Reviewerly/exHarmony huggingface dataset:
A Cross-domain System Architecture Model of Dynamically Configurable Autonomous Vehicles

The development of a dynamically configurable autonomous vehicle is subject to a high degree of complexity. This complexity is further intensified by usually domain-specific thinking and document-based development, leading to difficulties in managing the development processes. Therefore, there is a need for manageable and cross-domain approaches in the development of vehicle systems. For this purpose, Model-based Systems Engineering (MBSE) proposes an approach for modeling systems architectures with the necessary views for the development of vehicles. This contribution presents accordingly a cross-domain system architecture model in the sense of MBSE using the example of a dynamically configurable autonomous vehicle (DCAV) and highlights the resulting advantages for the development process. A DCAV includes an electrically and autonomously driven platform and exchangeable add-on capsules for different use cases. The system architecture model provides here a development environment where requirements on the DCAV, its behavior, and structure as well as their interrelations are described, structured, and unified at several levels for different use cases (e.g. passenger transport). In this way, a comprehensive basis for various development activities (e.g. function-oriented modularisation) is established, which at the same time indicates the great potential of system architecture models for the development of future vehicles.
Boosting Fraud Detection in Mobile Payment with Prior Knowledge

With the prevalence of mobile e-commerce, fraudulent transactions conducted by robots are becoming increasingly common in mobile payments, which is severely undermining market fairness and resulting in financial losses. It has become a difficult problem for mobile applications to identify robotic automation accurately and efficiently from a massive number of transactions. The current research does not propose any effective method or engineering implementation. In this article, an extension to boost algorithms is presented that permits the incorporation of prior human knowledge as a means of compensating for a training data shortage and improving prediction results. Prior human knowledge is accumulated from historical fraud transactions or transferred from different domains in the form of expert rules and blacklists. The knowledge is applied to extract risk features from transaction data, risk features together with normal features are input into the boosting algorithm to perform training, and therefore we incorporate boosting algorithm with prior human knowledge to improve the performance of the model. For the first time we verified the effectiveness of the method via a widely deployed mobile APP with 150+ million users, and by taking experiments on a certain dataset, the extended boosting model shows an accuracy increase from 0.9825 to 0.9871 and a recall rate increase from 0.888 to 0.948. We also investigated feature differences between robots and normal users and we discovered the behavior patterns of robotic automation that include less spatial motion detected by device sensors (1/10 of normal user pattern), higher IP group-clustering ratio (60% in robots vs. 15% in normal users), higher jailbroken device rate (92.47% vs. 4.64%), more irregular device names and fewer IP address changes. The quantitative analysis result is helpful for APP developers and service providers to understand and prevent fraudulent transactions from robotic automation.This article proposed an optimized boosting model, which has better use in the field of robotic automation detection of mobile phones. By combining prior knowledge and feature importance analysis, the model is more robust when the actual dataset is unbalanced or with few-short samples. The model is also more explainable as feature analysis is available which can be used for generating disposal rules in the actual fake mobile user blocking systems.
Aligned variational autoencoder for matching danmaku and video storylines

We study a task of aligning time-sync video comments (danmaku) to narrative video storylines, which is helpful for finding semantic segmentation of videos and conducting fine-grained user feedback analyses. Due to the mismatch of text styles and the shift of topics, it is hard to apply previous semantic matching models directly for the alignment. To tackle the problem, we propose to utilize variational auto-encoders to map both user comments and storylines into latent spaces. By posing a matching loss on their latent codes, we reduce their mismatches in the latent space and make the alignment easier to learn. To handle constraints in the alignment, we also apply dynamic programming for finding global optimal outputs. According to experiments on a TV series dataset (consisting of about 10 K pairs of storylines and danmaku sent by users), the proposed model can achieve competitive performances.
Developing a robot with computer vision for automating business processes of the industrial complex

Abstract Robotic Process Automation technology is one of the solutions implemented in mechanical engineering and industrial production. A business process is automated by observing how a person is doing a task in a graphical user interface. Then the cyberphysical device repeats the same task. A significant problem of the implementation of such automated systems is that if the work is to be carried out through the interface of a virtual desktop, then it is impossible to obtain data on the elements of the graphical interface without computer vision. The object of the research is the automation of business processes of the industrial complex using computer vision. The purpose of the study is to apply computer vision methods to automate business processes and develop an appropriate software robot. The paper analyzes computer vision methods for recognizing user interface elements and text within these elements. Moreover, the authors have developed and tested a cross-platform application that reproduces a business process using computer vision. The test results have shown that selected computer vision methods will help automate industrial production business processes on any platform with a virtual desktop interface.
A rule based expert system based on ontology for diagnosis of ITP disease

