Reviewerly/exHarmony huggingface dataset:
Hybrid Body Craft: Toward Culturally and Socially Inclusive Design for On-Skin Interfaces

Sensor device miniaturization and breakthroughs in novel materials have enabled technology to progress directly onto the skin surface. However, unlike all other media, the human body is a complex and meaning-laden surface that encompasses a wearer's individual, social, and political identities. Yet, research in on-skin interfaces has focused on engineering aspects, with a scant focus on the cultural and social dimensions of device design. Hybrid Body Craft presents a design approach for bridging the cultural aspects of body crafts, which are existing cultural, historical, and fashion-driven practices and rituals associated with body decoration, with emerging on-skin interfaces. Here, we present a series of more socially and culturally inclusive on-skin interface designs that incorporate various emerging materials and technologies into body craft customs. A design space examines the impact of technology on increasing the agency of self-expression and communication, along with design implications for increased democratization and inclusiveness in design for body surface interfaces to enable the presentation of unique identities.
Quality Measurement of Screen Images via Foreground Perception and Background Suppression

The widespread screen sharing poses a great challenge in the visual quality control of industrial applications, as there is a significant difference between the visual quality perception of natural content (NC) and screen content (SC) images (or videos) during <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">acquisition</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">compression</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distribution</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">display</i> . Apart from the foreground distortion, artifacts appeared in the smooth background of SC images also play a key role in its perceived quality. In this article, a robust approach is designed for quality measurement of SC images, which embodies both foreground perception and background suppression (FPBS) in a satisfying way. Specifically, the extraction of foreground perceptual features is investigated by considering both the local contrast variation and the local edge response features, while for the first time, a suppression strategy is devised to eliminate the influences of artifacts appeared in the simple and homogeneous background. Experimental results show that, based on only two simple features, the proposed scheme achieves the state-of-the-art results on two latest benchmark databases. We believe that the feature engineering method can further improve the performance of the FPBS framework on the quality measurement of screen images.
Propagation Enhanced Neural Message Passing for Graph Representation Learning

Graph Neural Network (GNN) is capable of applying deep neural networks to graph domains. Recently, Message Passing Neural Networks (MPNNs) have been proposed to generalize several existing graph neural networks into a unified framework. For graph representation learning, MPNNs first generate discriminative node representations using the message passing function and then read from the node representation space to generate a graph representation using the readout function. In this paper, we analyze the representation capacity of the MPNNs for aggregating graph information and observe that the existing approaches ignore the self-loop for graph representation learning, leading to limited representation capacity. To alleviate this issue, we introduce a simple yet effective propagation enhanced extension, Self-Connected Neural Message Passing (SC-NMP), which aggregates the node representations of the current step and the graph representation of the previous step. To further improve the information flow, we also propose a Densely Self-Connected Neural Message Passing (DSC-NMP) that connects each layer to every other layer in a feed-forward fashion. Both proposed architectures are applied at each layer and the graph representation can then be used as input into all subsequent layers. Remarkably, combining these two architectures with existing GNN variants can improve these models’ performance for graph representation learning. Extensive experiments on various benchmark datasets strongly demonstrate the effectiveness, leading to superior performance for graph classification and regression tasks.
Remote Special Education during COVID-19: A Combined Bibliometric, Content and Thematic Analysis

While massive research has been conducted to see how remote learning and teaching is conduct during the COVID-19 pandemic, less focus has been paid on remote special education for students with disabilities. Therefore, it is still not clear how those students learned and what types of challenges they faced. To fill this gap, this study first collected data from the literature via a systematic literature review, and from both 51 teachers and 21 students with disabilities who were involved in this remote teaching and learning experiences via surveys. It then conducted bibliometric, content and thematic analysis to draw conclusions. The obtained findings highlighted that online and offline remote teaching methods from home were applied. Additionally, different learning assessment methods, such as mini-projects and simple quizzes were adopted by teachers to assess the gained knowledge of students remotely, but none of these methods relied on emerging technologies, such as big data and learning analytics. Finally, parents were a core actor to maintain remote learning from home for students with disabilities.
Trust in DeFi: An Empirical Study of the Decentralized Exchange

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
A Course Teacher Recommendation Algorithm Based on Improved Latent Factor Model and PersonalRank

