Reviewerly/exHarmony huggingface dataset:
Image Encryption Algorithm Based on an Improved ML Neuron Model and DNA Dynamic Coding

Aiming at the problems of small key space, low security, and low algorithm complexity in a low-dimensional chaotic system encryption algorithm, an image encryption algorithm based on the ML neuron model and DNA dynamic coding is proposed. The algorithm first performs block processing on the R, G, and B components of the plaintext image to obtain three matrices, and then constructs a random matrix with the same size as the image components through logistic mapping and performs DNA encoding, DNA operation, and DNA decoding on the two parts. Second, it performs determinant permutation on the matrix by two different chaotic sequences obtained by logistic mapping iteration. Finally, it merges the block and image components to complete the image encryption and obtain the ciphertext image. Wherein, DNA encoding, DNA operation, and DNA decoding methods are all randomly and dynamically determined by the chaotic sequence generated by the ML neuron chaotic system. According to simulation results and performance analysis, the algorithm has a larger key space, can effectively resist various statistical and differential attacks, and has better security and higher complexity.
End-to-End Multi-Resolution 3D Capsule Network for People Action Detection

In this paper, we propose an end-to-end multi-resolution three-dimensional (3D) capsule network for detecting actions of multiple actors in a video scene. Unlike previous capsule, network-based action recognition does not specifically concern with the individual action of multiple actors in a single scene, our 3D capsule network takes advantage of multi-resolution technique to detect different actions of multiple actors that have different sizes, scales, and aspect ratios. Our 3D capsule network is built on top of 3D convolutional neural network (3DCNN) that extracts spatio-temporal features from video frames inside regions of interest generated by Faster RCNN object detection. We first apply our method to the problem of detecting illegal cheating activities in a classroom examination scene with multiple subjects involved. Second, we test our system on the publicly available and extensively studied UCF-101 dataset. We compare our method with several state-of-the-art 3DCNN-based methods, first the multi-resolution 3DCNN, the single-resolution 3D capsule network, and a combination of both these models. We show that models containing 3D capsule networks have a slight advantage over the conventional 3DCNN and multi-resolution 3DCNN. Our 3D capsule networks not only perform a classification of said actions but also generate videos of single actions. Our experimental results show that the use of multi-resolution pathways in the 3D capsule networks make the result even better. Such findings also hold even when we use pre-trained C3D (convolutional 3D) features to train these networks. We believe that the multiple resolutions capture lower-level features at different scales. At the same time, the 3D capsule layers combine these features in more complex ways than conventional convolutional models.
Comparison of the similarity between two quantum images

Comparing the similarity between digital images is an important subroutine in various image processing algorithms. In this study, we present three quantum algorithms for comparing the similarity between two quantum images. These algorithms are applied to binary, grey and color images for the first time. Without considering the image preparation, the proposed algorithms achieve exponential acceleration than the existing quantum and classical methods in all three cases. At the end of this paper, an experiment based on the real quantum computer of IBMQ and simulations verify the effectiveness of the algorithms.
Pose and Color-Gamut Guided Generative Adversarial Network for Pedestrian Image Synthesis

Tremendous transfer requirements in pedestrian reidentification (Re-ID) tasks have greatly promoted the remarkable success in pedestrian image synthesis, to relieve the inconsistency in poses and lighting. However, existing approaches are confined to transferring in a particular domain and are difficult to combine, since pose and color variables locate in two independent domains. To facilitate the research toward conquering this issue, we propose a pose and color-gamut guided generative adversarial network (PC-GAN) that performs joint-domain pedestrian image synthesis conditioned on certain pose and color-gamut through a delicate supervision design. The generator of the network comprises a sequence of cross-domain conversion subnets, where the local displacement estimator, color-gamut transformer, and pose transporter coordinate their learning pace to progressively synthesize images in desired pose and color-gamut. Ablation studies have demonstrated the efficacy and efficiency of the proposed network both qualitatively and quantitatively on Market-1501 and DukeMTMC. Furthermore, the proposed architecture can generate training images for person Re-ID, alleviating the data insufficiency problem.
Phones as a semiotic disadvantage

