Reviewerly/exHarmony huggingface dataset:
A bound on the minimum distance of generalized quasi-twisted codes

Generalized quasi-twisted (GQT) codes form a generalization of quasi-twisted (QT) codes and generalized quasi-cyclic (GQC) codes. By the Chinese remainder theorem, the GQT codes can be decomposed into a direct sum of some linear codes over Galois extension fields, which leads to the trace representation of the GQT codes. Using this trace representation, we first prove the minimum distance bound for GQT codes with two constituents. Then we generalize the result to GQT codes with s constituents. Finally, we present some examples to show that the bound is better than the well-known Esmaeili-Yari bound and sharp in many instances.
Enhancing E-Commerce through Blockchain (DLTs): The Regulatory Paradox for Digital Governance

Abstract Nowadays, the emergence of Distributed Ledger Technologies (DLTs) and the rapidly developing of the digital economy have the potential to transform the current international regulatory framework. Thus, the current developments of DLTs in e-commerce demand a closer analysis of the implications for global governance and international trade. While the Internet has enhanced the development of new platforms for international trade, DLTs may be instruments to unleash the potential of e-commerce. In this scenario, DLTs could drive, strength and promote e-commerce transactions by diminishing inefficiencies. For instance, it could significantly reduce intermediation costs, facilitating the use and interoperability of smart contracts and single window systems. Indeed, customs operations are very complex and involve different types of intermediaries across borders, creating unnecessary obstacles to trade that could be reduced by the application of DLTs. There is where the regulatory paradox enters into place. The operability of DLTs in e-commerce raises significant regulatory challenges posing new policy questions. For this reason, it is crucial to achieving common understandings among relevant stakeholders to identify a set of principles that guide a country’s regulatory frameworks to facilitate the interoperability of e-commerce. One of the main pieces of the puzzle of proposing a set of principles is how to deal with an appropriate level of intervention from regulators in order to promote trust in international transactions, transparency, efficiency, security and operability among regulatory frameworks. This paper will examine current developments, initiatives, and proposals from different approaches: It will start with an overview of the opportunities that DLTs will create to promote e-commerce. Then, it will explore the current international economic legal framework in light of the application of DLTs in e-commerce, taking into consideration policy recommendations from international institutions and initiatives in some specific jurisdictions. From those views, a set of principles will be identified.
Usability of Software–Intensive Systems from Developers’ Point of View

Even though the type of software-intensive systems has transitioned from the mainframe to the client-server and from the client-server to the cloud, usability is one of the significant quality characteristics throughout the transitions. To achieve and improve usability, it is important to ensure a common understanding of usability concepts and to utilize proven usability evaluation technologies. Quality characteristics and subcharacteristics of software-intensive systems, including usability related ones, are defined in the quality models, i.e. product quality model and quality in use model, of ISO/IEC 25010:2011 in ISO/IEC 250nn SQuaRE (Systems and Software Quality Requirements and Evaluation) series. The SQuaRE series also defines quality measures for quality characteristics and subcharacteristics to be used for quality evaluation. The quality models of software-intensive systems are now under revision considering evolved systems and software technologies and influence on various stakeholders, including “Public”. This paper describes the international standardization of usability definition and evaluation from the developers’ point of view. First, the overview of the SQuaRE is introduced. Second, concepts and technologies related to usability in the SQuaRE are explained. Third, the study results of the application of the SQuaRE to actual projects are introduced, focusing on usability. Finally, future perspectives of international standardization of usability of software-intensive systems are described.
Lateral scanning white-light interferometry on rotating objects

White-light interferometry (WLI) has been widely established as a contact-free measurement method for surface topographies. While the widespread vertical scanning approach provides a height resolution of under 1 nm, it is not suitable for measurements on objects in motion. An alternative for moving surfaces is laterally scanning white-light interferometry (LSWLI). Though LSWLI allows to measure objects in motion, the measurement on rotating, curved objects is still challenging, since the tilt angle between the optical axis of the WLI and the surface normal varies over the field of view, and the height evaluation strongly depends on the tilt angle that needs to be calibrated. For this reason, an enhancement of the signal evaluation is proposed that enables LSWLI measurements on rotating objects, cylinders in particular, without a preceding calibration of the tilt angle distribution in the field of view. This was achieved by enhancing a self-calibration algorithm originally developed for linearly scanned, plane surfaces with spatially resolved time-frequency analysis. Using this self-calibrating algorithm, the local tangent surface angles present in the field of view of the LSWLI are retrieved and used to calculate the micro topography of a cylindrical specimen. These topography data contain no information on the global shape of the object, therefore shape-removal algorithms, as needed for VSWLI measurements, are not necessary. As a byproduct of the angle evaluation, the radius of the cylindrical specimen can be determined in addition. The comparison with VSWLI reference data finally shows that the LSWLI setup is able to produce similar topography results despite the much inferior hardware.
Research on Cultural Tourism Experience Design Based on Augmented Reality

