Reviewerly/exHarmony huggingface dataset:
Privacy-enhanced OptiSwap

Fair Exchange is a fundamental problem in the exchange of digital items with direct application to electronic commerce. In a fair exchange protocol, two parties want to exchange their corresponding items such that either both receive the other's item, or neither of them receives anything. It has been shown that fair exchange without a trusted third party (TTP) is not possible. Optimistic fair exchange protocols limit the role of TTP to the case that one of the parties misbehaves. OptiSwap (Eckey et al., 2020) is a fair exchange protocol for the exchange of confidential digital items with digital coins. OptiSwap uses a smart contract as the TTP and allows the buyer to use an interactive dispute resolution protocol with the seller (mediated through smart contract) to generate a proof of misbehaviour for a misbehaving seller. We show that OptiSwap's dispute resolution protocol leaks information about the item to the smart contract (public) which can completely reveal the item to the public, and this provides an opportunity for a malicious buyer to pose a credible threat to the fairness guarantee of the system. We propose and design privacy-enhanced OptiSwap that prevents the leakage of information and guarantees security and fairness of the exchange without significantly affecting the efficiency of the protocol. We prove security of the new protocol in an extension of the universal composability for non-monolithic adversaries, and implement and evaluate its efficiency against the original OptiSwap. We discuss our results and suggest directions for future research.
Asynja: Sensorial Design for Running Motivation

If starting to run is an easy decision, committing to a long-term running routine proves to be a more challenging endeavor for many people. In this pictorial, we unravel the design process of Asynja, an artefact that triggers exercise imagery by using natural scents related to running. Relying on peripheral interaction, this research probe subtly nudges users to go running, thereby supporting them to trans- form their positive intentions into actions. Exploring sensoriality as a design opportunity for behavior change interventions, we invite the community to expand the design space of exercise-related motivational products and systems.
Hybridx Higher Education – A Multidimensional Overlay of Hybrid Forms of Learning and Teaching

The diversification of study programs serves to impart knowledge and skills in a way that is appropriate to the target group and to individualize learning paths in a student-centered manner. Traditional hybrid forms of learning such as the combination of face-to-face and distance learning (blended learning) are extended by new hybrid approaches. However, these often appear rather by chance than on a sound basis due to the diversity of opportunities and digital tools available today. Hybrid forms of university teaching are increasingly combined in different contexts and degrees of digitization. The resulting increase of higher education teaching complexity can only be managed through structured, transparent, and evidence-based approaches when designing learning and teaching models. The suggested Hybridx university teaching reference model describes the combination of several teaching and learning dimensions. It builds a foundation for systematically restructuring university teaching using innovative, digital approaches. Next to the conceptualization of the novel Hybridx reference model, the paper provides case study examples and recommendations for using hybrid forms of learning and teaching.
Introduction to Set Shaping Theory

Abstract: In this article, we define the Set Shaping Theory whose goal is the study of the bijection functions that transform a set of strings into a set of equal size made up of strings of greater length. The functions that meet this condition are many but since the goal of this theory is the transmission of data, we have analyzed the function that minimizes the average information content. The results obtained show how this type of function can be useful in data compression.
Image Steganalysis Based on HPF Neural Network

Existing image steganalysis methods cannot fully extract image residual information, resulting in poor performance of image steganalysis methods. We propose an image steganalysis method based on HPF neural network, which includes two novel modules. One module is used to learn the residual information of the image, and the other module is used to improve the nonlinear expression ability of the model. We conducted experiments on two commonly used image steganalysis datasets, BOSSbase V1.01 and ALASKA-V2. Results show that the proposed neural network model is better than the SRNet model.
Mosan: Combining Circularity and Participatory Design to Address Sanitation in Low-Income Communities

Abstract The health and environmental impact of unsafe sanitation affects more than half of the world’s population. The lack of access to safe sanitation affects disproportionately rural populations in low- and middle-income countries, where progress is also shown to be slower. The sheer scale of the problem, combined with the variability of climate, geographies, and socioeconomic conditions, requires a variety of adaptable, scalable, centralized, and decentralized solutions working cohesively. This paper presents the case of Mosan, an off-grid, market-based sanitation solution, in order to display how such sanitation approaches can contribute to bridge this gap by addressing the communities most at risk. Mosan is a decentralized, circular sanitation solution encompassing the whole sanitation chain from containment, collection, transport, transformation, and reuse. Focused on community-scale systems, Mosan is applying participatory design principles and co-creation to enable community engagement, raise awareness, trigger creativity, and support local innovation.
Rotation Invariant Deep Learning Approach for Image Inpainting

