Reviewerly/exHarmony huggingface dataset:
Adaptive Modular Convolutional Neural Network for Image Recognition

Image recognition has long been one of the research hotspots in computer vision tasks. The development of deep learning is rapid in recent years, and convolutional neural networks usually need to be designed with fixed resources. If sufficient resources are available, the model can be scaled up to achieve higher accuracy, for example, VggNet, ResNet, GoogLeNet, etc. Although the accuracy of large-scale models has been improved, the following problems will occur with the expansion of model scale: (1) There may be over-fitting; (2) increasing model parameters; (3) slow model convergence. This paper proposes a design method for a modular convolutional neural network model which solves the problem of over-fitting and large model parameters by connecting multiple modules in parallel. Moreover, each module contains several submodules (three submodules in this paper) and fuses the features extracted from the submodules. The model convergence can be accelerated by using the fused features (the fused features contain more image information). In this study, we add a gate unit based on the attention mechanism to the model, which aims to optimize the structure of the model (select the optimal number of modules), allowing the model to select an optimum network structure by learning and dynamically reducing FLOPs (floating-point operations per second) of the model. Compared to VggNet, ResNet, and GoogLeNet, the structure of the model proposed in this paper is simple and the parameters are small. The proposed model achieves good results in the Kaggle datasets Cats-vs.-Dogs (99.3%), 10-Monkey Species (99.26%), and Birds-400 (99.13%).
Optimizing Scalability and Availability of Cloud Based Software Services Using Modified Scale Rate Limiting Algorithm

Testing and measuring the performance and scalability of cloud-based software services is essential for the further development and improvement of cloud computing. The criteria for the performance of cloud-based software services are intertwined with scalability, flexibility, and efficiency. Especially in the context of quickly growing the quantity of service delivery, the performance evaluation and testing of cloud-based software services is crucial to support the Service Level Agreement (SLA) compliant quality of delivery of these services. In this work, we use the Token bucket method, a scale rate-limiting algorithmic technique, to maximize the efficiency of cloud-based service performance. Increased availability and scalability in distributed systems has been a major obstacle for a long time. For cloud computing businesses to keep their customers happy, keep their trust, and keep their money, they need to make their services easy to use. The Spring Cloud-based software service uses Zuul as its gateway, therefore it must execute rate-limiting and guarantee service stability in the face of rapid growth. Inability to guarantee availability of essential services while using token bucket rate restriction. This research created a method for protecting against token overuse by using a URI configuration file in tandem with the Zuul gateway to potentially filter requests before receiving tokens. The token-bucket rate-limiting technique was created to perform the traffic-limiting function and guarantee the availability of the cloud platform's services. Measuring both scalability and availability helps illustrate the quality of service provided to customers. The performance of cloud-based software services is evaluated with regards to scalability using elasticity measurements. A growth in demand for cloud-based software services is a potential outcome of ongoing research and development in the field of cloud computing.
Bridging Africa’s income inequality gap: How relevant is China’s outward FDI to Africa?

This study examines whether: (i) the remarkable inflow of Chinese FDI to Africa matters for bridging the continent's marked income inequality gap, (ii) Africa's institutional fabric is effective in propelling Chinese FDI towards the equalisation of incomes in Africa and (iii) there exist relevant thresholds required for the various governance dynamics to cause Chinese FDI to equalise incomes in Africa. Our results, which are based on the dynamic GMM estimator and macrodata for 48 African countries, reveal the following. First, although Chinese FDI contributes to fairer income distribution in Africa, the effect is weak. Second, although Africa's institutional fabric matters for propelling Chinese FDI towards the equalisation of incomes across the continent, governance mechanisms for ensuring political stability, low corruption, and voice and accountability are critical. Finally, the critical masses required for these three key governance dynamics to cause Chinese FDI and other income inequality-reducing modules to reduce income inequality are 0.8, 0.5 and 0.1, respectively. These critical masses are thresholds at which governance is necessary but no longer sufficient to complement Chinese FDI to mitigate income inequality. Hence, at the attendant thresholds, complementary policies are worthwhile. Policy recommendations are provided in the conclusion.
Discrete Fusion Adversarial Hashing for cross-modal retrieval