Following the continuous development of the internet and the widespread use of mobile devices, tourism products and services have experienced three different stages: 1.0 stage of group tours, 2.0 stage of independent travel, and 3.0 stage of in-depth tours. In addition to in-depth experience tours to meet the independent travel needs of tourists, it also needs to meet the needs of tourists for more in-depth understanding of the culture of attractions. In order to better integrate modern technology with China’s cultural heritage, the use of augmented reality (AR) to enhance a tourism experience is studied here. Firstly, the characteristics of AR technology are analysed, and its advantages of application in the design of cultural tourism activities is discussed. This includes advantages such as extended dimensions of perception, enhanced cognitive depths and aesthetic experiences. This is also extended to summarise the mechanism that makes up a tourist’s experience, and a framework of user experience design for AR tourism application is identified. Specifically, a tourism experience is designed from the sensory layer to the cognitive, the interactive, and finally the emotive layer, and the corresponding design strategies are given. Lastly, feasibility is verified by the design scheme.
An Energy-Efficient VM migrations optimization in Cloud Data Centers

Nowadays more attention focuses on VM management strategies in a variety of scenarios. The problem of virtual machine placement and migration is an optimization problem aiming for multiple goals. The efficiency of a data center, therefore, depends a lot on how virtual machines are provisioned and where they are located. An efficient VM allocation policy will improve energy efficiency while limiting the degradation of the quality of service (QoS) and alleviate hotspots., but will also reduce the operating costs of the data center. Migrating VMs into a fewer number of Physical Machines (PMs) can maximize the utilization of Cloud servers and reduce the energy consumption of the Cloud data center. In this paper, we study the problem of optimal VM allocation policy and migration to minimize power consumption in a data center while preserving QoS. CloudSim simulator is used to create a cloud environment. It provides the interface to deal with the physical and virtual machines. We evaluate and compare our algorithms corresponding to different approaches in order to find the one that optimizes VM placement and migration. The simulation result shows that virtual machine placement and migration techniques minimize the energy consumption, migrations and total simulation time. Nowadays more attention focuses on VM management strategies in a variety of scenarios. The problem of virtual machine placement and migration is an optimization problem aiming for multiple goals. The efficiency of a data center, therefore, depends a lot on how virtual machines are provisioned and where they are located. An efficient VM allocation policy will improve energy efficiency while limiting the degradation of the quality of service (QoS) and alleviate hotspots., but will also reduce the operating costs of the data center. Migrating VMs into a fewer number of Physical Machines (PMs) can maximize the utilization of Cloud servers and reduce the energy consumption of the Cloud data center. In this paper, we study the problem of optimal VM allocation policy and migration to minimize power consumption in a data center while preserving QoS. CloudSim simulator is used to create a cloud environment. It provides the interface to deal with the physical and virtual machines. We evaluate and compare our algorithms corresponding to different approaches in order to find the one that optimizes VM placement and migration. The simulation result shows that virtual machine placement and migration techniques minimize the energy consumption, migrations and total simulation time.
A novel method of constructing high-dimensional digital chaotic systems on finite-state automata*

When chaotic systems are implemented on finite precision machines, it will lead to the problem of dynamical degradation. Aiming at this problem, most previous related works have been proposed to improve the dynamical degradation of low-dimensional chaotic maps. This paper presents a novel method to construct high-dimensional digital chaotic systems in the domain of finite computing precision. The model is proposed by coupling a high-dimensional digital system with a continuous chaotic system. A rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney’s definition of chaos. Numerical experimental results for different high-dimensional digital systems indicate that the proposed method can overcome the degradation problem and construct high-dimensional digital chaos with complicated dynamical properties. Based on the construction method, a kind of pseudorandom number generator (PRNG) is also proposed as an application.
Common path polarization phase-shifting interference microscope for surface profilometry