Matrix completion technique aims to fill the missing information of a bidimensional structure, supporting applications such as recommender systems, image restoration, and image inpainting. In particular, most of literature methods for image inpainting take advantage of intrinsic high spatial correlations by incorporating low-rank regularizers into an optimization problem. However, the presence of geometric distortions such as the rotation of the objects in the image or of the patterns of the missing information can affect the quality in the completion task. To overcome such limitation, this work presents a non-local geometric similarity regularizer, invariant to the rotations in the spatial domain, the similarities presented in some sub-regions of the image with the same structure but rotated at some angle. The rotation invariant regularizer is integrated into a convolutional neural network and a generative adversarial neural network, improving the completion quality, specially for images with high rotation distortions.
Research on Teaching Quality Evaluation System of Public Security Colleges Based on Integrated Neural Network

In order to solve the problems in the process of teaching quality evaluation and improve the accuracy of teaching quality evaluation, a teaching quality evaluation system based on integrated neural network is proposed. Firstly, the system collects a large number of historical data of teaching quality in Public Security Colleges, and preprocesses the data to construct learning samples of teaching quality evaluation in Public Security Colleges; Then, RBF neural network, BP neural network and echo state network are used to evaluate the teaching quality of Public Security Colleges, and the evaluation results are obtained; Finally, the RBF neural network, BP neural network and echo state network are weighted to get the final result of teaching quality evaluation. On the same platform, compared with other university teaching quality evaluation systems, the results show that the accuracy of the proposed system is more than 95%, while the accuracy of the comparison system is less than 95%, which indicates that the integrated neural network is more reliable and can meet the requirements of modern teaching management.
Research on the Application of Sparse Group Lasso-Support Vector Machine Method in Financial Report Fraud Identification

How to effectively identify the fraud in financial reports has been a hot issue for investors and capital market regulators. On the basis of analyzing the deficiency of the existing methods, this paper introduces the Sparse Group Lasso-Support Vector machine method into the field of financial report fraud Identification. According to the characteristics of this method, taking the data of listed companies in China from 2008 to 2018 as a sample, and according to the main factors affecting the fraud risk of financial reports, this paper puts forward 15 financial indicators and non-financial indicators to construct an explanatory variable set, then, this paper makes an empirical analysis by using this method. The results show that the method has higher recognition rate and interpretability than other methods, such as Logistic regression, lasso-Support vector machine and Group lasso-Support vector machine, when the explanatory variables have natural grouping structure and higher group correlation level, it has strong theoretical and practical value.
Towards the Next Generation of the Linkedgeodata Project Using Virtual Knowledge Graphs

With the advancement of Semantic Technologies, large geospatial data sources have been increasingly published as Linked data on the Web. The LinkedGeoData project is one of the most prominent such projects to create a large knowledge graph from OpenStreetMap (OSM) with global coverage and interlinking of other data sources. In this paper, we report on the ongoing effort of exposing the relational database in LinkedGeoData as a SPARQL endpoint using Virtual Knowledge Graph (VKG) technology. Specifically, we present two realizations of VKGs, using the two systems Sparqlify and Ontop. In order to improve compliance with the OGC GeoSPARQL standard, we have implemented GeoSPARQL support in Ontop v4. Moreover, we have evaluated the VKG-powered LinkedGeoData in the test areas of Italy and Germany. Our experiments demonstrate that such system supports complex GeoSPARQL queries, which confirms that query answering in the VKG approach is efficient.
Rapid Assessment of the Verticality of Structural Objects with a Circular Base

Structural health monitoring represents an important task that involves various measurements and methods. Due to its importance, it uses the best available equipment and the most advanced algorithms to give precise and reliable information about an object's health. For tall objects such as chimneys, towers, and masts the main criterion of stability is verticality deviation (inclination). However, sometimes it is necessary to get a rapid assessment of an object’s stability and in that case, accuracy and reliability are sacrificed to save time. In this paper, an approach for rapid assessment of verticality of structural objects with a circular base is presented. Geodetic methods were used to measure points in two levels on the perimeter of the object. Three algebraic methods for circle fitting were used to determine the center coordinates and radius of the object in the two observed cross-sections. The final step was to determine the spatial vector which represents the object’s axis and compare it to the theoretical vertical. The proposed approach is fast, simple, and inexpensive in terms of equipment, human resources, and software necessary for data processing but at the same time, it is less accurate and reliable compared to the state of the art approaches for object verticality assessment.
Comparative analysis of metrics for estimating the introduced distortions in images when injection digital watermarks in the frequency spectrum

