Reviewerly/exHarmony huggingface dataset:
Single Scene Image Editing Based on Deep Intrinsic Decomposition

Intrinsic decomposition is an inherent problem in computer graphics and computer vision, which decomposes an image into a reflectance image and a shading image. Through the processing of reflectance and shading, it can achieve the image scene editing effect consistent with the vision of the real scene, which endows application potentials to augmented reality. In this paper, we propose a convolutional neural network structure that includes shared and independent encoder to the independent decoder as well as several different loss functions for training based on the assumption of intrinsic decomposition and differences in datasets. To address the shortcomings of the existing synthetic dataset, we reconstruct the new synthetic data and train our network on the synthetic and real datasets in sequence. We quantitatively and qualitatively evaluate our intrinsic decomposition results on the IIW dataset, and the result shows that they outperform those of existing methods. We also perform image editing based on our deep intrinsic decomposition on images of real different scenes and obtain satisfactory visual results.
Domain Adaptation for Person Re-Identification with Part Alignment and Progressive Pseudo-Labeling

With the recent technological advances, surveillance cameras became accessible to the general public and a huge amount of nonstructured data is being gathered. However, extracting value from this data is challenging, especially for tasks that involve human images, such as face recognition and person re-identification. Annotation of this kind of data is a challenging and expensive task. In this work, we propose a domain adaptation workflow to allow CNNs that were trained in one domain to be applied to another domain without the need for annotated target data. Our method uses AlignedReID++ as the baseline, trained using a Triplet loss with batch hard. Domain adaptation is done in an unsupervised manner by clustering unlabeled data to generate pseudo-labels in the target domain. Our results show that domain adaptation really improves the performance of the CNN when applied in the target domain.
Classifying high-frequency FX rate movements with technical indicators and inception model

Recent advances in the adoption of AI to forecast stock price movements highlight (i) new ways to encode financial data and technical indicators conveniently; (ii) a state-of-the-art architecture based on inception networks; and, (iii) the existence of across-asset universal features that can be leveraged to improve performances. We combine these three observations and investigate the extent to which this new pipeline can guarantee good predictive power in FX markets. Ultimately, we wonder if these approaches continue to work in a quote-driven market, wherein the AI cannot rely on the (micro) structure of the limit order book.
Filtering Algorithm of Out-of-Sequence Measurement Based on Sequential Filtering

In multi-sensor tracking and fusion system, due to the different pre-processing time and sampling rate of sensors, as well as the inherent random communication delay of the channel, the measurement data of sensor may arrive at the fusion center in an unordered manner. The current out-of-order measurement filtering methods cannot fully meet the engineering requirements in terms of tracking accuracy and computational complexity. This paper proposes a centralized data fusion method based on lag compensation. This method can deal with one-step or even multi-step delay measurement data, and the compensation accuracy can be closed to the fusion accuracy under orderly measurement. At the same time, this method has the ability of selective fusion, which improves the positioning accuracy while relatively reducing the amount of calculation and storage. The simulation results verify the effectiveness of the algorithm.
A study of individual identification of radiation source based on feature extraction and deep learning

Abstract Emitter identification technology can distinguish the types of radiation sources and identify the identity of emitter. It has broad application prospects in both military and civilian fields. The article mainly reviews the radiation source feature extraction methods for individual identification in recent years, and discusses the advantages and disadvantages of manually extracted features and the feature extraction based on deep learning. The technical difficulties of radiation source feature extraction methods are summarized with respect to the environment, the number of radiation sources, and the performance of algorithms, etc. Finally, the article points out the possible future development directions of individual radiation source identification.
Balanced Non-adjacent Forms

Integers can be decomposed in multiple ways. The choice of a recoding technique is generally dictated by performance considerations. The usual metric for optimizing the decomposition is the Hamming weight. In this work, we consider a different metric and propose new modified forms (i.e., integer representations using signed digits) that satisfy minimality requirements under the new metric. Specifically, we introduce what we call balanced non-adjacent forms and prove that they feature a minimal Euclidean weight. We also present efficient algorithms to produce these new minimal forms. We analyze their asymptotic and exact distributions. We extend the definition to modular integers and show similar optimality results. The balanced non-adjacent forms find natural applications in fully homomorphic encryption as they optimally reduce the noise variance in LWE-type ciphertexts.
Smart Campus Ibn Tofail Approaches and Implementation

