Reviewerly/exHarmony huggingface dataset:
AVA: Adversarial Vignetting Attack against Visual Recognition

Vignetting is an inherent imaging phenomenon within almost all optical systems, showing as a radial intensity darkening toward the corners of an image. Since it is a common effect for photography and usually appears as a slight intensity variation, people usually regard it as a part of a photo and would not even want to post-process it. Due to this natural advantage, in this work, we study the vignetting from a new viewpoint, i.e., adversarial vignetting attack (AVA), which aims to embed intentionally misleading information into the vignetting and produce a natural adversarial example without noise patterns. This example can fool the state-of-the-art deep convolutional neural networks (CNNs) but is imperceptible to human. To this end, we first propose the radial-isotropic adversarial vignetting attack (RI-AVA) based on the physical model of vignetting, where the physical parameters (e.g., illumination factor and focal length) are tuned through the guidance of target CNN models. To achieve higher transferability across different CNNs, we further propose radial-anisotropic adversarial vignetting attack (RA-AVA) by allowing the effective regions of vignetting to be radial-anisotropic and shape-free. Moreover, we propose the geometry-aware level-set optimization method to solve the adversarial vignetting regions and physical parameters jointly. We validate the proposed methods on three popular datasets, i.e., DEV, CIFAR10, and Tiny ImageNet, by attacking four CNNs, e.g., ResNet50, EfficientNet-B0, DenseNet121, and MobileNet-V2, demonstrating the advantages of our methods over baseline methods on both transferability and image quality.
Resonant quantum principal component analysis

An energy-tunable ancillary qubit efficiently probes the principal components of a low-rank matrix.
Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers

Pretrained vision-and-language BERTs aim to learn representations that combine information from both modalities.We propose a diagnostic method based on cross-modal input ablation to assess the extent to which these models actually integrate cross-modal information.This method involves ablating inputs from one modality, either entirely or selectively based on cross-modal grounding alignments, and evaluating the model prediction performance on the other modality.Model performance is measured by modality-specific tasks that mirror the model pretraining objectives (e.g.masked language modelling for text).Models that have learned to construct cross-modal representations using both modalities are expected to perform worse when inputs are missing from a modality.We find that recently proposed models have much greater relative difficulty predicting text when visual information is ablated, compared to predicting visual object categories when text is ablated, indicating that these models are not symmetrically cross-modal.
TRCLA: A Transfer Learning Approach to Reduce Negative Transfer for Cellular Learning Automata

In most traditional machine learning algorithms, the training and testing datasets have identical distributions and feature spaces. However, these assumptions have not held in many real applications. Although transfer learning methods have been invented to fill this gap, they introduce new challenges as negative transfers (NTs). Most previous research considered NT a significant problem, but they pay less attention to solving it. This study will propose a transductive learning algorithm based on cellular learning automata (CLA) to alleviate the NT issue. Two famous learning automata (LA) entitled estimators are applied as estimator CLA in the proposed algorithms. A couple of new decision criteria called merit and and attitude parameters are introduced to CLA to limit NT. The proposed algorithms are applied to standard LA environments. The experiments show that the proposed algorithm leads to higher accuracy and less NT results.
Framework for the Ensemble of Feature Selection Methods

Feature selection (FS) has attracted the attention of many researchers in the last few years due to the increasing sizes of datasets, which contain hundreds or thousands of columns (features). Typically, not all columns represent relevant values. Consequently, the noise or irrelevant columns could confuse the algorithms, leading to a weak performance of machine learning models. Different FS algorithms have been proposed to analyze highly dimensional datasets and determine their subsets of relevant features to overcome this problem. However, very often, FS algorithms are biased by the data. Thus, methods for ensemble feature selection (EFS) algorithms have become an alternative to integrate the advantages of single FS algorithms and compensate for their disadvantages. The objective of this research is to propose a conceptual and implementation framework to understand the main concepts and relationships in the process of aggregating FS algorithms and to demonstrate how to address FS on datasets with high dimensionality. The proposed conceptual framework is validated by deriving an implementation framework, which incorporates a set of Phyton packages with functionalities to support the assembly of feature selection algorithms. The performance of the implementation framework was demonstrated in several experiments discovering relevant features in the Sonar, SPECTF, and WDBC datasets. The experiments contrasted the accuracy of two machine learning classifiers (decision tree and logistic regression), trained with subsets of features generated either by single FS algorithms or the set of features selected by the ensemble feature selection framework. We observed that for the three datasets used (Sonar, SPECTF, and WD), the highest precision percentages (86.95%, 74.73%, and 93.85%, respectively) were obtained when the classifiers were trained with the subset of features generated by our framework. Additionally, the stability of the feature sets generated using our ensemble method was evaluated. The results showed that the method achieved perfect stability for the three datasets used in the evaluation.
Immersive Virtual Reality Exergame Promotes the Practice of Physical Activity in Older People: An Opportunity during COVID-19