To scientifically and accurately recommend suitable teachers for university courses and improve teaching quality, designing an effective recommendation algorithm is necessary. Therefore, we construct quantitative models of teacher characteristics, course characteristics, and teaching evaluations under the theories and methods of education and build a sparse experimental data matrix based on the quantified data. On this basis, we propose a teacher recommendation algorithm (PRLFM) based on the improved latent factor model (LFM) and the improved PersonalRank algorithm. Firstly, the improved LFM is used to predict the evaluation scores of those courses that teachers have not taught. The scores which are higher than the specified threshold are used to fill the corresponding missing items in the sparse matrix to reduce the matrix's sparsity. Then, the bipartite graph model based on the teacher set and course set is constructed according to the filled experimental data matrix. The weight of edges in the bipartite graph is replaced by the teacher and course's evaluation score multiplied by the course difficulty, which reflects the correlation between course and evaluation score. Next, an improved probability transition matrix based on the bipartite graph is constructed. The access probability in the matrix is replaced by the node's out degree's reciprocal multiplied by the edge's weight. The correlation degree between the course and all teachers is quickly calculated using the matrix algorithm of PersonalRank. Finally, a teacher recommendation model is constructed to realize teachers' top-N recommendation by combining the correlation degree with teachers' characteristics. Experiments show that the PRLFM algorithm can effectively improve the accuracy of prediction and top-N recommendation. It solves the problem of lack of scientific basis in recommending suitable teachers for university courses and improving the teaching quality.
Decision Making in Monopoly Using a Hybrid Deep Reinforcement Learning Approach

Learning to adapt and make real-time informed decisions in a dynamic and complex environment is a challenging problem. Monopoly is a popular strategic board game that requires players to make multiple decisions during the game. Decision-making in Monopoly involves many real-world elements such as strategizing, luck, and modeling of opponent’s policies. In this paper, we present novel representations for the state and action space for the full version of Monopoly and define an improved reward function. Using these, we show that our deep reinforcement learning agent can learn winning strategies for Monopoly against different fixed-policy agents. In Monopoly, players can take multiple actions even if it is not their. turn to roll the dice. Some of these actions occur more frequently than others, resulting in a skewed distribution that adversely affects the performance of the learning agent. To tackle the non-uniform distribution of actions, we propose a hybrid approach that combines deep reinforcement learning (for frequent but complex decisions) with a fixed-policy approach (for infrequent but straightforward decisions). We develop learning agents using proximal policy optimization (PPO) and double deep Q-learning (DDQN) algorithms and compare the standard approach to our proposed hybrid approach. Experimental results show that our hybrid agents outperform standard agents by 20% in the number of games won against fixed-policy agents. The hybrid PPO agent performs the best with a win rate of 91% against fixed-policy agents.
Yggdrasil: Privacy-Aware Dual Deduplication in Multi Client Settings

This paper proposes Yggdrasil, a protocol for privacy-aware dual data deduplication in multi-client settings. Yggdrasil is designed to reduce cloud storage space while safeguarding the privacy of clients’ data. This is achieved by exploiting a ‘dual’ setting, where both the cloud and the clients store a fraction of the data. Yggdrasil combines two innovative techniques to achieve this goal. First, generalized deduplication, an emerging solution to reduce data footprint; second, non- deterministic lightweight transformations that ensure a high level of privacy while improving the degree of cross-user data compression in the cloud. Our client preprocessing guarantees that an honest-but-curious cloud storage provider faces a high degree of uncertainty in determining the original clients’ data. We introduce an uncertainty metric to measure the privacy of the client’s outsourced data and three compression metrics to investigate the performance of Yggdrasil. Our experiments with a dataset of DVI files show that Yggdrasil achieves an overall compression rate of 43%, which means that Yggdrasil can represent the same database using less than half of the original space. Moreover, for the same experiment clients only store 17% of the original data, the cloud hosts the remaining 26%, and the client preprocessing ensures each outsourced fragment has 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">293</sup> possible original strings. Higher uncertainty is possible, but reduces the cloud’s compression capability.
FT-TDR: Frequency-Guided Transformer and Top-Down Refinement Network for Blind Face Inpainting