The development of a laser-based quantitative phase microscope with phase-shifting capability is reported. The proposed arrangement utilizes a suitably converging laser beam to illuminate the sample, a long working distance microscope objective, a wire grid polarizer to generate collinearly propagating and orthogonally polarized sample and reference beams, and polarizing devices for polarization phase shifting. Reorientation of a polarizer will revert the microscope back to a standard bright-field microscope that can be used for viewing the object under test.
Machine Learning Aided Blockchain Assisted Framework for Wireless Networks

Inspired by its success in financial sectors, the blockchain technique is emerging as an enabling technology for secure distributed control and management of wireless networks. In order to fully benefit from this distributed ledger technology, its limitations, cost, complexity and empowerment also have to be critically appraised. Depending on the specific context of the problem to be solved, these limitations have been handled to some extent through a clear dichotomy in the blockchain architectures, namely by conceiving both permissioned and permissionless blockchains. Permissionless blockchain requires massive computing power to achieve consensus, while its permissioned counterpart is energy efficient but would require trusted participants. To combine these benefits by gaining trust at a high energy efficiency, a novel mechanism is proposed for automatically learning the trust level of users in a public blockchain network and granting them access to a private blockchain network. In this context, machine learning is a very powerful tool capable of automatically learning the trust level. We have proposed reinforcement learning for bridging the dichotomy of blockchains in terms of striking a trust vs complexity trade-off in an unknown environment. Benefits and limitations of various forms of blockchain techniques are analyzed, followed by their reinforcement-aided evolution. We demonstrate that the proposed reinforcement learning aided blockchain is capable of supporting high-integrity autonomous operation and decision making in wireless networks. The win-win amalgamation of these techniques has been demonstrated for striking a compelling balance between the benefits of permissioned and permissionless blockchain networks through the case-study of the proposed blockchain based unmanned aerial vehicle aided wireless networks.
Research on the Relationship between Physical Exercise, Psychological Flexibility and Positive Emotion of College Students Based on Computer Mathematical Model

Abstract In the 1970s and 1980s, the study of mental resilience emerged in the field of psychology in the United States and quickly became a hot topic in the field of international psychology. The field studies the psychological adaptation and development of children, adolescents (and later adults) in adversity. The extensive research on the psychological resilience of college students can enlighten the complexity of their psychological development. Throughout life, people inevitably encounter setbacks and adversities, and are under pressure from all sides at all times. With the development of society and the intensification of competition, college students are facing more and more pressure. Some college students can adapt to pressure and grow up healthily, while others cannot adapt to adversity, which leads to psychological problems. In the era of computer big data, relevant experience shows that physical exercise has certain help in forming mental resilience and positive emotions. Therefore, this paper analyzes the relationship between physical exercise, mental resilience and positive emotions of college students by using computer technology and mathematical model, so as to provide help for college students to form healthy psychology.
Deep dilated and densely connected parallel convolutional groups for compression artifacts reduction

A widely used JPEG lossy compression standard introduces different artifacts, particularly, blocking, ringing and blurring effects in the compressed image. Existing algorithms focus on deblocking and discard the ringing, blurring artifacts that produce a visually unpleasant images. These artifacts must be eliminated to restore visually pleasing output while decompressing the image. In this paper, we propose a novel 10-layer network with parallel convolutional groups based on deep dilated convolutions and dense connections (D3C Net) to reduce blocking, ringing and blurring artifacts arise due to JPEG compression. The dilated convolution helps to expand receptive field exponentially without increasing the complexity of the network, thereby relieving the network from the training burden. Moreover parallel convolutional layers are integrated into network for efficient feature representation and model parallelism. In addition, the skip connections allow the information to be back-propagated efficiently to bottom layers and tackles the problem of vanishing gradients. Furthermore, the gradient explosion problem is tackled by an end-to-end mapping between the JPEG decompressed image and the corresponding residual image. Different from the existing algorithms for compression artifacts removal which usually train for a specific compression factor, our D3C Net is able to handle compression artifacts with unknown compression factors (i.e., blind JPEG compression artifacts removal). The subjective and objective performance analysis with metrics PSNR, SSIM and PSNR-B demonstrates that the proposed network produces significant performance improvement when compared to state-of-the-art methods.
Learning Robust Scene Classification Model with Data Augmentation Based on Xception