Life expectancy has increased in recent years. Physical activity has been postulated as a key element in active aging strategies. However, adherence to physical exercise programs has traditionally been low among the elderly, and the current situation of the COVID-19 pandemic has added extra impediments. Immersive virtual reality (IVR) devices could motivate this population to practice exercise. This study aimed to analyse the use of IVR exergames as a tool to facilitate physical exercise in older people. Four healthy older adults (males, 65–77 years) participated in the study. They carried out two exergaming sessions with HTC Vive ProTM. Outcomes were evaluated using the Simulator Sickness Questionnaire (SSQ), System Usability Scale (SUS), Game Experience Questionnaire (GEQ post-game module), an ad hoc satisfaction questionnaire, and perceived effort. All participants completed the sessions without adverse effects, with no SSQ symptoms reported. SUS scores were high in both sessions (SUS &gt; 85/100). Post-game GEQ scores were 3.08–3.41/4 (positive experiences) and 0.08–0.16/4 (negative experiences). Opinions showed high levels of satisfaction with the experience. Exergaming programs, based on commercial head-mounted displays, are a feasible alternative to traditional senior exercise, and could be a solution to the current situation that has arisen from the impact of the COVID-19 pandemic.
Cascadable all-optical NAND gates using diffractive networks

Abstract Owing to its potential advantages such as scalability, low latency and power efficiency, optical computing has seen rapid advances over the last decades. Here, we present the design and analysis of cascadable all-optical NAND gates using diffractive neural networks. We encoded the logical values at the input and output planes of a diffractive NAND gate using the relative optical power of two spatially-separated apertures. Based on this architecture, we numerically optimized the design of a diffractive neural network composed of 4 passive layers to all-optically perform NAND operation using diffraction of light, and cascaded these diffractive NAND gates to perform complex logical functions by successively feeding the output of one diffractive NAND gate into another. We numerically demonstrated the cascadability of our diffractive NAND gates by using identical diffractive designs to all-optically perform AND and OR operations, which can be formulated as $$\mathrm{AND}\left({I}_{1}, {I}_{2}\right)=\mathrm{NAND}\left(\mathrm{NAND}\left({I}_{1}, {I}_{2}\right), \mathrm{NAND}\left({I}_{1},{I}_{2}\right)\right)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>AND</mml:mi> <mml:mfenced> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mfenced> <mml:mo>=</mml:mo> <mml:mi>NAND</mml:mi> <mml:mfenced> <mml:mi>NAND</mml:mi> <mml:mfenced> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mfenced> <mml:mo>,</mml:mo> <mml:mi>NAND</mml:mi> <mml:mfenced> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mfenced> </mml:mfenced> </mml:mrow> </mml:math> and $$\mathrm{OR}\left({I}_{1}, {I}_{2}\right)=\mathrm{NAND}\left(\mathrm{NAND}\left({I}_{1}, {I}_{1}\right), \mathrm{NAND}\left({I}_{2},{I}_{2}\right)\right)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>OR</mml:mi> <mml:mfenced> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mfenced> <mml:mo>=</mml:mo> <mml:mi>NAND</mml:mi> <mml:mfenced> <mml:mi>NAND</mml:mi> <mml:mfenced> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mfenced> <mml:mo>,</mml:mo> <mml:mi>NAND</mml:mi> <mml:mfenced> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>I</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mfenced> </mml:mfenced> </mml:mrow> </mml:math> , respectively. We also designed an all-optical half-adder that takes two logical values as input and returns their sum and the carry using cascaded diffractive NAND gates. Cascadable all-optical NAND gates composed of spatially-engineered passive diffractive layers can serve optical computing platforms.
Building a large-scale and wide-area quantum Internet based on an OSI-alike model