Deep cross-modal hashing enables a flexible and efficient way for large-scale cross-modal retrieval. Existing cross-modal retrieval methods based on deep hashing aim to learn the unified hashing representation for different modalities with the supervision of pair-wise correlation, and then encode the out-of-samples via modality-specific hashing network. However, the semantic gap and distribution shift were not considered enough, and the hashing codes cannot be unified as expected under different modalities. At the same time, hashing is still a discrete problem that has not been solved well in the deep neural network. Therefore, we propose the Discrete Fusion Adversarial Hashing (DFAH) network for cross-modal retrieval to address these issues. In DFAH, the Modality-Specific Feature Extractor is designed to capture image and text features with pair-wise supervision. Especially, the Fusion Learner is proposed to learn the unified hash code, which enhances the correlation of heterogeneous modalities via the embedding strategy. Meanwhile, the Modality Discriminator is designed to adapt to the distribution shift cooperating with the Modality-Specific Feature Extractor in an adversarial way. In addition, we design an efficient discrete optimization strategy to avoid the relaxing quantization errors in the deep neural framework. Finally, the experiment results and analysis on several popular datasets also show that DFAH outperforms the state-of-the-art methods for cross-modal retrieval.
Gaussian quantum estimation of the loss parameter in a thermal environment

Lossy bosonic channels play an important role in a number of quantum information tasks, since they well approximate thermal dissipation in an experiment. Here, we characterize their metrological power in the idler-free and entanglement-assisted cases, using respectively single- and two-mode Gaussian states as probes. In the problem of estimating the lossy parameter, we study the energy-constrained quantum Fisher information (QFI) for generic temperature and lossy parameter regimes, showing qualitative behaviours of the optimal probes. We show semi-analytically that the two-mode squeezed-vacuum state optimizes the QFI for any value of the lossy parameter and temperature. We discuss the optimization of the {\it total} QFI, where the number of probes is allowed to vary by keeping the total energy-constrained. In this context, we elucidate the role of the "shadow-effect" for reaching a quantum advantage. We also consider a photon-number normalization for the environment, widely used in the analysis of quantum illumination and quantum reading protocols. With this normalization, we prove that the large bandwidth TMSV state is the optimal probe for any parameter value. Here, the quantum advantage is of at most a factor of $2$, and is reached in the bright environment case for {\it any} lossy parameter values. Finally, we discuss the implications of our results for quantum illumination and quantum reading applications.
Fuzzy Elliptic Curve Cryptography for Authentication in Internet of Things

The security and privacy of the network in Internet of Things (IoT) systems are becoming more critical as we are more dependent on smart systems. Considering that packets are exchanged between the end user and the sensing devices, it is then important to ensure the security, privacy, and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for IoT systems. In this article, in order to improve the authentication and the encryption in IoT systems, we present a novel method of authentication and encryption based on elliptic curve cryptography (ECC) using random numbers generated by fuzzy logic. We evaluate our novel key generation method by using standard randomness tests, such as: frequency test, frequency test with mono block, run test, discrete Fourier transform (DFT) test, and advanced DFT test. Our results show superior performance compared to existing ECC based on shift registers. In addition, we apply some attack algorithms, such as Pollard’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> and Baby-step Giant-step, to evaluate the vulnerability of the proposed scheme.
Design of artificial intelligence image encryption algorithm based on hyperchaos

The artificial intelligence is an inevitable breakthrough direction, and the privacy protection of images in the artificial intelligence environment has become a hotspot. An image encryption algorithm that can be applied in the AI-based work combining compression sensing with Rabinovich hyperchaos is proposed. Firstly, the plaintext image will be transformed into a sparse form by DWT transform. With the improved hyperchaotic sequence, the double reset mechanism of the bit plane and the encryption engine function are designed to perform circular diffusion operation. Finally, the combined random Gaussian measurement matrix will perform compressed sensing sampling on the encrypted image. Simulation experiments show that the encryption algorithm can pass the plaintext sensitivity test (with the NPCR of 0.9978 and the UACI of 0.3337), the correlation test (with the best result of −0.0021), and the information entropy test (with the result of 7.9987), which shows high security to resist several types of attacks.
A Model to Develop Chatbots for Assisting the Teaching and Learning Process

