Reviewerly/exHarmony huggingface dataset:
An Integrated Platform for Vehicle-Related Services and Records Management Using Blockchain Technology

This paper proposes an integrated system for vehicle-related services and records management using blockchain technology, where various stakeholders (vehicle owners, regional transport offices, insurance issuers, pollution control test centers, traffic police) can avail or provide various services (vehicle’s registration, driving license issuance, insurance and pollution-under-control certificates issuance, automated insurance claim, auditable trail of vehicle’s documents access and verification) in a hassle-free manner without any untrusted intermediaries. While the use of blockchain technology increases accountability, transparency, and trust in the system, this allows a cohesive integration of the proposed system with other smart city digital infrastructures easily. We develop a prototype of our proposal as a proof of concept using Hyperledger Fabric, adopting Attribute-Based Access Control (ABAC) policy, and we present an experimental evaluation to demonstrate the performance of the system. To the best of our knowledge, this proposal is the first of its kind to provide a common blockchain-based platform for all vehicle-related services and records management.
Anti-JPEG Compression Steganography Based on the High Tense Region Locating Method

Robust data hiding techniques attempt to construct covert communication in a lossy public channel. Nowadays, the existing robust JPEG steganographic algorithms cannot overcome the side-information missing situation. Thus, this paper proposes a new robust JPEG steganographic algorithm based on the high tense region location method which needs no side-information of lossy channel. First, a tense region locating method is proposed based on the Harris-Laplacian feature point. Then, robust cover object generating processes are described. Last, the advanced embedding cost function is proposed. A series of experiments are conducted on various JPEG image sets and the results show that the proposed steganographic algorithm can resist JPEG compression efficiently with acceptable performance against steganalysis statistical detection libraries GFR (Gabor Filters Rich model) and DCTR (Discrete Cosine Transform Residual).
Efficient Task Scheduling in Cloud Computing using Multi-objective Hybrid Ant Colony Optimization Algorithm for Energy Efficiency

The efficiency of Internet services is determined by the Cloud computing process.Various challenges in computing are being faced, such as security, the efficient allocation of resources, which in turn results in the waste of resources.Researchers have explored a number of approaches over the past decade to overcome these challenges.The main objective of this research is to explore the task scheduling of cloud computing using multi-objective hybrid Ant Colony Optimization (ACO) with Bacterial Foraging (ACOBF) behavior.ACOBF technique maximized resource utilization (Service Provider Profit) and also reduced Makespan and user wait times Job request.ACOBF classifies the user job request in three classes based on the sensitivity of the protocol associated with each request, Schedule Job request in each class based on job request deadline and create a Virtual Machine (VM) cluster to minimize energy consumption.Based on comprehensive experimentation, the simulated results show that the performance of ACOBF outperforms the benchmarked techniques in terms of convergence, diversity of solutions and stability.
Fast Certified Robust Training via Better Initialization and Shorter Warmup.

Recently, bound propagation based certified adversarial defense have been proposed for training neural networks with certifiable robustness guarantees. Despite state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, to reach SOTA performance they usually need a long warmup schedule with hundreds or thousands epochs and are thus still quite costly for training. In this paper, we discover that the weight initialization adopted by prior works, such as Xavier or orthogonal initialization, which was originally designed for standard network training, results in very loose certified bounds at initialization thus a longer warmup schedule must be used. We also find that IBP based training leads to a significant imbalance in ReLU activation states, which can hamper model performance. Based on our findings, we derive a new IBP initialization as well as principled regularizers during the warmup stage to stabilize certified bounds during initialization and warmup stage, which can significantly reduce the warmup schedule and improve the balance of ReLU activation states. Additionally, we find that batch normalization (BN) is a crucial architectural element to build best-performing networks for certified training, because it helps stabilize bound variance and balance ReLU activation states. With our proposed initialization, regularizers and architectural changes combined, we are able to obtain 65.03% verified error on CIFAR-10 ($\epsilon=\frac{8}{255}$) and 82.13% verified error on TinyImageNet ($\epsilon=\frac{1}{255}$) using very short training schedules (160 and 80 total epochs, respectively), outperforming literature SOTA trained with a few hundreds or thousands epochs.
The Use of Augmented Reality in Contemporary Art

