode squeezed light in the system through weak measurement. Surprisingly, we find that tuning the interferometer to be less projective increases the measurement efficiency relative to the optimum setting for projective measurement. These enhancements may enable remote entanglement with lower efficiency components in a system with qubits in both arms of the interferometer.
Stylometric analysis of characters in Shakespeare’s plays

Abstract Playwrights and screenwriters compose dialogues with characters from both genders. Assuming that men and women speak or write differently, can a great author take account of this difference? Previous studies have ascertained some stylistic markers that can discriminate between men and women either in writing or oral productions. The main aim of this study is to verify whether in Shakespeare’s plays female figures are talking in a distinct way compared with male ones. First, this study confirms the effectiveness of a set of predefined stylistic features using a tweet dataset extracted from CLEF-PAN corpora. Second, we demonstrate that the two genders present distinct styles when analyzing twenty-nine of Shakespeare’s plays, demonstrating that the author adopts a distinct style for each gender. Our experiments are grounded on two efficient classifiers (logistic regression and random forest) able to automatically identify short passages (500 or 1,000 word-tokens) corresponding to either a male or a female utterance with an accuracy of around 80%.
Sparse norm regularized attribute selection for graph neural networks

Graph Neural Networks (GNNs) have been widely used for graph learning tasks. The main aspect of GNN's layer-wise message passing is conducting attribute/feature propagation on graph. Most existing GNNs generally conduct feature propagation across all feature dimensions. However, in many real applications, attributes usually contain irrelevant and redundant noise. In this case, attribute/feature selection is desired to extract meaningful features and eliminate noisy ones for GNN's layer-wise propagation. Based on this observation, in this paper, we combine ℓ2,1/ℓ1-norm regularized attribute selection and GNNs together and propose a novel Attribute selection guided GNNs (AsGNNs) for graph data representation. AsGNNs aim to adaptively select some desired meaningful features/attributes that best serve GNNs. Moreover, an effective optimization framework has also been derived to train the proposed AsGNNs. The proposed AsGNNs provide a general framework which can incorporate any GNNs to conduct feature selection for layer-wise propagation. In this paper, we implement AsGNNs on both graph convolutional network (GCN) and graph attention network (GAT) and develop AsGCN and AsGAT for graph learning. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed AsGNNs (AsGCN, AsGAT) on semi-supervised learning tasks.
Hanging Out with My Pandemic Pal: Contextualizing Motivations of Anthropomorphizing Voice Assistants during COVID-19

COVID-19 has fundamentally changed the way people connect, collaborate, and socialize. With the ongoing pandemic amplifying people’s feelings of loneliness, voice assistants are growing as a pandemic-era staple of supporting people’s well-being and mitigating feelings of disconnectedness. Combining the uses and gratification approach and theory of anthropomorphism, this study examined social attraction and social presence as drivers for people to anthropomorphize voice assistants during the COVID-19 pandemic. Further, this study investigated whether loneliness, social disconnection, and attachment can moderate the effect of social attraction and social presence on the anthropomorphism of voice assistants. Drawing on survey data from 458 US voice assistant users, the results indicated that social attraction and social presence positively affect peoples’ anthropomorphism toward voice assistants. Moreover, the moderating effects of loneliness and social disconnection were examined and found positive impacts on the effect of social presence on anthropomorphism. The findings have implications for theorizing the anthropomorphism of digital media when face-to-face communication is less available. This study is also helpful for voice assistants’ developers and brands to design these smart devices appealing to customers and fostering a more customized and more robust user-technology interaction.
A One-Time Single-bit Fault Leaks All Previous NTRU-HRSS Session Keys to a Chosen-Ciphertext Attack

This paper presents an efficient attack that, in the standard IND-CCA2 attack model plus a one-time single-bit fault, recovers the NTRU-HRSS session key. This type of fault is expected to occur for many users through natural DRAM bit flips. In a multi-target IND-CCA2 attack model plus a one-time single-bit fault, the attack recovers every NTRU-HRSS session key that was encapsulated to the targeted public key before the fault. Software carrying out the full multi-target attack, using a simulated fault, is provided for verification. This paper also explains how a change in NTRU-HRSS in 2019 enabled this attack.
Weak Subtweakeys in SKINNY

Lightweight cryptography is characterized by the need for low implementation cost, while still providing sufficient security. This requires careful analysis of building blocks and their composition. SKINNY is an ISO/IEC standardized family of tweakable block ciphers and is used in the NIST lightweight cryptography standardization process finalist Romulus. We present non-trivial linear approximations of two-round SKINNY that have correlation one or minus one and that hold for a large fraction of all round tweakeys. Moreover, we show how these could have been avoided.
Modifying Bent Functions to Obtain the Balanced Ones with High Nonlinearity