Blind face inpainting refers to the task of reconstructing visual contents without explicitly indicating the corrupted regions in a face image. Inherently, this task faces two challenges: (1) how to detect various mask patterns of different shapes and contents; (2) how to restore visually plausible and pleasing contents in the masked regions. In this paper, we propose a novel two-stage blind face inpainting method named Frequency-guided Transformer and Top-Down Refinement Network ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FT-TDR</b> ) to tackle these challenges. Specifically, we first use a transformer-based network to detect the corrupted regions to be inpainted as masks by modeling the relation among different patches. For improved detection results, we also exploit the frequency modality as complementary information and capture the local contextual incoherence to enhance boundary consistency. Then a top-down refinement network is proposed to hierarchically restore features at different levels and generate contents that are semantically consistent with the unmasked face regions. Extensive experiments demonstrate that our method outperforms current state-of-the-art blind and non-blind face inpainting methods qualitatively and quantitatively.
Blockchain Based Verification Of Vehicle History For Pre-owned Vehicle Industry

The automotive industry is one of the lucrative markets in the world. With the advancement of the human lifestyle, everybody needs a car for their daily business. Before buying a used vehicle, the history of the vehicle should be verified. Currently, the history of the vehicle is obtained from the second-hand vehicle dealers. The data provided by dealers need not be correct because they do not maintain a proper record. The only way for the buyer is to believe the dealer and proceed with the deal. To resolve these problems, we proposed a distributed framework using blockchain technology to verify the history of the vehicles. The creator or deployer in the system is responsible for creating the registration of the vehicle with a unique VIN and it will be updated to the shared ledger. Then the vehicles are distributed to the trusted dealers in the system with the execution of the smart contract in the supply chain and finally released to the user. In the running phase, the proposed model records insurance, real-time data and maintenance services, and legal issues if any. The main advantage of the vehicle verification on the blockchain is that it can reduce the possibility of fraud and counterfeiting documents.
Spliced images detection by using Viola-Jones algorithms method

Photographs are measured to be a useful document that is used to stored life proceedings. The new progress in the representation of image expurgation software cause image operation. The additional and familiar image splicing, will not disappear with any illustration sign that having it interfered. Consequently, it turns complicated to identify whether the images are genuine or not. There are mostly three types of image forgery, these are image splicing, copy-move, and image retouching. There are dissimilar techniques accessible to ensure the dependability of the image. This manuscript proposed a Viola-Jones algorithms technique to detect the splice in images consisting of a human being. The primary stage, detected all the facts presented in the image then it is distorted addicted to dissimilar color spaces. Then it is used illuminant maps of every color space is achieved. Extracted the special feature for example quality of color and a number of the image classes are measured. The SVM classifier is used to detect whether the images are fake or real.
Governance quality, social and macro-economic conditions: implications for financial inclusion

This paper seeks to identify the key factors affecting financial inclusion in developed and developing countries. Considering six dimensions of financial inclusion and different indicators, several regressions have been performed to determine the key factors affecting financial inclusion. Particularly, using OLS regression model, we regress each financial inclusion indicator of each dimension on a set of variables related to social factors, macro-economic factors and institutional quality. Results suggest that institutional quality and social factors are significantly linked to financial inclusion. Our findings have an important policy implication in that institutional quality and social factors should be considered as vital drivers in enhancing the level of financial inclusion which is consistent with Sha’ban et al (2019). Particularly, governments have to implement efficient measures and increase their capacity to control corruption. Finally, our research offers findings of specific interest to identify policies to boost financial inclusion all over the world.
Graph-Convolved Factorization Machines for Personalized Recommendation

Factorization machines (FMs) and their neural network variants (neural FMs) for modeling second-order feature interactions are effective in building modern recommendation systems. However, feature interactions are based upon pairs of features, whereas multi-features correlations commonly arise in real-world financial product recommendation scenarios. We propose an effective neural recommender system, graph-convolved factorization machine (GCFM), with the spirit of the symbolic graph reasoning principle that provides lightweight and interpretable recommendation suggestions. Given a sample for the recommendation, GCFM constructs the corresponding dense feature embeddings and computes the sample-specific feature relationship graph. Then, a multi-filter graph-convolved feature crossing (GCFC) layer for feature embeddings establishes cross features with their neighboring embeddings. GCFM thus extends the feature interactions from pairs to neighbors to capture more comprehensive and explainable information while simultaneously reaping the advantages of representation learning. To exploit these capabilities, we apply a Graph Bayesian Optimization (GBO). During training, our GBO automatically optimizes our GCFM, including training hyperparameters and architecture hyperparameters. Besides, we conduct extensive experiments on two public financial applications benchmarks, USCFC and OTC, and two real-world datasets that we collect offline. Our GCFM significantly outperforms state-of-the-art algorithms and shows its interpretability in recommendation tasks.
Automatic Modulation Classification Using Fractional Low Order Cyclic Spectrum and Deep Residual Networks in Impulsive Noise

