Reviewerly/exHarmony huggingface dataset:
Detecting chaos in adversarial examples

The puzzling phenomenon of adversarial examples continues to attract significant research within the machine learning community. The confirmation that adversarial examples can arise in natural real-life circumstances has but increased the interest. While several methods have been proposed for both generating adversarial examples and defending against them, in this work we focus on a previous serendipitous discovery indicating that they can be considered as chaotic signals. More specifically, it has been recently shown that measures of chaoticity in the input signal can be used to detect adversarial examples efficiently. In this work, we extend that approach in two aspects, leading to significant improvements in detection accuracy as demonstrated by results obtained in experiments with four datasets and using seven different attack methods. • Applying chaos theory to detect adversarial examples, two different contributions. • Lyapunov exponents in the image space and also spatial information are considered. • Adversarial examples are generated using 7 state-of-the-art methods. • Use of common datasets like MNIST, Fashion-MNIST, CIFAR-10 and Tiny-ImageNet. • Three additional adversarial detection methods are trained and compared. • A proper adaptive attack is developed to test the method. • Results are compared for the different approaches in an ablation study.
Semantic Web Undertaking Effort Estimation Utilizing COCOMO II, SVM and NN

Programming exertion and cost assessment are urgent pieces of programming project improvement. It decides the spending planning time, and assets expected to foster a product project. The achievement of a product project advancement relies primarily upon the exactness of programming exertion and cost assessment. The semantic web basically focused upon the quality assessment utilizing the effort estimation. The COCOMO model utilized for assessment is contrasted and the proposed work and further based on disarray framework boundaries accuracy, review, f-measure, precision are determined and similar examination utilizing proposed model against COCOMO II, SVM and NN is performed in MATLAB. The general investigation shows that the proposed model accomplished better outcomes as contrast with COCOMO II model. The near examination of assessment boundaries (tp, fp, tn, fn) are registered by utilizing proposed model against COCOMO II, SVM and NN models. The outcome utilizing exertion assessment is isolated based on two standards, first guideline depends on 5% exertion investigation and second principle depends on 10%. The normal % of exertion investigation utilizing proposed model is registered as 127.729. The execution characterizes that the proposed model is superior to the COCOMO II model using fuzzy logic.
Fs-Set Functions-Images

In this study, we defined Fs-Set function between two Fs-Sets, identify Image of Fs-Subset under an Fs-Set function and we study some images properties.
TLRNN: Two-Level RNN Based Personalized Recommendation in Tourism Domain

Nowadays people’s interactions in social media are getting increased gradually due to their usage of various gadgets and the evolution of technology. Lifestyle changes play a vital role in everyday life and people started consuming food outside rather than home and travel more than usual practice. Information related to location and the user check-in data is huge and it is difficult for the system to identify the individual user interests for good service. Recommendation helps users to narrow down their choices in social networks and provides them personalized service. A novel Two Level Recurrent Neural Network (TLRNN) has been used to provide a personalized recommendation in the tourism domain and it uses the user’s profile, search history, and user interests in the first level of the neural network to classify the places. By comparing the user profile and search history against the predefined table, user preferences in the category of place have been ranked. Later, it considers the category, time, and distance for top n recommendation of places to the users. This technique overcomes the cold-start user problem in a tourism domain and provides the top-ranked recommendation list to the user. Results of RNN by training with time and distance have been compared with the results of RNN without time and distance. The final results show that the proposed technique narrows down the user’s preference places in the tourism domain.
Blockchain and Competitive Business Performance

The study explores the mechanism and path how blockchain enhance firm performance. 360 participants gave their responses for the study. The analysis proves that blockchain enhance performance directly as well as through supply chain flexibility and agility. The study has theoretical and practical implications. The study provide basis for further research about blockchain and developing models for industrial use. The study is useful for managers to implement blockchain in supply chains. The study provide basis to explore application of blockchain in health care, banking, financial sector and many more.
Privacy Enhanced Healthcare Data Management Using Associative Data Mining Approaches

Hospital medical records with health examination findings can be integrated to assist in uncovering the link between aberrant test results and illness. It is possible to establish a disease-preventive knowledge center using these integrated data by performing associated rule mining on the results. In order to integrate data, sensitive patient information must be shared. Patients’ privacy may be violated by the disclosure of sensitive information. Thus, privacy-preserving associated rule mining in physically partitioned healthcare data is addressed in this article. The suggested technique is further evaluated in terms of data protection, transmission, and computing costs.
Semi-supervised classifier guided by discriminator