Abstract While previous studies have outlined the advantages of semiotic resources for meaning making and relationship building, not all semiotic resources are equal in their ability to enhance these features. Using linguistic ethnographic interviews and focus group discussions, this article examines whether mobile phones provide sufficient semiotic resources for English as a Foreign Language (EFL) migrants to effectively communicate, particularly for service situations regarding finances where they often have to speak in English with unknown interlocutors. Two important elements are uncovered regarding EFL migrants’ difficulty in using phones as a semiotic resource – (1) linguistic superiority, where they are judged by their English-speaking interlocutor as engaging in inferior English practices, and (2) paralinguistic insufficiency, where the lack of gestures and facial expressions takes away their ability to make meaning. These elements combined make telephone conversations difficult for many EFL migrants, affecting their expressiveness and ability to effectively communicate, leading to negative outcomes such as avoidance behaviors, and feelings of anxiety and disempowerment. Such difficulties lead to the finding that EFL migrants must be better accommodated when engaging in service transactions, with other, semiotically richer resources required to better aid their understanding and ease feelings of anxiety.
Micro Expression Recognition Using Delaunay Triangulation and Voronoi Tessellation

Facial Expression Recognition is a visual cue used for conveying emotions and intentions between human beings. The micro-expressions (MEs) are not visible to the human eye, making it challenging to capture the minute changes in the facial areas as the expressions change. As a result, automating the detection of ME is a challenging task. This work utilizes Delaunay Triangulation and Voronoi Diagram properties to segment Region of Interest (ROI) based on Action Unit indexes. The ROI-based feature extraction aided in improving the performance of the Micro-Expression Recognition (MER) system. The Cross-Database Evaluation (CDE) and Holdout Database Evaluation (HDE) are performed on three publicly available datasets CASMEII, SAMM, and SMIC (HS). The proposed approach resulted in an improved Unweighted Average Recall (UAR) and Unweighted F1 (UF1) scores by 6.09% and 4.36%, respectively. The results obtained with CDE and HDE demonstrate that the proposed model is robust compared to earlier studies.
An agnostic and efficient approach to identifying features from execution traces

Program comprehension is a necessary step during software understanding and maintenance. It is usually performed by analyzing data gathered from program execution. These execution traces reveal the relationship between high-level concepts (features) and low-level implementation details. However, identifying features from execution traces is difficult and time-consuming due to their large volume and complexity. Existing work assists the process by semi-automated tools, leveraging either human input or prior knowledge of the program implementation. It remains the key limitation towards a general method to build such mapping. In this paper, we proposed TRASE, an approach to identify features by segmenting the execution traces without the need for any human intervention. The segments are identified by mining recurring patterns on a sequence database, which is constructed by numerous execution traces gathered from normal use of a program. Each segment refers to a feature and the labels are inferred from the traces to assist program comprehension. The performance is evaluated on traces collected from android applications and a synthetic dataset. TRASE achieved up to 86% in F1 score and the result indicates that it is robust to highly variate traces while efficient for large data.
Design of Hierarchical Retrieval Model of Digital English Teaching Information Based on Ontology

In order to improve the effect of English teaching information retrieval, this paper designs a hierarchical retrieval model of digital English teaching information through ontology and constructs an intelligent system structure that can be used for hierarchical retrieval of digital English teaching information. Moreover, this paper proposes a method for judging the similarity of English teaching information structure based on non-negative matrix factorization and develops a hierarchical retrieval model of digital English teaching information based on ontology according to an improved algorithm. In addition, this paper uses the hierarchical search idea to design the hierarchical structure and study the system framework and hierarchical process. Finally, this paper verifies the performance of the system through simulation experiments. The experiment shows that the hierarchical retrieval model of digital English teaching information based on ontology proposed in this paper meets the basic needs of this paper to build an English teaching information system and can play a certain role in English teaching.
Pedagogical support of the development of civic maturity of students

The research is aimed at resolving the contradiction between the requirements outlined in the program documents for university graduates, characterizing their readiness for active manifestation of patriotism and the qualities of a civilly mature personality, and the lack of elaboration of pedagogical support for the formation of civic maturity of students. The purpose of the study is to identify effective means of pedagogical support for the development of civic maturity of students. The theoretical analysis is based on the integration of the provisions of two methodological approaches: personality-oriented and activity-oriented. As a result of the theoretical analysis, it is concluded that civic maturity is based on a scientific worldview, ideological conviction in the importance of realizing one's civil rights and duties, in their embodiment in concrete deeds. The scientific novelty of the research lies in the theoretical substantiation and empirical confirmation of the components of civic maturity: civic consciousness, civic feelings, civic activity and civic behavior. In the course of the study, significant positive correlations of all components of civic maturity among themselves and significant negative correlations of these components with the level of dysfunctional attitudes of the individual were established. The role of subjectivity of students as a key attribute of civic maturity is stated. As a methodological result, the article substantiates the levels and directions of pedagogical support for the development of civic maturity of students in the conditions of a university, its directions and targets are highlighted.
Big Data analytics for privacy through ND-homomorphic encryption