Automatic modulation classification (AMC) plays an important role in cognitive radio, surveillance and electronic warfare systems. However, due to the presence of impulsive noise in wireless communication systems, conventional AMC methods which are based on Gaussian noise assumption usually exhibit poor performance in impulsive noise environment. Aiming at realizing efficient AMC with impulsive noise, a novel AMC method based on fractional low order cyclic spectrum (FLOCS) and deep residual networks (ResNets) is proposed in this paper. In this method, FLOCS can effectively suppress impulsive noise, and meanwhile the discriminating features can also be extracted from FLOCS for AMC. Then, on the basis of these features, deep residual networks are employed to classify different modulation types. Monte Carlo simulation results demonstrate that the proposed AMC method can achieve superior classification performance in impulsive noise environment.
A Framework for Testing and Evaluation of Operational Performance of Multi-UAV Systems

In this paper, we propose a data-driven testing and evaluation framework for multi-UAVs to evaluate their performance in executing missions in the physical world. Seven micro-behaviors, termed here as modes of operation, are leveraged to describe the autonomous functionalities of the UAVs. These functionalities are then used to design five scenarios for model training, validation and testing of the proposed framework. Each scenario includes a distinct sequence of behaviors for the UAVs in order for the different autonomous functionalities to be evaluated. We develop and implement a simulation environment using the Robot Operating System (ROS), Gazebo, and the Pixhawk autopilot to generate synthetic data for the training of a classification model. This trained model is then utilized to evaluate the behaviors of the UAVs while performing real-world missions. Finally, the proposed framework is tested using synthetic data generated from a simulation environment and validated using real-world data.
Timestamp prefix carving for filesystem metadata extraction

While file carving is a popular and effective method for extracting file content from unallocated space in a forensic image, it can be time consuming to carve for the wide variety of possible file signatures. Furthermore, file carving does not connect the discovered file to its filesystem metadata. These limitations of file carving are the advantages of Generic Metadata Time Carving, in which filesystem metadata is searched for by first finding repeated co-located timestamps using a potential timestamp carving algorithm. The potential metadata is verified by a filesystem specific parser, and the pointer within the metadata to the file data may allow for full file recovery. Currently, a limitation of the Generic Metadata Time Carving method is that it will only find metadata records that have multiple equivalent timestamps, thus missing metadata records and files with differing, but very similar, timestamps. Therefore, in order to improve the recall of the Generic Metadata Time Carving methodology, we have designed and implemented a prefix matching potential timestamp carving algorithm. We apply our experiments to realistic NTFS and Ext4 forensic images, in which we compare the precision and recall results for differing prefix lengths. Our results indicate that using prefix-based potential timestamp carving can yield significantly greater recall for extracting filesystem metadata records, with little to no reduction in precision as compared to the original exact potential timestamp carving method.
Mapping carbonate-hosted Pb-Zn mineralization zones in Yahyali Province (Eastern Taurus - Turkey) using ASTER data

The first record of the carbonate-hosted Pb-Zn deposits in Turkey dates back to the Roman period. The carbonate-hosted Pb-Zn deposits in Turkey are economically important and found in the Aladağlar - Zamantı Province. Mineral exploration and geological mapping through the utilization of the traditional geological method still remain very challenging in many high terrains (High mountains). In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping the carbonate-hosted Pb-Zn deposits in the Yahyali region of Turkey. The Band Ratio (BR), Least square fitting methοd (Ls–fit), Band Ratio Color Composite (BRCC), and Principal Cοmpοnent Analysis (PCA) were used tο map the Pb-Zn mineralization zone. The results of this investigation show the spatial distribution of Fe-Mg – OH/CO3, hydroxyl-bearing and iron oxide minerals. Field work and laboratory analysis were carried out to verify the occurrence of alteration zones and highly prospective locations and to confirm the availability of alteration and potential zones of carbonate-hosted Pb-Zn mineralization in the study area. Spatial correlation and distribution were observed between the previous geological map, active and abandoned mines, and the result obtained from the remote sensing data. Different techniques applied to the Aster data image in this study are recommended fοr carbonate-hosted Pb-Zn and lithological mapping in hostile terrains.