The article analyzes the use of augmented reality (AR) in creative projects of contemporary art. Features of application of augmented reality technologies are investigated. The author reviews the possibilities and options for using augmented reality technology to support museums and galleries. This article will help contemporary artists to better understand these technologies, understand the role of AR in modern society and assess its prospects. Analysis of scientific research has shown that the use of augmented reality in art is increasingly of interest to artists and scientists. There are also articles dedicated to improving the learning experience of art gallery visitors using augmented reality. The present study reveals the chronology of research on this phenomenon. An analysis of the experience of Ukrainian, European and American artists and designers in the implementation of AR‑technologies in the field of culture has also been conducted, on the basis of which a system of thematic areas of augmented reality in contemporary art has been presented. There are five areas where augmented reality in the field of art is used. These are museums of history and art and exhibition halls, art city tourism, film industry, concerts and television. But it turns out that the number of these areas is not final. Eventually, AR will be used in other art venues. The materials of this article provide recommendations for the spread of augmented reality technologies in art. With the help of these technologies, the image of an innovative progressive museum and exhibition hall, open to new opportunities, can be created, which gives the opportunity to attract new visitors, generating a unique complex interactive experience. So far, augmented reality is used exclusively in art, but more and more artists and designers are beginning to move towards interactive implementations in their work. Many of these authorial experiments grow into unique creative projects and provide enormous advantages in the system of culture and art.
Evaluating the impact of curriculum learning on the training process for an intelligent agent in a video game

We want to measure the impact of the curriculum learning technique on a reinforcement training setup, several experiments were designed with different training curriculums adapted for the video game chosen as a case study. Then all were executed on a selected game simulation platform, using two reinforcement learning algorithms, and using the mean cumulative reward as a performance measure. Results suggest that curriculum learning has a significant impact on the training process, increasing training times in some cases, and decreasing them up to 40% percent in some other cases.
FPGA Implementation of a Chaos-based Stream Cipher and Evaluation of its Performances

Chaos based stream cipher (CSC) has caught the attention of various security applications, especially for military needs and protection in Internet of Things (IoT). In fact, computing and memory resources have been suited by chaos-based stream cipher for real-time communication. In this paper, we designed a chaos-based stream cipher using a robust pseudo chaotic number generator (RPCNG).The simulation of the proposed CSC is done in VHDL using the ISE Design Suite 14.6 tool of Xilinx with finite computing precision N = 32-bit and the hardware implementation is realized on the SAKURA-G FPGA board.The proposed system requires 6036 slices LUTs as hardware cost and achieves throughput of 301.184Mbps.It is robust against statistical attacks and thus can be used in all applications that require confidentiality.
On the Instability of Diminishing Return IR Measures

The diminishing return property of ERR (Expected Reciprocal Rank) is highly intuitive and attractive: its user model says, for example, that after the users have found a highly relevant document at rank r, few of them will continue to examine rank $$(r+1)$$ and beyond. Recently, another IR evaluation measure based on diminishing return called iRBU (intentwise Rank-Biased Utility) was proposed, and it was reported that nDCG (normalised Discounted Cumulative Gain) and iRBU align surprisingly well with users’ SERP (Search Engine Result Page) preferences. The present study conducts offline evaluations of diminishing return measures including ERR and iRBU along with other popular measures such as nDCG, using four test collections and the associated runs from recent TREC tracks and NTCIR tasks. Our results show that the diminishing return measures generally underperform other graded relevance measures in terms of system ranking consistency across two disjoint topic sets as well as discriminative power. The results generalise a previous finding on ERR regarding its limited discriminative power, showing that the diminishing return user model hurts the stability of evaluation measures regardless of the utility function part of the measure. Hence, while we do recommend iRBU along with nDCG for evaluating adhoc IR systems from multiple user-oriented angles, iRBU should be used under the awareness that it can be much less statistically stable than nDCG.
Reflecting in Space on Time: Augmented Reality Interactive Digital Narratives to Explore Complex Histories

This article discusses the state of AR, which is nearing its ubiquity moment, and is a breakthrough technology whose availability is taken for granted. On this backdrop, the author considers the problem of representing complex situations with AR and proposes the addition of interactive digital narratives as a solution, explaining the specific advantages of such an approach. In order to show the application of this approach, the article uses the example of a complex historical situation-that of the Free City of Danzig in the first half of the 20th century. The article applies this example in a multi-step design process during the pre-production phase in order to select a fitting type of AR Interactive Digital Narrative for a given project.
AdaptationExplore – A Process for Elicitation, Negotiation, and Documentation of Adaptive Requirements