This paper describes the concept and studies of “smart campus” using different methodologies and introduces a global strategy and use case for each environment in the University Park. Our main goal is to define, present the general smart campus principles and objectives which revolve around the different IOT and cellular infrastructures in the university, introducing a general ICT architecture for their coordination and also detailing their direct use in the management of the university and applications for learning and research, reduce the cost and take one step forward a university smart campus.
3-absorbing primary ideals

The notions of 3-absorbing primary ideals of ternary Γ-SO semiring were introduced in the following manuscript and obtain corresponding conditions and some characteristics of 3-absorbing primary ideals in ternary Γ-SO semirings.
Research on Traffic Data Resource Sharing Model Based on Blockchain

research-article Research on Traffic Data Resource Sharing Model Based on Blockchain Share on Authors: Zichen Zhang Beijing University of Technology, China Beijing University of Technology, ChinaView Profile , Yong Shao Beijing University of Technology, China Beijing University of Technology, ChinaView Profile , Yuliang Shi Beijing University of Technology, China Beijing University of Technology, ChinaView Profile , Changshun Yan Beijing University of Technology, China Beijing University of Technology, ChinaView Profile Authors Info & Claims DSIT 2021: 2021 4th International Conference on Data Science and Information TechnologyJuly 2021 Pages 103–108https://doi.org/10.1145/3478905.3478927Online:28 September 2021Publication History 0citation17DownloadsMetricsTotal Citations0Total Downloads17Last 12 Months17Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
SUBSTANTIATION OF THE CONTENT OF THE PROGRAM OF PSYCHOPROPHYLAXIS OF THE EMOTIONAL BURNING SYNDROME THE SERVICE OFFICERS OF THE ENGINEERING TROOPS OF THE ARMED FORCES OF UKRAINE

The content of the author's program of psychoprophylaxis of emotional burnout syndrome in servicemen of engineering troops of the Armed Forces of Ukraine is substantiated in the article. In particular, it is noted that the huge amount of engineering work carried out in large areas, short deadlines prove that the successful solution of engineering problems of combat operations requires intensive communication and causes deep shortterm emotional experiences, which can provoke emotional burnout of individual servicemen. Therefore, the system of professional training of servicemen is expected to use a system of specific methods of training, which are expressed in the development of professionally significant personality traits, adaptation to stressful factors of professional activity, and non-specific, characterized by the use of means and methods of emotional self-regulation. Psychoprophylaxis of emotional burnout of a serviceman is realized through the development of his skills of self-regulation, personality-oriented professional education, as well as his professional readiness. The program of psychoprophylaxis of emotional burnout is a holistic, sufficiently dynamic system, open for constant recovery, based on the idea of the development of the serviceman first as an individual and then as a specialist, a professional with a deep understanding of socio-cultural and educational situations. Achieving this goal is possible by synthesizing the main components of the personality of engineering troops serviceman: worldview, spiritual, moral and professional.
Towards Use of OntoClean for Ontology Contextualization

Ontologies are formal systems of concepts used to describe numerous domains of interest. Ontologies are usually very expressive, but it comes at a price of computationally expensive reasoning over them. In our previous work we discussed the possible performance benefits that can be obtained by decomposing an ontology into contexts. While the benefits are appealing, we discovered that, in our case, the main obstacle against using contextual versions of ontologies was the necessity of performing the costly process of their decomposition with the participation of human experts. In this paper we discuss the possibility of using OntoClean method for streamlining and at least partial automation of suggesting a decomposition of an ontology into contexts. We present a hypothesis about how to build a structure of contexts, and verify this hypothesis against several ontologies used in state-of-the-art research. The ontologies have been obtained by us in the process that uses elements of Systematic Literature Review. The final assessment of the method has been performed by human experts, during interviews, and we present the details of their evaluation in the paper.
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields

Thermal images model the long-infrared range of the electromagnetic spectrum and provide meaningful information even when there is no visible illumination. Yet, unlike imagery that represents radiation from the visible continuum, infrared images are inherently low-resolution due to hardware constraints. The restoration of thermal images is critical for applications that involve safety, search and rescue, and military operations. In this paper, we introduce a system to efficiently reconstruct thermal images. Specifically, we explore how to effectively attend to contrasting receptive fields (RFs) where increasing the RFs of a network can be computationally expensive. For this purpose, we introduce a deep attention to varying receptive fields network (AVRFN). We supply a gated convolutional layer with higher-order information extracted from disparate RFs, whereby an RF is parameterized by a dilation rate. In this way, the dilation rate can be tuned to use fewer parameters thus increasing the efficacy of AVRFN. Our experimental results show an improvement over the state of the art when compared against competing thermal image super-resolution (SR) methods.
Learning Modular Programming using Realistic Scenarios: an Experience Report