This paper is aimed to implement the semantic rule based expert system for diagnosing a kind of blood immune thrombocytopenia disease. This paper presents an ontology to depict the knowledge domain of this disease, symptoms and its related treatments. The developed system uses the real data from laboratory of Iranian hospitals. In the system, the observed symptoms are taken from the patient via Java based graphical user interface. This system supports the patients suffering from this disease to get a type of their disease and recommends suitable treatments. Some semantic rules were defined and then, Jess as a reasoner inferenced the rules to do the diagnosis process. The diagnosis process is validated by blood specialists. Since this system can be used by patients, doctors or medical students anywhere, it helps to make the disease follow up easier and tries to save cost and time for patients. The questionnaire is developed to measure the usability of the system. The results of questionnaire were satisfactory after it was tested with 154 respondents. The reliability test was done and the Cronbach's Alpha was .865 which is higher than 0.7. The mean value of questionnaire is more than 4 and the total mean is 4.49 which is an acceptable value to show the high degree of user acceptance and accuracy of system.
Watermarking E-Government Document Images Using the Discrete Wavelets Transform and Schur Decomposition

This paper describes and efficient digital watermarking algorithm that will provide copyright protection to e-government document images. The proposed algorithm is based on utilizing two frequency domain transforms: the discrete wavelet transformation (DWT) and the Schur transform. The unique characteristics of e-government document images are taken into consideration in order to embed the watermark in a robust and imperceptible manner. The performance of the proposed DWT-Schur based algorithm is evaluated with respect to robustness. The results show that the proposed algorithm produces robustness against Gaussian noise attacks.
Multi-modal image synthesis combining content-style adaptive normalization and attentive normalization

We propose a novel unsupervised image translation model following an end-to-end manner,which incorporates Content-Style Adaptive Normalization(CSAN) and Attentive Normalization(AN). First of all, a new attentive normalization is applied for the first time in the style transfer task, which is an improvement and supplement to the traditional instance normalization, it helps to guide the model to pay more attention to the key areas in image translation, while ignoring the secondary areas. Secondly, our proposed CSAN function absorbs not only information of style codes, but also that of content codes. Compared with Adaptive Instance Normalization(AdaIN), CSAN is more favorable to retain content information of input images. In addition, CSAN can help the attention mechanism to flexibly control the amount of change in texture and shape of input images. Finally, a series of comparative experiments and qualitative and quantitative evaluations on the challenging datasets prove that the proposed model is superior and more advanced than State-Of-The-Art(SOTA) in terms of visual quality, diversity,semantic integrity, and style reflection of generated images.
Data Storage in Blockchain Based Architectures for Internet of Things (IoT)

Data storage in IoT systems describe how data is pushed from the sensor and where it will be stored. In Bitcoin, there is a limit to the number of transactions that can be stored in the block, similarly in Ethereum it is limited by the gas limit in block. An IoT infrastructure have several applications that includes a sensor collecting data from physical environment. Storing a large amount data in a blockchain faces several challenges due to the distributed nature of the blockchain, high transaction processing time, and reduced scalability. Hence it is crucial to identify what data should be stored and how to store it in a secure way. In this paper, we provide how data is being stored in various blockchain based IoT applications and provides data storage compliance in treating IoT data in a blockchain environment.
Sightseeing Navigation System from Normal Times to Disaster Outbreak Times within Urban Tourist Areas in Japan

In tourist areas, it is necessary to prepare a method that supports tourists’ activities by providing information concerning disaster support facilities during normal times, in addition to sightseeing spots and tourism-related facilities, because there is a risk of confusion during disasters, as tourists are not aware of the locations of disaster support facilities. The present study aims to develop a navigation system that supports the activities of users during both normal times and disasters by integrating augmented reality (AR) and web geographic information systems (Web-GISs), as well as by using pictograms. The system can not only effectively provide users with information concerning sightseeing spots and tourism-related facilities but also information concerning disaster support facilities. The system was operated over a period of 6 weeks in Chofu City, Tokyo Metropolis, Japan. Based on the results of a questionnaire survey for 60 users, the system was highly evaluated for its originality in terms of displays and functions using pictograms, navigation using AR, and obtaining information during disasters. Additionally, based on the results of access log analysis, the system was continuously utilized by users during the operation period. Therefore, by continuously operating the system, it can be expected that users will further utilize each function of the system.
Anonymous Fair Auction on Blockchain