[Context and motivation] Current and future systems have to operate in complex and dynamic environments. An adaptive system addresses these challenges as it monitors its environment and reacts by changing its behavior. [Question/Problem] Representations of adaptive requirements (e.g., at runtime) and strategies for decision-making have gained a lot of interest in past and current research. Yet, there is a lack of support for elicitation of requirements and environmental information for adaptive systems. [Principal ideas/results] We suggest to apply creativity techniques to elicit adaptation requirements and make use of situations to negotiate them (a situation represents the state of the system and its environment at a particular instance of time). [Contributions] In this paper, we introduce AdaptationExplore, a process for the development of adaptive systems, which supports engineers in particular during the early phases. The results of a pilot study are reported. 37 Master students applied the process on different cases. The study provides first positive experiences on the effectiveness and applicability of the process.
False Financial Statement Identification Based on Fuzzy C-Means Algorithm

Financial accountants falsify financial statements by means of financial techniques such as financial practices and financial standards, and when compared with conventional financial data, it is found that the falsified financial data often lack correlation or even contradict each other in terms of financial data indicators. At the same time, there are also inherent differences in reporting patterns from conventional financial data, but these differences are difficult to test manually. In this paper, the fuzzy C-means (FCM) clustering method is used to amplify these differences and thus identify false financial statements. In the proposed algorithm, firstly, the normalization constraint of the FCM clustering algorithm on the sum of affiliation of individual samples is relaxed to the constraint on the sum of affiliation of all samples, thus reducing the sensitivity of the algorithm to noise and isolated points; secondly, a new affiliation correction method is proposed to address the problem that the difference in affiliation is too large after the relaxation of the constraint. In the discussion of this paper, most of the information comes from the annual reports of companies, administrative penalty decisions of the Securities Regulatory Commission, and some information comes from research reports made by securities institutions, which are limited sources of information. The proposed method can correct the affiliation to a reasonable range, effectively avoiding the problem that some samples have too much affiliation and become a class of their own and also avoiding the problem that it is difficult to choose the termination threshold of the algorithm iteration due to too little affiliation, and can ensure that the constraint on the sum of affiliation of all samples is always satisfied during the iteration of the algorithm. The method has the characteristics of high recognition accuracy and has the significance of theoretical method innovation.
Introduction to GeomInt

Abstract The use of the subsurface as a source of resources, a storage space and for installing underground municipal or traffic infrastructure has become much more intensive and diverse in recent years. In addition to classical anthropogenic interventions such as mining, oil and gas production or tunnel construction, other forms of underground use have come into the focus of economic, political and scientific research, particularly in connection with the transformation of energy systems.
A Comprehensive and Reproducible Comparison of Cryptographic Primitives Execution on Android Devices

With technology evolving rapidly and proliferating, it is imperative to pay attention to mobile devices’ security being currently responsible for various sensitive data processing. This phase is essential as an intermediate before the cloud or distributed ledger storage delivery and should be considered additional care due to its inevitability. This paper analyzes the security mechanisms applied for internal use in the Android OS and the communication between the Android OS and the remote server. Presented work aims to examine these mechanisms and evaluate which cryptographic methods and procedures are most advantageous in terms of energy efficiency derived from execution time. Nonetheless, the dataset with the measurements collected from 17 mobile devices and the code for reproducibility is also provided. After analyzing the collected data, specific cryptographic algorithms are recommended to implement an application that utilizes native cryptographic operations on modern Android devices. In particular, selected algorithms for symmetric encryption are AES256 / GCM / No Padding; for digital signature – SHA512 with RSA2048 / PSS, and for asymmetric encryption – RSA3072 / OAEP with SHA512 and MGF1 Padding.
Single Image Dehazing Using Deep Learning