Recently, in the commercial and entertainment sectors, we have seen increasing interest in incorporating chatbots into websites and apps, in order to assist customers and clients. In the academic area, chatbots are useful to provide some guidance and information about courses, admission processes and procedures, study programs, and scholarly services. However, these virtual assistants have limited mechanisms to suitably help the teaching and learning process, considering that these mechanisms should be advantageous for all the people involved. In this article, we design a model for developing a chatbot that serves as an extra-school tool to carry out academic and administrative tasks and facilitate communication between middle-school students and academic staff (e.g., teachers, social workers, psychologists, and pedagogues). Our approach is designed to help less tech-savvy people by offering them a familiar environment, using a conversational agent to ease and guide their interactions. The proposed model has been validated by implementing a multi-platform chatbot that provides both textual-based and voice-based communications and uses state-of-the-art technology. The chatbot has been tested with the help of students and teachers from a Mexican middle school, and the evaluation results show that our prototype obtained positive usability and user experience endorsements from such end-users.
Intelligent Amphibious Ground-Aerial Vehicles: State of the Art Technology for Future Transportation

Amphibious ground-aerial vehicles fuse flying and driving modes to enable more flexible air-land mobility and have received growing attention recently. By analyzing the existing amphibious vehicles, we highlight the autonomous fly-driving functionality for the effective uses of amphibious vehicles in complex three-dimensional urban transportation systems. We review and summarize the key enabling technologies for intelligent flying-driving in existing amphibious vehicle designs, identify major technological barriers and propose potential solutions for future research and innovation. This paper aims to serve as a guide for research and development of intelligent amphibious vehicles for urban transportation toward the future.
An Efficient Transfer and Ensemble Learning Based Computer Aided Breast Abnormality Diagnosis System

Breast cancer is the second most deadly type of cancer globally among women and can be prevented to a great extent in the case of early detection.In order to raise the survival rate, research scientists have conducted several experiments to develop tools to alleviate this problem, including Computer-Aided Diagnosis (CADx) systems.Deep Learning and its important sub-field Convolutional Neural Networks (CNN)s have revolutionized (CADx) development research.While the Curated Breast Imaging Subset of Digital Database for Screening Mammography, or the CBIS-DDSM dataset, has been classified using different pre-trained architectures, few of them have used ensemble learning to provide a more robust and accurate architecture.To the best of our knowledge, we are the first to integrate the application of the state-of-the-art pre-trained model called EfficientNet along with other pre-trained models for the part, and subsequently, the models were concatenated (ensembled).With the application of pre-trained CNN-based models, we are able to address the problem of not having a large dataset.Nevertheless, with the EfficientNet family offering better results with fewer parameters, we obtained significant improvement in accuracy, and later ensemble learning was applied to provide robustness for the network.After performing 10-fold crossvalidation, our experiments yielded promising test accuracy results, 96.05% and 85.71% for abnormality type and pathology diagnosis classification, respectively.
An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16.
A new framework for collaborative filtering with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e756" altimg="si3.svg"><mml:mi>p</mml:mi></mml:math>-moment-based similarity measure: Algorithm, optimization and application

In this paper, a general framework of user-based collaborative filtering (CF) is developed with a new p-moment-based similarity measure. The p-moment-based statistics (PMS) of individual rating data are employed to analyze the user rating habit, thereby facilitating the performance improvement of the CF algorithm. On the basis of the PMS, a new yet comprehensive similarity measure is proposed to quantify the distance between two users with focus on both the users’ preferences on items and their rating habits. Compared with the traditional ones, our proposed similarity measure is more general with clearer application insights in complicated situations. Furthermore, the weights of different statistics are regarded as adjustable parameters that are determined by utilizing the particle swarm optimization technique so as to achieve good prediction performance. Based on the proposed similarity measure with optimized weights, the neighborhood set consisting of similar users is formed and then the user-based rating prediction is eventually provided. The developed CF algorithm has advantages of high prediction accuracy and wide application potential. Moreover, this CF algorithm is applied on a real-world disease (Friedreich’s ataxia) assessment system in order to assist diagnosis for patients with uncertain/missing information. Experimental results demonstrate the validity and efficiency of the proposed algorithm.
Arabic sentiment analysis using dependency-based rules and deep neural networks