The informatization of society and scientific and technological progress contribute to the continuous increase and rapid dissemination of video content on the global network. When distributing unique video information, the task of copyright protection arises, as well as the task of unambiguous identification of the source of the content. Therefore, the preservation of protected, stable and visually imperceptible signs with information about the sources of playback in the structure of the video stream is an urgent task. One way to introduce digital watermarks is to transform in the frequency domain of the image, inserting digital watermarks into the Fourier spectrum. Any changes in the frequency spectrum will inevitably lead to image distortion and the appearance of artifacts. In the process of research in the field of digital watermark implementation, there is a problem of assessing the degree of distortion introduced into the image.
An Authentication Method Combining Blockchain and Subject-Sensitive Hashing for the Data Sharing of Remote Sensing Image

Data sharing of remote sensing images promotes the development of remote sensing research and applications in various industries, but the sharing of remote sensing images has security issues such as piracy, leakage, and theft. The emergence of the blockchain has made it possible to solve the above problems. However, the hash function adopted by the blockchain can only verify the changes in the binary level of remote sensing images, and cannot reflect the changes of the content of remote sensing images. Aiming at the security problems in remote sensing image sharing, this paper combines blockchain and subject-sensitive hashing to build an authentication method for remote sensing images. In our method, subject-sensitive hash algorithm is used to calculate the hash value of remote sensing images, instead of using cryptographic hash function. Experiments show that our method is not only robust to operations that do not change the content of remote sensing images, but also can detect content tampering of the images.
Indicators Recommendation Algorithm based on Information Entropy and FP Growth for Power Report System

Abstract For the Indicators recommendation problem of a power report system without user grade but a large amount of browsing records data, a novel algorithm of mining association rules based on information entropy and Fp-growth is proposed. Firstly, though normalizing the browsing frequency, the information entropy is used to determine the weights of items so as to calculate the interesting measurement value for users. Besides, the forgetting function is introduced to reflect the interest transfer. Then, the FP-growth algorithm is improved by dynamically adjusting support and activity. Finally, the browsing records data of the power report system were used as the test dataset for experiments. The results showed that the algorithm can effectively recommend interest items for users, and the recommendation performance is significantly improved compared with other algorithms.
FBNet: Feature Balance Network for Urban-Scene Segmentation

Image segmentation in the urban scene has recently attracted much attention due to its success in autonomous driving sys-tems. However, the poor performance of concerned fore-ground targets, e.g., traffic lights and poles, still limits its further practical applications. In urban scenes, foreground targets are always concealed in their surrounding stuff be-cause of the special camera position and 3D perspective pro-jection. What's worse, it exacerbates the imbalance between foreground and background classes in high-level features due to the continuous expansion of the receptive field. We call it Feature Camouflage. In this paper, we present a novel add-on module, named Feature Balance Network (FBNet), to eliminate the feature camouflage in urban-scene segmen-tation. FBNet consists of two key components, i.e., Block-wise BCE(BwBCE) and Dual Feature Modulator(DFM). Bw-BCE serves as an auxiliary loss to ensure uniform gradients for foreground classes and their surroundings during back-propagation. At the same time, DFM intends to enhance the deep representation of foreground classes in high-level features adaptively under the supervision of BwBCE. These two modules facilitate each other as a whole to ease feature cam-ouflage effectively. Our proposed method achieves a new state-of-the-art segmentation performance on two challenging urban-scene benchmarks, i.e., Cityscapes and BDD100K. Code will be released for reproduction.
Data-driven Operational Effectiveness Evaluation of Space-based Information System

Aiming at the complexity of space-based information system (SIS) effectiveness evaluation and the coupling and interaction of various combat capabilities, a data-driven operational effectiveness evaluation model is proposed. By introducing the PSO-BPNN to fit the performance evaluation I/O data, design the mapping relationship between the neural network input layer, hidden layer, output layer and hierarchical index system. On this basis, the affiliation relationship among the indicators at each level and the association relationship between the indicators are excavated, and the SIS effectiveness evaluation model is constructed. Taking the maritime integrated joint operations supported by space-based information as an example, the effectiveness data is obtained by using MATLAB, VENSIM and STK co-simulation. By quantitatively analyzing the coupling interaction between indexes, the operational effectiveness of SIS was evaluated, and the accuracy of the model and the feasibility of the evaluation method were verified.
The Development of an Online Teacher Training Model for Reading Literacy and Numeracy Improvement