Some machine learning applications do not allow for data augmentation or are applied to modalities where the augmentation is difficult to define. Our study aimed to develop a new method in semi-supervised learning (SSL) applicable to various modalities of data (images, sound, text), especially when augmentation is hard or impossible to define, i.e., medical images. Assuming that all samples, labeled and unlabeled, come from the same data distribution, we can say that labeled and unlabeled data sets used in the semi-supervised learning tasks are similar. Based on this observation, the data embeddings created by the classifier should also be similar for both sets. In our method, finding these embeddings is achieved based on two models-classifier and an auxiliary discriminator model, inspired by the Generative Adversarial Network (GAN) learning process. The classifier is trained to build embeddings for labeled and unlabeled datasets to cheat discriminator, which recognizes whether the embedding comes from a labeled or unlabeled dataset. The method was named the DGSSC from Discriminator Guided Semi-Supervised Classifier. The experimental research aimed evaluation of the proposed method on the classification task in combination with the teacher-student approach and comparison with other SSL methods. In most experiments, training the networks with the DGSSC method improves accuracy with the teacher-student approach. It does not deteriorate the accuracy of any experiment.
Design of plasmonic enhanced all-optical phase-change memory for secondary storage applications

Phase-change optical device has recently gained tremendous interest due to its ultra-fast transmitting speed, multiplexing and large bandwidth. However, majority of phase-change optical devices are only devoted to on-chip components such as optical tensor core and optical main memory, while developing a secondary storage memory in an optical manner is rarely reported. To address this issue, we propose a novel phase-change optical memory based on plasmonic resonance effects for secondary storage applications. Such design makes use of the plasmonic dimer nanoantenna to generate plasmonic resonance inside the chalcogenide alloy, and thus enables the performance improvements in terms of energy consumption and switching speed. It is found that choosing height, radius, and separation of the plasmonic nanoantenna as 10 nm, 150 nm, and 10 nm, respectively, allows for a write/erase energies of 100 and 240 pJ and a write/erase speed of 10 ns for crystallization and amorphization processes, respectively. Such performance merits encouragingly prevail conventional secondary storage memories and thus pave a route towards the advent of all-optical computer in near future.
VMetaFlow: A Meta-Framework for Integrating Visualizations in Coordinated View Applications

The analysis and exploration of complex data sets are common problems in many areas, including scientific and business domains. This need has led to substantial development of the data visualization field. However, the diversity of problems to which visual data analysis is applied hinders the implementation of a universal solution that meets the current and future needs of all disciplines. In this paper, we present VMetaFlow, a graphical meta-framework to design interactive and coordinated views applications for data visualization. Our meta-framework is based on data flow diagrams since they have proved their value in simplifying the design of data visualizations. VMetaFlow operates as an abstraction layer that encapsulates and interconnects visualization frameworks in a web-based environment, providing them with interoperability mechanisms. The only requirement is that the visualization framework must be accessible through a JavaScript API. We propose a novel data flow model that allows users to define both interactions between multiple data views and how the data flows between visualization and data processing modules. In contrast with previous data-flow-based frameworks for visualization, we separate the view interactions from data items, broadening the expressiveness of our model and supporting the most common types of multi-view interactions. Our meta-framework allows visualization and data analysis experts to focus their efforts on creating data representations and transformations for their applications, whereas nonexperts can reuse previously developed components to design their applications through a user-friendly interface. We validate our approach through a critical inspection with visualization experts and two case studies. We have carefully selected these case studies to illustrate its capabilities. Finally, we compare our approach with the subset flow model designed for multiple coordinated views.
Repair of Unstable Sociomaterial Relations: A Study on Librarians’ and Social Workers’ Practices in Addressing the Needs of Their Clients

In this paper we address the increased digitalization of the fabric of modern welfare societies, and the expectations on individuals to be independent and digitally capable that follows from this. We focus on enactments of repair aimed at maintaining the lives of individuals situated in unstable sociomaterial relations in the context of social care and a public library in Sweden. The purpose was to explore enactments of repair in two Swedish empirical contexts: one in a social service setting, and one in a municipal library setting. The analysis was conducted with the help of the concept of repair, understood as maintenance or transformation. We conducted interviews and observations in these two settings and analyzed these with the help of the concept of repair. The analysis shows that in the context of social service, repair was enacted differently in relation to different interpretations of existing legislation concerning minimal livelihoods, and that in the context of the public library repair was enacted in relation to expectations of the library visitors to become digitally independent. The analysis showed that in both of these contexts repair was enacted as maintenance rather than as transformation.
A Study on AI Creations and Patent Law Protection