Rapidly rising the quantity of Big Data is an opportunity to flout the privacy of people. When high processing capacity and massive storage are required for Big Data, distributed networks have been used. There are several people involved in these activities, the system may contribute to privacy infringements frameworks have been developed for the preservation of privacy at various levels (e.g. information age, information the executives and information preparing) as for the existing pattern of huge information. We plan to frame this paper as a literature survey of these classifications, including the Privacy Processes in Big Data and the presentation of the Associate Challenges. Homomorphic encryption is particularised aimed at solitary single action on the ciphered information. Homomorphic enciphering is restrained to an honest operation on the encoded data. The reference to encryption project fulfils many accurate trading operations on coded numerical data; therefore, it protects the written in code-sensible information even more.
Adversarial Attacks on Deep-Learning RF Classification in Spectrum Monitoring with Imperfect Bandwidth Estimation

In a spectrum-monitoring scenario, a monitor will attempt to intercept and classify a signal. If the monitor uses a Convolutional Neural Network (CNN) for classification, the intercepted signal can frustrate classification attempts by employing an adversarial waveform. An adversarial waveform is a small additive perturbation at the transmitter, and is generated similarly to adversarial attacks against image classifiers. We demonstrate that without foreknowledge of the CNN employed at the monitor the communication system can develop such an adversarial waveform and deploy it thus transferring the attack. The adversarial waveform is created by constraining the signal-to-interference ratio at the transmitter, which has the dual benefits of making the adversarial waveform easy to deploy and mitigates impairment to the communications link. We also demonstrate the vulnerability of a spectrum monitoring system to this type of attack as a function of symbol rate uncertainty, where the monitor does not have an exact estimate of the symbol rate employed by the communications link. The spectrum monitor becomes more susceptible to the attack as bandwidth is increased.
Clustering of divergent multi-line structured light stripes based on structural constraint

Aiming at the confusion between multi-line structured light stripes in the 3D measurement system based on divergent multi-line structured light projection, an effective clustering method of multi-line structured light stripes based on structural constraint is proposed in this paper. The light stripes are numbered, and the light stripe image collected by CCD camera are divided into regions. According to the formula proposed in this paper, the region parameters are calculated, and the number of each light stripe is recognized to realize the clustering of line structured light stripe. Experimental results show that this method effectively avoided the confusion between light stripes.
Practical Reflection on Non-Traditional Forms of Conducting Classes in Cooperative Learning as a Way of Foreign Language Learning at a Pedagogical University

The purpose of the study is to present the experience of using various non-traditional forms of conducting classes in cooperative learning as a learning strategy that involves students’ cooperation in groups, increases motivation and contributes to critical thinking development. The paper analyses the practical experience of work that is aimed at implementing the principle of cooperative learning and makes it possible to activise the educational process in the discipline “Practice of Oral and Written Speech of the English Language”, to increase the level of foreign language proficiency and helps to form professional competencies of future teachers. Scientific novelty of the study lies in describing the stages of working with non-traditional forms of conducting classes in cooperative learning with language students at a pedagogical university. The paper shows the types and order of tasks when using a cooperative learning strategy. As a result of the study, the researchers have developed three-stage non-traditional forms of conducting classes within the cooperative learning strategy for students pursuing a degree in “Pedagogical Education”.
Learning Semantically Rich Network-based Multi-modal Mobile User Interface Embeddings

Semantically rich information from multiple modalities—text, code, images, categorical and numerical data—co-exist in the user interface (UI) design of mobile applications. Moreover, each UI design is composed of inter-linked UI entities that support different functions of an application, e.g., a UI screen comprising a UI taskbar, a menu, and multiple button elements. Existing UI representation learning methods unfortunately are not designed to capture multi-modal and linkage structure between UI entities. To support effective search and recommendation applications over mobile UIs, we need UI representations that integrate latent semantics present in both multi-modal information and linkages between UI entities. In this article, we present a novel self-supervised model—Multi-modal Attention-based Attributed Network Embedding (MAAN) model. MAAN is designed to capture structural network information present within the linkages between UI entities, as well as multi-modal attributes of the UI entity nodes. Based on the variational autoencoder framework, MAAN learns semantically rich UI embeddings in a self-supervised manner by reconstructing the attributes of UI entities and the linkages between them. The generated embeddings can be applied to a variety of downstream tasks: predicting UI elements associated with UI screens, inferring missing UI screen and element attributes, predicting UI user ratings, and retrieving UIs. Extensive experiments, including user evaluations, conducted on datasets from RICO, a rich real-world mobile UI repository, demonstrate that MAAN out-performs other state-of-the-art models. The number of linkages between UI entities can provide further information on the role of different UI entities in UI designs. However, MAAN does not capture edge attributes. To extend and generalize MAAN to learn even richer UI embeddings, we further propose EMAAN to capture edge attributes. We conduct additional extensive experiments on EMAAN, which show that it improves the performance of MAAN and similarly out-performs state-of-the-art models.
MetaFaaS