This study aimed to generate an online teacher training model that can be used to improve teachers' reading literacy and numeracy. The method used to develop an online Teacher Training Model for Reading Literacy and Numeracy Improvement was ADDIE, that consisted of five stages, including analysis, design, development, implementation and evaluation. Quantitative data were gathered in the form of expert and practitioner validation scores. Qualitative data included expert and practitioner critiques, recommendations, and feedback. The model was applied online via the zoom application. The findings indicate that the activities included in the teacher training model can improve teachers' reading literacy and numeracy. These activities include fostering positive attitudes and perceptions of the classroom atmosphere and training assignments, identifying the characteristics of reading literacy and numeracy through problem analysis, recognizing, comparing, criticizing, and reflecting on reading literacy and numeracy questions, compiling reading literacy and numeracy questions, performing diagnostic tests to recognize reading literacy and numeracy, preparing lesson plans, and peer teaching. The research findings suggest that the online teacher training model developed for improving teachers' reading literacy and numeracy is valid and applicable.
Agile Methodology use factors that influence project performance in South African Banking sector-A case study

Lately, different software communities have been adopting and using agile methodologies widely. Regardless of the extensive benefits pointed out by many authors and the advantages agile brings to organisations, there are many other critical issues it also possesses. Most studies have reported many benefits which includes, but not limited to improved communication within the team and high quality of the software that agile brings to organisations. Some studies in contradiction have cross-questioned the benefits claimed, while others differed strikingly with the arguments that the benefits claimed have insufficient scientific proof. This study investigates how the agile method use factors influence project performance in the South African Banking Sector. The research model for evaluating how the agile methodology use factors influence project performance was proposed. The survey was conducted through a questionnaire. The data was quantitatively analysed using Statistical Package for Social Sciences. Reliability and validity were tested using Cronbach's alpha and KMO respectively. This study has indicated that that reduced upfront planning, iterative delivery, environmental feedback and technical feedback positively influence project performance.
N-Body Simulation Inspired by Metaheuristics Optimization

The N-body problem in classical physics, is the calculation of force of gravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists and mathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been the major hurdles in solving this problem for large and complex galaxies. Advent of high performance computational machines have mitigated the problem to much extent, but still for large number of bodies it consumes huge amount of resources and days for computation. Conventional algorithms have been able to reduce the computational complexity from to by splitting the space into a tree or mesh network, researchers are still looking for improvements. In this research work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. The results are compared with that of conventional algorithms. The outcomes show about 50% time saving with almost no loss in accuracy. The proposed approach being a metaheuristics optimization technique, attempts to find optimal solution to the problem, searching the entire space in a unique and efficient manner in a very limited amount of time.
Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search

Different from other deep scalable architecture-based neural architecture search (NAS) approaches, broad NAS (BNAS) proposes a broad scalable architecture which consists of convolution and enhancement blocks, dubbed broad convolutional neural network (BCNN), as the search space for amazing efficiency improvement. BCNN reuses the topologies of cells in the convolution block so that BNAS can employ few cells for efficient search. Moreover, multiscale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology. However, BNAS suffers some drawbacks: 1) insufficient representation diversity for feature fusion and enhancement and 2) time consumption of knowledge embedding design by human experts. This article proposes Stacked BNAS, whose search space is a developed broad scalable architecture named Stacked BCNN, with better performance than BNAS. On the one hand, Stacked BCNN treats mini BCNN as a basic block to preserve comprehensive representation and deliver powerful feature extraction ability. For multiscale feature enhancement, each mini BCNN feeds the outputs of deep and broad cells to the enhancement cell. For multiscale feature fusion, each mini BCNN feeds the outputs of deep, broad and enhancement cells to the output node. On the other hand, knowledge embedding search (KES) is proposed to learn appropriate knowledge embeddings in a differentiable way. Moreover, the basic unit of KES is an over-parameterized knowledge embedding module that consists of all possible candidate knowledge embeddings. Experimental results show that: 1) Stacked BNAS obtains better performance than BNAS-v2 on both CIFAR-10 and ImageNet; 2) the proposed KES algorithm contributes to reducing the parameters of the learned architecture with satisfactory performance; and 3) Stacked BNAS delivers a state-of-the-art efficiency of 0.02 GPU days.