Abstract Scene classification technology based on computer vision has been widely applied in many fields. However, with the increasing complexity of images, many computer vision classification models are difficult to meet requirements of current scene classification tasks, as they not only require considering the object, background, spatial layout and other information, but also many relationships in the image. Based on the analysis of existing scene classification algorithms and Xception model, an approach that adds optimization from two aspects of data set processing is proposed to solve complicated scene classification tasks. Combined with the image enhancement technology, the serialized image enhancement method is used to expand the dataset and enhance the image features, and takes advantage of the Xception model to extract the image features to obtain the scene classification model with high robustness. The experimental results showed that Xception model was able to deal with scene classification efficiently by making up for the shortcomings of traditional Convolutional Neural Networks (CNN) models in feature extraction and generalization ability.
Improved photometric acceptance testing in image feature extraction tasks

Image correspondence is established by “matching” the feature descriptors of the interest points in the target image to that of the reference image. By acceptance testing, we refer to a postmatching hypothesis test used to screen out potential false matches—conventionally using descriptor test statistics. We propose a new acceptance testing strategy that does not rely on the descriptor test statistics exclusively. The contribution we bring is to demonstrate that, unlike feature matching, acceptance testing may incorporate additional photometric values of the scene to improve the recall rate. We show experimentally that the acceptance testing strategy that incorporates image feature detection statistics we refer to as detector response-ratio thresholding that are usually excluded from the feature descriptor vectors has a superior recall–precision performance compared to the state-of-the-art feature extraction techniques.
A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective

Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results, and identify future trends to encourage researchers to advance their current work.
Human Interaction Anticipation by Combining Deep Features and Transformed Optical Flow Components

The anticipation of ongoing human interactions is not only highly dynamic and challenging problem but extremely crucial in applications such as remote monitoring, video surveillance, human-robot interaction, anti-terrorists and anti-crime securities. In this work, we address the problem of anticipating the interactions between people monitored by single as well as multiple camera views. To this end, we propose a novel approach that integrates Deep Features with novel hand-crafted features, namely Transformed Optical Flow Components (TOFCs). In order to validate the performance of the proposed approach, we have tested the proposed approach in real outdoor environments, captured using single as well as multiple cameras, having shadow and illumination variations as well as cluttered backgrounds. The results of the proposed approach are also compared with the state-of-the-art approaches. The experimental results show that the proposed approach is promising to anticipate real human interactions.
Recent Advances in Saliency Estimation for Omnidirectional Images, Image Groups, and Video Sequences

We present a review of methods for automatic estimation of visual saliency: the perceptual property that makes specific elements in a scene stand out and grab the attention of the viewer. We focus on domains that are especially recent and relevant, as they make saliency estimation particularly useful and/or effective: omnidirectional images, image groups for co-saliency, and video sequences. For each domain, we perform a selection of recent methods, we highlight their commonalities and differences, and describe their unique approaches. We also report and analyze the datasets involved in the development of such methods, in order to reveal additional peculiarities of each domain, such as the representation used for the ground truth saliency information (scanpaths, saliency maps, or salient object regions). We define domain-specific evaluation measures, and provide quantitative comparisons on the basis of common datasets and evaluation criteria, highlighting the different impact of existing approaches on each domain. We conclude by synthesizing the emerging directions for research in the specialized literature, which include novel representations for omnidirectional images, inter- and intra- image saliency decomposition for co-saliency, and saliency shift for video saliency estimation.
Application Research Based on Machine Learning in Network Privacy Security

As the hottest frontier technology in the field of artificial intelligence, machine learning is subverting various industries step by step. In the future, it will penetrate all aspects of our lives and become an indispensable technology around us. Among them, network security is an area where machine learning can show off its strengths. Among many network security problems, privacy protection is a more difficult problem, so it needs more introduction of new technologies, new methods and new ideas such as machine learning to help solve some problems. The research contents for this include four parts: an overview of machine learning, the significance of machine learning in network security, the application process of machine learning in network security research, and the application of machine learning in privacy protection. It focuses on the issues related to privacy protection and proposes to combine the most advanced matching algorithm in deep learning methods with information theory data protection technology, so as to introduce it into biometric authentication. While ensuring that the loss of matching accuracy is minimal, a high-standard privacy protection algorithm is concluded, which enables businesses, government entities, and end users to more widely accept privacy protection technology.