With the growth of social platforms in recent years and the rapid increase in the means of communication through these platforms, a significant amount of textual data is available that contains an abundance of individuals’ opinions. Sentiment analysis is a task that supports companies and organizations to evaluate this textual data with the intention of understanding people’s thoughts concerning services or products. Most previous research in Arabic sentiment analysis relies on word frequencies, lexicons, or black box methods to determine the sentiment of a sentence. It should be noted that these approaches do not take into account the semantic relations and dependencies between words. In this work, we propose a framework that incorporates Arabic dependency-based rules and deep learning models. Dependency-based rules are created by using linguistic patterns to map the meaning of words to concepts in the dependency structure of a sentence. By examining the dependent words in a sentence, the general sentiment is revealed. In the first stage of sentiment classification, the dependency grammar rules are used. If the rules are unsuccessful in classifying the sentiment, the algorithm then applies deep neural networks (DNNs). Three DNN models were employed, namely LSTM, BiLSTM, and CNN, and several Arabic benchmark datasets were used for sentiment analysis. The performance results of the proposed framework show a greater improvement in terms of accuracy and F1 score and they outperform the state-of-the-art approaches in Arabic sentiment analysis.
Acceptability of the COVID-19 contact-tracing app – Does culture matter?

During the pandemic, several countries deployed contact-tracing apps in order to contain or reduce the community spread of COVID-19. However, the success rate in terms of acceptance and use of these apps was reportedly low. Using information gathered from citizens across four European countries and the United States of America, this study explores the role of national culture in relation to the acceptance of these apps. Using partial least squares structural equation modelling (PLS-SEM), an analysis was undertaken of 3595 records from a cross-country survey dataset that is in the public domain and can be obtained from the Centre for Open Science (Study 1). This analysis was followed by another survey comprising 910 respondents (Study 2). The research model was then validated by using a qualitative approach and undertaking interviews with 51 participants from four countries (Study 3). The results confirmed the moderating role of national culture on the acceptability of the contact-tracing apps in relation to power-distance, masculinity, individualism, long-term orientation and indulgence in the pre-deployment phase (Study 1). There were, however, no significant differences in acceptability of the apps between countries in relation to uncertainty avoidance; and none of the hypotheses in Study 2 was supported. The study concludes that national culture is significant in terms of the acceptance of COVID-19 apps only during the pre-deployment phase; therefore attention is required with pertinence to pre-deployment strategies. Recommendations regarding how governments and public health institutions can increase the acceptability of contact-tracing apps have been highlighted.
Automated Attack Synthesis by Extracting Finite State Machines from Protocol Specification Documents

Automated attack discovery techniques, such as attacker synthesis or model-based fuzzing, provide powerful ways to ensure network protocols operate correctly and securely. Such techniques, in general, require a formal representation of the protocol, often in the form of a finite state machine (FSM). Unfortunately, many protocols are only described in English prose, and implementing even a simple network protocol as an FSM is time-consuming and prone to subtle logical errors. Automatically extracting protocol FSMs from documentation can significantly contribute to increased use of these techniques and result in more robust and secure protocol implementations.In this work we focus on attacker synthesis as a representative technique for protocol security, and on RFCs as a representative format for protocol prose description. Unlike other works that rely on rule-based approaches or use off-the-shelf NLP tools directly, we suggest a data-driven approach for extracting FSMs from RFC documents. Specifically, we use a hybrid approach consisting of three key steps: (1) large-scale word-representation learning for technical language, (2) focused zero-shot learning for mapping protocol text to a protocol-independent information language, and (3) rule-based mapping from protocol-independent information to a specific protocol FSM. We show the generalizability of our FSM extraction by using the RFCs for six different protocols: BGPv4, DCCP, LTP, PPTP, SCTP and TCP. We demonstrate how automated extraction of an FSM from an RFC can be applied to the synthesis of attacks, with TCP and DCCP as case-studies. Our approach shows that it is possible to automate attacker synthesis against protocols by using textual specifications such as RFCs.
Low-Bandwidth Threshold ECDSA via Pseudorandom Correlation Generators

Digital signature schemes are a fundamental component of secure distributed systems, and the theft of a signing-key might have huge real-world repercussions e.g., in applications such as cryptocurrencies. Threshold signature schemes mitigate this problem by distributing shares of the secret key on several servers and requiring that enough of them interact to be able to compute a signature. In this paper, we provide a novel threshold protocol for ECDSA, arguably the most relevant signature scheme in practice. Our protocol is the first one where the communication complexity of the preprocessing phase is only logarithmic in the number of ECDSA signatures to be produced later, and it achieves therefore a so-called silent preprocessing. Our protocol achieves active security against any number of arbitrarily corrupted parties.