The process of conceiving an invention, how AI may contribute to the conception process, and how AI inventions should be protected by patent law are examined. In our approach and analysis, AI remains in the category of sophisticated tools used by natural persons to create inventions. The approach is assessed against a decision by the Australian Federal Court acknowledging that the AI machine DABUS can be held as an inventor. As a result of our study, however, we believe that it is at least questionable whether DABUS can be considered an autonomous inventor. Thus, in those cases where someone names an AI machine as the inventor, we propose setting a high standard of proof in requesting to produce as evidence the actual machine output claimed as the invention and to illustrate how the machine operates to arrive at such output.
Finding Second Hierarchy Points for Cooperative Multi-Vehicles Delivery Route with P-median and Voronoi Diagram in Various Driving Area

With the recent development of autonomous driving technology, various unmanned vehicles such as serving robots, mobile delivery robots, and drones have been developed. However, each robot has a different driving area, so it is necessary to find cooperative route for optimal unmanned delivery. In this paper, a cooperative scenario of an autonomous courier vehicle and five mobile delivery vehicles perform unmanned delivery tasks. The courier vehicle moves from a warehouse to the second hierarchy point, and drops off the five mobile delivery vehicles in order to transport goods to the doorstep of the destinations. We define the second hierarchy point by using the P-Median method and the Voronoi diagram method, and compare the results of each method using real map data. The points using P-Median are calculated based on the actual road distance and have shorter average moving distance than the Voronoi diagram method. The Voronoi diagram uses the Euclidian distance based on location information, which defines the second hierarchy point faster than the P-Median method. By comparing the results, we consider the Voronoi diagram method have capability in the cooperative multi-vehicle delivery route problem. The result of this paper have potential to be used in rough and remote areas where road information and infrastructure are lacking.
Multi-Target Domain Transfer Detector on Bad Weather Conditions

With the popularization of object detection in fields such as autonomous driving and surveillance, object detection networks are required to be used in more changeable scenarios. However, a multi-target domain adaptive object detection network is a challenging problem. Due to the lack of relevant datasets and the limitation of traditional domain adaptive methods, there is a lack of pertinent research in this field. Traditional single-target domain adaptive methods are often ineffective when used in multi-target domain adaptive problems. To allow the object detection network to be applied to multiple target domains, we propose the “domain transfer module” and the “multi-scale hybrid attention domain alignment module”. At the same time, we synthesized the “BlendedCityspce” dataset for training and testing of a multi-target domain adaptive target object network. The network provided in this paper has a good performance in the multi-target domain adaptation and reaches the state-of-the-art in the single-target domain adaptation. You can find our source code in https://github.com/Chonghuan-Liu/Multi-Target-Domain-Adaptative-Detector.
Sensitivity Analysis of Stroke Predictors Using Structural Equation Modeling and Bayesian Networks

This study applies and validates causal graphical models for the task of assessing the risk of a stroke given the stroke patient data. A probabilistic causal (Bayesian) network is designed to evaluate the risk factors identified by a stroke expert. Structural Equation Modeling is applied on empirical data to provide a quantitative assessment of causal relationships among the variables in the Bayesian Network, thus statistically validating the expert's knowledge. Several scenarios of risk assessment using the inference mechanism on the Bayesian network are demonstrated.
Research on a Hierarchical Storage Framework and Algorithm of High-Speed Railway Maintenance Big-Data Based on Multi Metric Features

Aiming at the problems of low storage and retrieval efficiency and high execution cost caused by the current unified storage method of high-speed railway maintenance big-data, a data hierarchical storage method based on multi metric features was proposed. Firstly, the data hierarchical storage requirements of high-speed railway maintenance data asset collection platform were analyzed. The data hierarchical storage architecture was proposed. Secondly, considering the business and attribute characteristics of high-speed railway maintenance big-data, a data value evaluation system was established in this paper. For the initially stored data, a data hierarchical storage algorithm was proposed. For the already stored data, a data migration algorithm based on generation countermeasure network was proposed. Finally, the existing data of the platform has been taken as the experimental sample for verification. The experimental results show that the accuracy rate of the data layering algorithm proposed in this paper is 88%, which realizes the efficient storage of data and reduces the storage cost.
Improving Class Balancing at Both Feature Extractor and Classifier Head

Training data are often imbalanced across classes in practice, and such class imbalance issue often causes model predictions biased toward majority classes during inference. Different from existing solutions which employ various training strategies to alleviate the class imbalance issue, this study proposes a novel two-head model architecture to help alleviate the issue. One auxiliary classifier head helps the feature extractor of the classifier more fairly learn to extract features for each class, and the main classifier head learns in a more class-balanced manner by dividing each majority class into multiple clusters in advance and considering each cluster as a new class. Extensive empirical evaluations on four class-imbalanced image datasets showed that the proposed approach achieves state-of-the-art classification performance.
ECL: Exclusive Curriculum Learning for Video Super-Resolution