Many real-world situations such as bad weather may result in hazy environments. Images captured in these hazy conditions will have low image quality due to microparticles in the air. The microparticles light to scatter and absorb, resulting in hazy images with various effects. In recent years, image dehazing has been researched in depth to handle images captured in these conditions. Various methods were developed, from traditional methods to deep learning methods. Traditional methods focus more on the use of statistical prior. These statistical prior have weaknesses in certain conditions. This paper proposes a novel architecture based on PDR-Net by using a pyramid dilated convolution and pre-processing modules, processing modules, post-processing modules, and attention applications. The proposed network is trained to minimize L1 loss and perceptual loss with the O-Haze dataset. To evaluate our architecture's result, we used structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and color difference as an objective assessment and psychovisual experiment as a subjective assessment. Our architecture obtained better results than the previous method using the O-Haze dataset with an SSIM of 0.798, a PSNR of 25.39, but not better on the color difference. The SSIM and PSNR results were strengthened by using subjective assessments and 65 respondents, most of whom chose the results of the restoration of the image produced by our architecture.
Modern Digital Art Practices and “More-than-Human” Perception

The article analyzes the concept of “more-than-human” perception, the features of which are constructed in the networks of relations, as a result of the interaction and relationships of heterogeneous forces (human activities, animals, bacteria, objects, technologies, etc.). This is not a subjective human perception, personal judgment of individual taste or social “distribution of sensitive”, but the collaborative process of configuring affective “field of the possible things” (define perception) as a result of the participation of multiple actants in the creation of life events, situations, processes, and conflicts. Based on the philosophical ideas of A. Bergson, W. Whitehead, J. Simondon, J. Deleuze, and F. Guattari, the author examines the affective nature of the interaction between the works of contemporary artists and the audience-participants. It is argued that creativity and artistic practice can be reinterpreted as processes of co-creation with the movements of matter formation. It is a way to think of art not as a form, but as a process open to a continuous interval of renewal and invention, which is revealed through the material relations of matter-energy, duration, transitions, and intuition. Through affective attunement techniques, participants organize the movements of matter-en- ergy flows, and each individual perception by the subject-actant becomes a joint “more-than- human” perception. Interactive and participatory works do not reflect reality in aesthetic forms, but instead create new processes, new places of creativity (manifestations of chance), in which the aesthetic is performatively realized before it is understood and reflected by the participants themselves. The text clarifies what constitutes “more-than-human” perception, how it relates to the usual understanding of the sphere of human sensory experience, and how it is implemented when working with modern interactive and participatory art projects.
Few Shot Dialogue State Tracking using Meta-learning

Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information etc.With the increasing need to deploy such systems in new domains, solving the problem of zero/fewshot DST has become necessary.There has been a rising trend for learning to transfer knowledge from resource-rich domains to unknown domains with minimal need for additional data.In this work, we explore the merits of meta-learning algorithms for this transfer and hence, propose a meta-learner D-REPTILE specific to the DST problem.With extensive experimentation, we provide clear evidence of benefits over conventional approaches across different domains, methods, base models and datasets with significant (5-25%) improvement over the baseline in lowdata setting.Our proposed meta-learner is agnostic of the underlying model and hence any existing state-of-the-art DST system can improve its performance on unknown domains using our training strategy.
Teaching Practice and Exploration of Cloud Class Combined With Mind Map Design in Biochemistry Course in Colleges and Universities of Traditional Chinese Medicine in the Internet Era

Abstract Biochemistry is the core course of life science related majors, which expounds the structure, properties, metabolism and function of biological macromolecules. Mastering these basic knowledge is of great significance to students’ follow-up learning and development. Based on the investigation method, pithy formula method and lesson study method, this paper explores how to use cloud class and mind map to carry out the teaching of traditional Chinese medicine biochemistry, stimulate students’ interest in autonomous learning, help students preview, self-study and review, complete the real-time interactive feedback between teachers and students in the mobile environment, so as to better promote the teaching reform of biochemistry.
A two-level strategy based on evolutionary algorithm to solve the inter-domain path computation under node-defined domain uniqueness constraint

The Inter-Domain Path Computation problem under Node-defined Domain Uniqueness constraint (IDPC-NDU) is a recently investigated topic for finding the effective routing paths on the multi-domain network topology as well as transportation. The objective of the IDPC-NDU is to find the shortest path in the multi-domain directed graph that traverses every domain at most once. Since the IDPC-NDU belongs to NP-Hard class, this paper proposes a novel two-level approach based on an Evolutionary Algorithm (EA) to solve it. The first level aims to determine the sequence of crossed domains using an improved Genetic Algorithm (GA), while the second one aims to locate the minimally costly path between two nodes among the entire domains. Furthermore, we devise an approach to represent a chromosome, which reduces the chromosome length to the number of domains. Experiments on numerous sets of instances were implemented to show the effectiveness and characteristics of the proposed algorithm.