Balanced Boolean functions with high nonlinearity are considered as major cryptographic primitives in the design of symmetric key cryptosystems. Dobbertin, in early nineties, gave an explicit construction for balanced functions on (even) n variables, with nonlinearity $$2^{n-1}-2^{\frac{n}{2}} + nlb(\frac{n}{2})$$ , where nlb(t) is the maximum nonlinearity of a balanced Boolean functions in t variables and conjectured that $$nlb(n) \le 2^{n-1} - 2^{\frac{n}{2}} + nlb(\frac{n}{2})$$ . This bound still holds. In this paper we revisit the problem. First we present a detailed combinatorial analysis related to highly nonlinear balanced functions exploiting the inter-related properties like weight, nonlinearity, and Walsh–Hadamard spectrum. Our results provide a general framework to cover the works of Sarkar-Maitra (Crypto 2000), Maity-Johansson (Indocrypt 2002), and Maity-Maitra (FSE 2004) as special cases. In this regard, we revisit the well-known construction methods through modification of bent functions and provide supporting examples for 8, 10, 12, and 14 variables. We believe these results will advance the understanding related to highly nonlinear balanced Boolean functions on even numbers of variables as well as the Dobbertin’s conjecture.
User Name-Based Compression and Encryption of Images Using Chaotic Compressive Sensing Theory

Abstract Simultaneous compression and encryption of images using a novel chaotic map is proposed in this paper. Both compression and encryption of images are carried out by the theory of compressive sensing (CS). A novel chaotic map with a high degree of chaos that is extremely sensitive to its initial parameters is proposed. A measurement matrix for the CS framework is designed based on the proposed map. The compression and recovery of images with different compression/sampling ratios are tested using the designed measurement matrix. Encryption of the compressed data is carried out using the proposed chaotic map and a novel user name-based encryption scheme. The entire encryption/decryption process proposed is completely dependent on the sequence obtained from the proposed chaotic map as well as the authorized user name. Thus, by this process, only authorized people with a valid user name will be able to decrypt the encrypted data and recover the actual underlying image. Simulation results on the proposed scheme with different images show that the average peak signal-to-noise ratio and structural similarity index values of about 32 dB and 0.861 are obtained for a sampling ratio of 0.5. Validations on the proposed map and the encryption process that were carried out using various standard tests prove the efficiency of the system in successfully compressing and encrypting the images. Also, the qualitative evaluation of the proposed compression–encryption process outperforms some of the existing works in the literature.
Cancer Diagnosis based on Artificial Intelligence, Machine Learning, and Deep Learning

Since cancer is a genetic disease induced by the gradual accumulation of several genetic modifications, it will provide a better forum for understanding the cancer formation and development process to recognize the variations in the expression profile of cancer cells compared to their normal counterparts. Developments in computer science have allowed many scientists to use computer techniques. For instance, multivariable analysis in order to assess the accuracy of the disease is substantially higher than the accuracy of empirical forecasts. As the most common applications have been discovered in recent years in clinical cancer research, artificial intelligence (AI), and specifically machine learning (ML) and deep learning (DL), the success of new levels of cancer prediction have reached based on the selection of genes and in the study of microdata. This paper discusses and summarizes the literature on AI’s application for three cancer diagnoses and projections, namely oral, lung and breast cancer. Different experiments are analyzed for the various types of systems that are used to detect cancer early. This paper, therefore, gives a new insight in AI technology in order to enhance the diagnosis and prediction of cancer and further increase health.
A Blockchain Enabled System for Enhancing Fintech Industry of the Core Banking Systems

The Financial Technologies (Fintech) plays a major role in transforming traditional financial institutions. Although it disrupts them, fintech enables these traditional financial institutions to reduce costs, introduce innovative solutions and gain a competitive edge over rivals and develop new digital technology and infrastructure, which increase the dependency on digital systems. The stronger dependence on these new technologies widens the cyber threat landscape towards digital systems/assets and these institutions. This landscape is rapidly increasing and getting more complex. Threat actors are becoming more organized, sophisticated, and cyber-crime has been commercialized. WannaCry, one of the recent large-scale cyber-attacks with global impact was an example of how vulnerabilities can be used in attacks for financial gains. This paper aims to critically analyze the spectrum of cyber threats facing the digital financial sector and illustrate the distributed ledger as an appealing solution which protects the database with its immutable property, as one of the most important assets to digital banking and its’ data. A blockchain model is illustrated to show how the technology can lead to safer implementation, enhanced operations, and to reduced exposure to cyber threats.
Augmented Reality in Childhood Education: Research and Development

Augmented Reality is a technology being used in an increasing number of fields as of late. This involves the field of education, where it is used in a variety of ways as an educational tool for all ages. This paper aims to research and design the components that such a digital application for preschool-aged children comprises, in order to create one. Various existing works are explored and analysed for the purpose of determining the optimal method to carry out specific functions. The educational aspect is Dhivehi-based, in an attempt to solve the problem of a lack of such applications in the digital market. This paper also highlights the results of a market study that was carried out to determine the demand for an application of this kind in the Maldivian community. System design is underlined, along with the details of a prototype in the current stage of development.