Meta-learning is a technique to transfer learning from a pre-built model on known tasks to build a model for unknown tasks. Graident-based meta-learning algorithms are one such family that use the technique of gradient descent for model updates. These meta-learning architectures are hierarchical in nature and hence incur large training times, which are prohibitive for industries relying on models trained using the most recent data to make relevant predictions. To address these issues, we propose MetaFaaS, a function-as-a-service (FaaS) paradigm on public cloud to build a scalable and cost-performance optimal deployment framework for gradient-based meta-learning architectures. We propose an analytical model to predict the cost and training time on cloud for a given workload. We validate our approach on multiple meta-learning architectures, (MAML, ANIL, ALFA) and attain a speed-up of over 5× in training time on FaaS. We also propose eALFA, a compute-efficient meta-learning architecture, which achieves a speed-up of > 9× as compared to ALFA. We present our results with four quasi-benchmark datasets in meta-learning, namely, Omniglot, Mini-Imagenet (Imagenet), FC100 (CIFAR), and CUBirds200.
<scp>DVO</scp> + <scp>LCLMF</scp>: A web service recommendation mechanism with <scp>QoS</scp> privacy preservation

Abstract QoS‐aware based web service recommendation is one of the crucial solutions to help users find high‐quality web services. To accurately predict the QoS values of candidate services, it is usually required to collect historical QoS data of users (QoS data for short). If these collected QoS data are improperly processed, QoS data privacy may be threatened. However, how to accurately predict the QoS values of candidate services while protecting QoS data privacy has not been well studied. In response to the situation, we propose a hybrid web service recommendation mechanism, which is divided into three parts. In the first part, the QoS data privacy preservation algorithm, which called DVO, is proposed based on keeping the cosine similarity of QoS data unchanged, that is, to realize the confusion of QoS data while ensuring the availability of QoS data remains unchanged. In the second part, a hybrid matrix factorization model based on location information and service features, which called LCLMF, is proposed to improve the accuracy of QoS values prediction. According to DVO and LCLMF, the DVO + LCLMF is designed in the third part, which can accurately predict QoS values while protecting QoS data privacy. The experimental results show that DVO + LCLMF can accurately predict the QoS values of candidate services on the basis of attaining QoS data privacy protection.
The Moderating Effect of The Cloud Computing on The Relationship Between Accounting Information Systems on The Firms Performance in Jordan

This research explores the relationship between Accounting Information systems (AIS) components, namely, System availability, security and integrity, confidentiality and privacy, and system quality with firm performance in Jordan, alongside the moderating influence of cloud computing. The data was collected in 2021 using a questionnaire from 263 respondents from the firms listed on Amman Stock Exchange that use cloud computing services. The findings revealed a significant relationship between the AIS components and cloud computing with firm performance, except for the system quality. In addition, cloud computing plays a significant moderating role in the relationship between System availability, and security &amp; integrity, with firm performance. This study suggested that the AIS components substantially influence management monitoring, which may affect the firm's effectiveness and lead to better performance. With the use of cloud computing, the firm will gain more as reliable data is always available.
Heterogeneous Social Linked Data Integration and Sharing for Public Transportation

Solid (social linked data) technology has made significant progress in social web applications developed, such as Facebook, Twitter, and Wikipedia. Solid is based on semantic web and RDF (Resource Description Framework) technologies. Solid platforms can provide decentralized authentication, data management, and developer support in the form of libraries and web applications. However, thus far, little research has been conducted on understanding the problems involved in sharing public transportation data through Solid technology. It is challenging to provide personalized and adaptable public transportation services for citizens because the public transportation data originate from different devices and are heterogeneous in nature. A novel approach is proposed in this study, in order to provide personalized sharing of public transportation data between different users through integrating and sharing these heterogeneous data. This approach not only integrates diverse data types into a uniform data type using the semantic web but also stores these data in a personal online data store and retrieves data through SPARQL on the Solid platform; these data are visualized on the web pages using Google Maps. To the best of our knowledge, we are the first to apply Solid in public transportation. Furthermore, we conduct performance tests of the new C2RMF (CSV to RDF Mapping File) algorithm and functional and non-functional tests to demonstrate the stability and effectiveness of the approach. Our results indicate the feasibility of the proposed approach in facilitating public transportation data integration and sharing through Solid and semantic web technologies.