Online auction systems allow to achieve an optimal trading of digital goods and services in a competitive market. Existing blockchain-based auctions allow the bidders to take part in the process while maintaining the confidentiality of their bids in a decentralized, transparent, secure and auditable way. However, in a competitive market, parties would prefer not to disclose their interests to competitors, and to remain anonymous during auctions. In this paper, we firstly analyze the specific requirements for blockchain-based anonymous fair auctions. We propose a formal model tailored to study auction systems facilitating anonymity, and we put forward a generic protocol relying on existing cryptographic primitives, such as designated verifier ring signature, to achieve bid confidentiality and bidder anonymity. Finally, we prove it to be secure against the security model we defined.
CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment

Maritime images captured under haze environment often have a terrible visual effect, making it easy to overlook important information. To avoid the failure of vessel detection caused by fog, it is necessary to preprocess the collected hazy images for recovering vital information. In this paper, a novel CNN-enabled visibility dehazing framework is proposed, consisting of two subnetworks, that is, Coarse Feature Extraction Module (C-FEM) and Fine Feature Fusion Module (F-FFM). Specifically, C-FEM is a multiscale haze feature extraction network, which can learn information from three scales. Correspondingly, F-FFM is an improved encoder-decoder network to fuse multiscale information obtained by C-FEM and enhance the visual effect of the final output. Meanwhile, a hybrid loss function is designed for monitoring the multiscale output of C-FEM and the final result of F-FFM simultaneously. It is worth mentioning that massive maritime images are considered the training dataset to further adapt the vessel detection task under haze environment. Comprehensive experiments on synthetic and realistic images have verified the superior effectiveness and robustness of our CNN-enabled visibility dehazing framework compared to several state-of-the-art methods. Our method preprocesses images before vessel detection to demonstrate our framework has the capacity of promoting maritime video surveillance.
Two-Stage Textual Knowledge Distillation for End-to-End Spoken Language Understanding

End-to-end approaches open a new way for more accurate and efficient spoken language understanding (SLU) systems by alleviating the drawbacks of traditional pipeline systems. Previous works exploit textual information for an SLU model via pre-training with automatic speech recognition or finetuning with knowledge distillation. To utilize textual information more effectively, this work proposes a two-stage textual knowledge distillation method that matches utterancelevel representations and predicted logits of two modalities during pre-training and fine-tuning, sequentially. We use vqwav2vec BERT as a speech encoder because it captures general and rich features. Furthermore, we improve the performance, especially in a low-resource scenario, with data augmentation methods by randomly masking spans of discrete audio tokens and contextualized hidden representations. Consequently, we push the state-of-the-art on the Fluent Speech Commands, achieving 99.7% test accuracy in the full dataset setting and 99.5% in the 10% subset setting. Throughout the ablation studies, we empirically verify that all used methods are crucial to the final performance, providing the best practice for spoken language understanding. Code is available at https://github.com/clovaai/textual-kd-slu.
A Multiple Sieve Approach Based on Artificial Intelligent Techniques and Correlation Power Analysis

Side-channel analysis achieves key recovery by analyzing physical signals generated during the operation of cryptographic devices. Power consumption is one kind of these signals and can be regarded as a multimedia form. In recent years, many artificial intelligence technologies have been combined with classical side-channel analysis methods to improve the efficiency and accuracy. A simple genetic algorithm was employed in Correlation Power Analysis (CPA) when apply to cryptographic algorithms implemented in parallel. However, premature convergence caused failure in recovering the whole key, especially when plenty of large S-boxes were employed in the target primitive, such as in the case of AES. In this article, we investigate the reason of premature convergence and propose a Multiple Sieve Method (MS-CPA), which overcomes this problem and reduces the number of traces required in correlation power analysis. Our method can be adjusted to combine with key enumeration algorithms and further improves the efficiency. Simulation experimental results depict that our method reduces the required number of traces by <?TeX $63.7\%$?> and <?TeX $30.77\%$?> , compared to classic CPA and the Simple-Genetic-Algorithm-based CPA (SGA-CPA), respectively, when the success rate is fixed to <?TeX $90\%$?> . Real experiments performed on SAKURA-G confirm that the number of traces required for recovering the correct key in our method is almost equal to the minimum number that makes the correlation coefficients of correct keys stand out from the wrong ones and is much less than the numbers of traces required in CPA and SGA-CPA. When combining with key enumeration algorithms, our method has better performance. For the traces number being 200 (noise standard deviation <?TeX $\sigma = 3.0$?> ), the attacks success rate of our method is <?TeX $85\%$?> , which is much higher than the classic CPA with key enumeration ( <?TeX $10\%$?> success rate). Moreover, we adjust our method to work on that DPA contest v1 dataset and achieve a better result (40.04 traces) than the winning proposal (42.42 traces).
Counter rotating terms and dipole–dipole interaction effects on the entanglement and population inversion of two qubits interacting with a two-mode field