Software Engineering is essentially an applied research field. Students will only understand some of the abstract concepts and principles taught in software engineering courses if they see the intricacies of the real world in practice. Industrial or close-to-real scenarios are increasingly being incorporated into classrooms. It tends to enable the students to appreciate software development as an engineering process in which aspects such as quality must be considered in decision-making. This paper presents an instructional proposal adopted in the Modular Programming course at Federal University of the State of Rio de Janeiro (UNIRIO). The proposed instructional design is based on Project-Based Learning theory, mixing group and individual tasks during the software development activities of a software project. It capitalizes on the microservice architectural pattern so that the students work in parallel developing different system modules. Also, to increase the perception of realism by the students, the instructional design employs modern software development tools commonly found in software organizations. Our preliminary results indicate students are more engaged in the course activities, and they feel more technically productive in their jobs. On the other hand, students pointed out some issues such as better accommodating task dependencies in the developed project and facilitating the configuration of software development tools. In conclusion, we believe that our proposal can be used in or adapted to other institutions interested in providing a more realistic experience to students in learning Modular Programming.
A Data Collection System Design for Hand Gestures

In this study, we aim at designing a smart glove, which consists of different inertial sensors and an EMG sensor and developing a human-machine interaction application by pre-processing and fusing these different sensory data. We also aim at providing solutions in cases where image processing-based approaches are inefficient. In the proposed smart glove, the quaternion-based orientation data to be produced by the magnetometer and gyroscope together, the acceleration data to be generated by the accelerometer, and the analog data generated by the EMG sensor are collected and then prepared for use by different applications.
Unsupervised Domain Adaption via Similarity-Based Prototypes for Cross-Modality Segmentation

Deep learning models have achieved great success on various vision challenges, but a well-trained model would face drastic performance degradation when applied to unseen data. Since the model is sensitive to domain shift, unsupervised domain adaption attempts to reduce the domain gap and avoid costly annotation of unseen domains. This paper proposes a novel framework for cross-modality segmentation via similarity-based prototypes. In specific, we learn class-wise prototypes within an embedding space, then introduce a similarity constraint to make these prototypes representative for each semantic class while separable from different classes. Moreover, we use dictionaries to store prototypes extracted from different images, which prevents the class-missing problem and enables the contrastive learning of prototypes, and further improves performance. Extensive experiments show that our method achieves better results than other state-of-the-art methods.
Smart Authentication based on Modified Merkle Puzzles

Most computer users practice password-based authentication as the first layer of security due to its easy implementation. Yet, the technology advancements have provided the attackers with enough resources to get the password by trying out diverse means. In addition, there is a burden of entering a password when the user wants to access the computer, which is comprehended as discomfort to the user. Given this discomfort, the users often leave the computer in an unlocked state while moving away. The computer in this situation is most vulnerable and makes it susceptible to lunchtime attacks.To address the aforementioned issues, we propose a method called smart authentication that requires minimal user interaction with the computer. Smart authentication encompasses the concepts of authentication, de-authentication, and re-authentication under the same hood. The proposed method uses continuous authentication so that the concepts called de-authentication and re-authentication can be implemented with an insignificant effort. Considering the constraints associated with password-based authentication, second factors of authentication have been introduced as an additional security feature. Due to the difficulty of carrying extra hardware components around all of the time, the proposed approach considers the smartphone device as the second factor. Hardware cost is another aspect that ascends scalability concerns to most of these approaches. The proposed approach aims to use existing hardware commonly found in modern desktop machines and thereby decrease the cost factor of scalability drastically.
Content based Image Retrieval using the Domain Knowledge Acquisition