The theory and experiment of quantum information have been studied extensively in recent years, and the feasibility of quantum communication has been proved. Although the fundamental technology is not yet mature, research on quantum internet should be conducted. To implement quantum internet, an architecture that describes how quantum nodes are linked to form networks and how protocol functions are vertically composed need to be developed urgently. In this paper, we present a novel design of a cluster-based structure to describe how quantum nodes are interconnected, and how the structure can improve the performance of qubit transmission and reduce the network complexity. The idea of the quantum local area network (QLAN) is proposed as an essential component of the quantum internet. Besides, each quantum repeater links to neighboring repeaters to form a core network, and multiple QLANs are connected through the core network. The core network can be grouped into different hierarchical quantum repeater networks according to needed service requirements. For the sake of interoperability and fast prototyping, we adopt the idea of OSI layering model of the current Internet in the design of quantum internet. Finally, we elaborate on the composition of quantum nodes and the realization of end-to-end communication.
Experimental continuous-variable quantum key distribution using a thermal source

Abstract Gaussian-modulated coherent-state (GMCS) continuous-variable quantum key distribution (CVQKD) protocol can allow authenticated users to share secret key with unconditional security. So far, all previous experimental implementations of GMCS CVQKD schemes are based on active modulations, i.e. amplitude and phase modulators and quantum random number generator (QRNG) are required. However, high-speed modulation with high extinction ratio and stability is challenging, which is extremely remarkable in chip-scale silicon photonic realization. While the passive-state-preparation (PSP) CVQKD scheme, which explores the intrinsic field fluctuations of a thermal source, avoids the uses of active modulations and QRNG. In this paper, we experimentally realize the intact PSP CVQKD through a realistic optical fiber channel using off-the-shelf amplified spontaneous emission source. In particular, specially designed frame synchronization method is used to build the correlation between the data measured from the two legitimate parties, and excess noise are synthetically controlled to generate secure secret keys at the metro-area distances when considering the practical and non-negligible finite-size effects under collective Gaussian attacks. Due to the avoidance of modulators and QRNG, the passive state encoding scheme provides a promising direction of applicable high-speed, chip-based and even sunlight-based CVQKD with less cost and complexity.
Blockchain and Big Data for Decentralized Management of IoT-Driven Healthcare Devices

In the modern world, humans were inventing and discovering many new inventions, but they never concern about their health. Health is the main thing for a human, by that they can achieve various goals in the competitive earth. Blockchain is a decentralized technology that has the digital records of the data that are stored as blocks using a cryptographic hash. Internet of Things (IoT) is a collection of Wi-Fi sensors, actuators, software, and computer devices. After that, it is incorporated with mobile devices for various purposes. Big data is also an emerging that tends to analyze, extract information from complex databases to achieve high output with data security and confidentiality. By the integration of both, IoT and big data provide many advanced features for the current century that will enhance to improve various applications like health care, automobile, financial, and risk management. In this chapter, we investigate peer-to-peer cryptographic secured blockchain technology for IoT and big data in healthcare applications like collecting the data, analyzing the data, and providing full-pledged care to the patients by the healthcare management systems by the integration of IoT and big data to provide high privacy, authentication, integrity, and nonrepudiation to the applications. And also, it summarizes about concept of healthcare applications, blockchain technology proposed architecture, working model, and future works.
What drives continuance intention to use a food-ordering chatbot? An examination of trust and satisfaction

Purpose Artificial intelligence (AI) customer service chatbots are a new application service, and little is known about this type of service. This study applies service quality, trust and satisfaction to predict users' continuance intention to use a food-ordering chatbot. Design/methodology/approach The proposed model and hypotheses are tested using online questionnaire responses to collect users' perceptions of such services. One hundred and eleven responses of actual users were received. Findings Empirical results show that anthropomorphism and service quality, such as problem-solving, are the antecedents of trust and satisfaction, while satisfaction has the most significant direct effect on the users' intention. Originality/value The results provide further useful insights for service providers and chatbot developers to improve services.
Multi-granularity relabeled under-sampling algorithm for imbalanced data