Recalling that counter rotating terms (CRTs) in atom–field interaction are not always ignorable, particularly in the strong coupling regimes of atom–field systems, in this paper we are going to study the interaction between two qubits (two-level atoms) with a two-mode field in a high quality cavity by considering CRTs while the dipole–dipole (D–D) interaction is also taken into account. Indeed, by performing an appropriate dynamical Hamiltonian associated with the Jaynes–Cummings and anti-Jaynes–Cummings terms, as well as the D–D interaction for the system, we solve the corresponding time-dependent Schrödinger equation with specific initial conditions for atoms and field. In the continuation, concurrence, as an appropriate measure of the degree of entanglement of the two qubits, and atomic population inversion of the qubits, as the rate of energy exchange between atoms and field, are evaluated. Meanwhile, we investigate the influence of CRTs and D–D interaction (as well as other involved parameters in the model interaction) on the mentioned quantities. Our numerical results indicate that collapse-revival phenomenon can be observed in the absence of D–D interaction with or without rotating wave approximation, however, CRTs cause an increase or decrease in the number of collapse-revivals in a finite interval of time, depending on the chosen parameters. In fact, by entering the D–D interaction term, this phenomenon is destroyed and the time behaviour of the quantities experiences irregular oscillations. Noticing that the initial field is considered in the vacuum state, the occurrence of collapse-revival in the population inversion of the two qubits is an interesting feature of this work. As is shown, the degree of entanglement and atomic population inversion of the qubits can generally be tuned, and the death of entanglement can be prevented, both by adjusting the evolved parameters in the model.
Graph Signal Compression via Task-Based Quantization

Graph signals arise in various applications, ranging from sensor networks to social media data. The high-dimensional nature of these signals implies that they often need to be compressed in order to be stored and conveyed. The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation. In this work we study the joint design of graph signal sampling along with the quantization of these samples, for graph signal compression. We focus on bandlimited graph signals, and show that the compression problem can be represented as a task-based quantization setup, in which the task is to recover the spectrum of the signal. Based on this equivalence, we propose a joint design of the sampling and recovery mechanisms for a fixed quantization mapping, and present an iterative algorithm for dividing the available bit budget among the discretized samples. Our numerical evaluations demonstrate that the proposed scheme achieves reconstruction accuracy within a small gap of that achievable with infinite resolution quantizers, while compressing high-dimensional graph signals into finite bit streams.
More than just a walk through: Connect library users to resources with new 360 tools

New embedding and hyperlinking features of 360 tours have the potential to significantly impact how users access information and library services. This article examines current uses, benefits, and challenges of 360 tours for academic libraries. Librarians at the University of Louisiana at Lafayette's Edith Garland Dupré Library are creating tours that serve as a point of access for library services, information, and instruction. Based on their own experiences and rooted in practices from early virtual online library tour development, the authors identify best practices for 360 tour creation and areas of opportunity for future development and testing.
Learning to Sort Handwritten Text Lines in Reading Order through Estimated Binary Order Relations

Recent advances in Handwritten Text Recognition and Document Layout Analysis make it possible to extract information from digitized documents and make them accessible beyond the archive shelves. But the reading order of the elements in those documents still is an open problem that has to be solved in order to provide that information with the correct structure. Most of the studies on the reading order task are rule-base approaches that focus on printed documents, while less attention has been paid to handwritten text documents. In this work we propose a new approach to automatically determine the reading order of text lines in handwritten text documents. The task is approached as a sorting problem where the order-relation operator is learned directly from examples. We demonstrate the effectiveness of our method on three different datasets.
Towards a Reference Architecture for Interoperable Clouds

Cloud computing is growing very fast in the market and has become one of the most controversial discussed developments in recent years. Cloud computing providers become very numerous in these areas and each of them prefers its own cloud computing infrastructure, due to the incompatibility of standards and cloud access formats, which prevents them from accepting to support cloud computing applications in a standardized manner, this heterogeneity creates the problem of interoperability between clouds, and considering that Cloud customers are probably in search of an interoperable Cloud Computing, where they will have total control over their applications and simply migrate their services as needed, without additional development investment. A Cloud federation strategy should be considered. In our article, we propose a common architecture for a cloud that's based on existing architectures and the also use of best practices from ICT frameworks, such as IBM, ITIL, NIST, etc., to address interoperability of architectures issues in a multi-Cloud system.