It is widely known that, nowadays the whole world is dominated by the visual information and this keeps increasing day by day. These data should be organized in such a way that the image retrieval can be more effective as well as efficient, this can be carried out through CBIR method. This paper has proposed a novel methodology named as DKA (Domain Knowledge Acquisition), which provides the advanced image retrieval as it considers the manifold structure. Moreover, the other existing technique requires the complex segmentation region from background. This research work are carried as first the shape feature has been used and once it is done a SIFT feature is applied and later with the help of DKA(Domain Knowledge Acquisition) the features are redefined and finally it helps in capturing the manifold structure in a better way and expose the inherent relation among the objects. The main intention here is to develop the efficient CBIR technique. In order to evaluate the algorithm, the feature point analysis has been performed with the help of Corel-10k datasets and later the comparison is done with the default SIFT feature and the SIFT with DKA. The analysis shows that, SIFT with DKA provides more flexible and efficient technique.
Entanglement Based Quantum Networks: Protocols, AI control plane &amp; coexistence with classical communication

Quantum networks scale the advantages of quantum communication protocols to more than just two distant users. Here we present a fully connected quantum network architecture in which a single entangled photon source distributes quantum states to a multitude of users [1] and our work scaling the network to a complete end to end metropolitan quantum network with 8 users [2] . Our network architecture optimises the resources required by each user without sacrificing security or functionality. We established several long-distance loopback connections and demonstrated extended stable operation with high secure key rates. Unlike previous attempts at multi-user networks, which have been based on active components, and thus limited to some duty cycle, our implementation is fully passive. Further, we present our efforts towards coexistence of 100 Gbps classical communication and all quantum channels used in the network over the same deployed optical fibres.
Construction of Japanese Interactive Analysis System Based on Perception Model and Pattern Mining

Construction of the Japanese interactive analysis system based on perception model and pattern mining is studied in this paper. In the multi-segment support model, the minimum support that meets the required conditions is set for each individual item in the data item set. When looking for frequent sets, use the maximum or minimum value of the minimum support of each item in the related item set to mine. Based on this model, the pattern mining framework is defined. The counting matrix improves the discovery efficiency of the positive and negative frequent items. In the strong association rule discovery stage, and the interactive model is defined. The proposed model is simulated on the real scenarios and the experiment is conducted.
Generative Dataset Distillation

As the structure and function of deep learning models become more and more complex, huge amounts of data are often required for training to avoid over-fitting. Dataset distillation is a method for reducing dataset sizes by learning a small amount of synthetic samples which take almost the same training effect as a larger dataset, and the method behaves as transferring the information of the original dataset to a smaller one. This has several benefits like accelerating model training, saving energy, and reducing storage space. However, the distilled data of current methods of dataset distillation is highly dependent on the training model and looks very noisy. And no metric has been proposed yet to reveal the generalization quality of the distilled data. In this paper, we discuss the generalization of the distillation and propose an approach to distill with auxiliary generative neural networks to enhance the performance. For example, training the LeNet with 10 distilled images (one per class) and random initialization results in over 82% test accuracy on MNIST, and the distilled samples look much more informative with better generalization. Besides, we demonstrate an application in key feature identification of images with our method, where the features for distinction of two classes of data samples are distilled.
Nonlinearity of Many-Valued Logic Component Functions of Modern Cryptographic Algorithms S-boxes

Block symmetric cryptographic algorithms are the essential and inalienable element of any information security system. The main component of block symmetric cryptographic algorithms is the S-box, which largely determines the speed and cryptographic quality of the entire cryptographic transformation. The rapid development of cryptanalysis methods, including the development of quantum computers, requires a more careful research of all possible representations of nonlinear elements of block symmetric cryptographic algorithms. In view of the fact that in addition to the mathematical apparatus of Boolean functions used today to estimate the cryptographic quality of S-boxes, a cryptanalyst can apply the mathematical apparatus of functions of many-valued logic, which makes relevant the task of researching and comparing the cryptographic quality of S-boxes of modern cryptographic algorithms, represented by functions of many-valued logic. This paper presents a research and comparative analysis of nonlinear transformations of the cryptographic algorithms AES, Kalyna, BelT, and Kuznechik when represented by functions of many-valued logic. It was found that only the nonlinear transformation of the BelT cryptographic algorithm is characterized by growth when represented by many-valued logic functions, while the Kalyna cryptographic algorithm demonstrates the greatest decrease in nonlinearity when represented by 16-functions among the cryptographic algorithms researched. The results obtained in the paper indicate a significant unused reserve of cryptographic quality, which can be used in the design of new cryptographic algorithms and their structural elements.