The imbalanced classification problem turns out to be one of the important and challenging problems in data mining and machine learning. The performances of traditional classifiers will be severely affected by many data problems, such as class imbalanced problem, class overlap and noise. When the number of one class in the data set is larger than other classes, class imbalanced problem will inevitably occur. Therefore, many researchers are committed to solving the problem of category imbalance and improving the overall classification performances of the classifier. The Tomek-Link algorithm was only used to clean data when it was proposed. In recent years, there have been reports of combining Tomek-Link algorithm with sampling technique. The Tomek-Link sampling algorithm can effectively reduce the class overlap on data, remove the majority instances that are difficult to distinguish, and improve the algorithm classification accuracy. However, the Tomek-Links under-sampling algorithm only considers the boundary instances that are the nearest neighbors to each other globally and ignores the potential local overlapping instances. When the number of minority instances is small, the under-sampling effect is not satisfactory, and the performance improvement of the classification model is not obvious. Therefore, on the basis of Tomek-Link, a multi-granularity relabeled under-sampling algorithm (MGRU) is proposed. This algorithm fully considers the local information of the data set in the local granularity subspace, and detects the local potential overlapping instances in the data set. Then, the overlapped majority instances are eliminated according to the global relabeled index value, which effectively expands the detection range of Tomek-Links. The simulation results show that when we select the optimal global relabeled index value for under-sampling, the classification accuracy and generalization performance of the proposed under-sampling algorithm are significantly better than other baseline algorithms.
Experimental investigation for the estimation of the intensity of solar irradiance on oblique surfaces by means of various models

Abstract In the literature several models have been derived by different authors in order to predict the solar irradiance intensity over inclined surfaces, however for the most models accuracy at various inclinations have not been verified. The study evaluated the estimation of solar irradiance at different tilt angles by means of different models based on the experimental measurements. For this purpose, two groups of models (isotropic and anisotropic) were carried out: the first group of models was used for estimating the diffuse solar irradiance component, and the second group was used for estimating the global solar irradiance. Five models have been selected and implemented for the estimation of the diffuse solar irradiance component, and five models have been selected for the estimation of global solar irradiance. The results of the analysis were compared with local experimental measurements for diffuse radiation and global irradiance. There are three tilt angles (0°, 30°, 60°) and a two-axis tracking system has been determent for comparison experiments with the model estimated results. The results showed all the selected models generated an error percentage in both the diffuse and global irradiance investigations.
TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection

In recent years, phishing scams have become the most serious type of crime involved in Ethereum, the second-largest blockchain platform. The existing phishing scams detection technology on Ethereum mostly uses traditional machine learning or network representation learning to mine the key information from the transaction network to identify phishing addresses. However, these methods adopt the last transaction record or even completely ignore these records, and only manual-designed features are taken for the node representation. In this paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to enhance phishing scams detection performance on Ethereum. Specifically, in the temporal edges representation module, we model the temporal relationship of historical transaction records between nodes to construct the edge representation of the Ethereum transaction network. Moreover, the edge representations around the node are aggregated to fuse topological interactive relationships into its representation, also named as trading features, in the edge2node module. We further combine trading features with common statistical and structural features obtained by graph neural networks to identify phishing addresses. Evaluated on real-world Ethereum phishing scams datasets, our TTAGN (92.8% AUC, and 81.6% F1score) outperforms the state-of-the-art methods, and the effectiveness of temporal edges representation and edge2node module is also demonstrated.
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation

User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via leveraging the advantages of graph neural networks (GNNs) or multi-task learning. However, most existing works do not take the complex dependencies among different behaviors of users into consideration. They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user’s behavioral patterns which will be used in downstream recommendation tasks. To tackle the challenge, in this paper, we first propose the concept of hyper meta-path to construct hyper meta-paths or hyper meta-graphs to explicitly illustrate the dependencies among different behaviors of a user. How to obtain a unified embedding for a user from hyper meta-paths and avoid the previously mentioned limitations simultaneously is critical. Thanks to the recent success of graph contrastive learning, we leverage it to learn embeddings of user behavior patterns adaptively instead of assigning a fixed scheme to understand the dependencies among different behaviors. A new graph contrastive learning based framework is proposed by coupling with hyper meta-paths, namely HMG-CR, which consistently and significantly outperforms all baselines in extensive comparison experiments.
Integrated room temperature single-photon source for quantum key distribution

High-purity single-photon sources (SPS) that can operate at room temperature are highly desirable for a myriad of applications, including quantum photonics and quantum key distribution. In this work, we realize an ultra-bright solid-state SPS based on an atomic defect in hexagonal boron nitride (hBN) integrated with a solid immersion lens (SIL). The SIL increases the source efficiency by a factor of six, and the integrated system is capable of producing over ten million single photons per second at room temperature. Our results are promising for practical applications of SPS in quantum communication protocols.