Video super-resolution (VSR) problem has gained a soaring development along with deep learning methods. However, the further progress requires the blessing of more complex architectures. Unlike them, this paper promotes VSR performance from a new perspective of sample difficulty. We propose an exclusive curriculum learning strategy for VSR, which can improve the representation power without noticeable computation increment. Specifically, this paper memorizes the performance track of every sample and calculate a customized weight for each sample according to it. In this way, the model can automatically concentrate on the easy samples first and gradually focus on the hard ones. Experimental analysis on training process and benchmark datasets demonstrate that our method can substantially boost the performance with a superior convergence speed and a limited number of parameters.
Dual-Scale Alignment-Based Transformer on Linguistic Skeleton Tags for Non-Autoregressive Video Captioning

Due to the characteristic of one-time parallel generation of a caption, non-autoregressive video captioning lacks strong dependencies between words. Although using guideline of scene-related visual words can promote caption generation, the semantic relations among visual words are barely explored, limiting the accurate representation. To this end, we propose a Dual-Scale Alignment-based transformer on Linguistic Skeleton Tags (DSA-LST), which alleviates the defect above in the form of visual words group (several words representing a video frame). Different groups represent different semantic dependencies by attention. We utilize linguistic skeleton tags (i.e., several groups) as sentence-level supervision for visual words sequence. For visual words group to accurately express a specific frame, we further design dual scales of visual-language bi-direction alignment to achieve internal relevance of the tags. Extensive experiments conducted on widely used datasets: MSVD and MSR-VTT demonstrate the effectiveness of our method when compared with existing approaches.
TransCoop: Cooperation of Transformers and CNNs for Camouflaged Object Segmentation

Camouflaged object segmentation (COS) is a challenging task due to the existence of high intrinsic similarities between the object and background. To overcome this challenge, we pro-pose a new framework, called TransCoop, for COS through the cooperation of Transformers and convolutional neural net-works (CNNs). Specifically, Transformer is used to model global context and structural information for accurately positioning potential target objects. Meanwhile, a well-designed texture feature fusion module (TFFM) is used to fuse the features encoded by CNN with the shallow Transformer to fully mine the low-level features in the scene. Furthermore, we propose a noise removal module (NRM), which can eliminate the background noise of low-level features with the guidance of precise target location. Notably, our TFFM and NRM can effectively realize the interaction between Transformer and CNN features. Extensive experiments on four benchmark datasets demonstrate the superiority of our TransCoop against existing state-of-the-art methods.
Physics-Based Appearance and Illumination Estimation from a Single Face Image

Deep learning-based 3D face reconstruction methods have shown promising results in facial shape estimation. However, due to the intrinsic ill-posedness, few of them succeeded in recovering high-quality facial appearance and lighting conditions, which largely limits the re-rendering performance. In this paper, we propose a novel physics-based deep learning framework for human facial appearance and illumination estimation. A computation-friendly physics-based model is proposed to simulate complex real-world illumination effects, e.g., specularities and cast shadows. Combining with the proposed Normal Fusion Block (NFBlk) and online illumination augmentation scheme, our framework is capable of estimating dense illumination and full-posed appearance from a single face image. Experiments demonstrate that, even with limited amount of training data, the proposed framework can achieve promising results in different face manipulation tasks, such as face relighting, reenactment and swapping.
Coverage Control for PTZ Camera Networks Using Scene Potential Map

Pan-Tilt-Zoom (PTZ) camera networks are pervasive in many applications, such as video surveillance, sports analysis and epidemic prevention. However, it is difficult to control several PTZ cameras observing a large scene due to the high complexity of PTZ camera networks. This problem, called the coverage control for PTZ cameras, draws broad attention of researchers. Virtual potential field (VPF) can enhance the coverage performance and reduce the overlap region of cameras, but it does not fully take the actual scenario into account. In this work, we introduce scene potential map (SPM) to the VPF method, investigating both the scene and the task in the optimization. The proposed scene potential map characterizes the importance of target region and evaluates the perception quality of PTZ camera. Then we propose a novel virtual force analysis method to optimalize poses of the PTZ camera network. We also develop a region partition method based on the perception quality measure to divide the target region and achieve better zoom level, realizing an excellent coverage performance of PTZ camera networks. Finally, the evaluation experiments clearly demonstrated that our proposed coverage control scheme can achieve remarkably better performance than state-of-the-arts.