Reviewerly/exHarmony huggingface dataset:
Privacy-preserving SVM Classification Algorithm Based on Negative Database

In the era of big data, data mining algorithms usually handle plenty of data and the data may involve sensitive information about people in some scenarios, so privacy protection becomes an important task. Support Vector Machine (SVM) is a popular data mining algorithm widely used in various fields. In this paper, we mainly study the problem of privacy protection during SVM classification. Nowadays, lots of privacy-preserving SVM approaches have been proposed, but many of them have some flaws. Negative database (NDB) is a new type of data representation, which stores the complementary information of data. It is considered as a promising technique for privacy-preserving data mining, since reversing NDB has been proven as an NP-hard problem. Therefore, we propose a privacy-preserving SVM method based on NDBs. We also design a model for estimating the results of the dot product from NDBs, which is the basis of SVM kernel functions. Private data are converted into NDBs before being transmitted to other parties, and thus privacy is protected. Then SVM classification can be effectively performed using the proposed estimating model. We conduct several experiments on five public datasets, and the results demonstrate the effectiveness of the proposed method.
Print-Cam Robust Image Watermarking Based on Hybrid Domain

Image watermarking is an effective means of hiding and reading information. However, the process of printing out an image and then taking a picture of it, also known as the print-cam process, introduces major noises such as geometric distortion and picture blurring as well as color degradation. It is a strong attack party. Digital image watermarking is often not robust to print-cam attacks. In this paper, we propose an effective image watermarking method against print-cam attacks embedded in the composite domain of the image. The B channel of the image is selected for watermark embedding. Firstly, the Discrete Wavelet Transform (DWT) transform is performed on the image, and then the blocks in the LL sub-band are taken and the Discrete Cosine Transform (DCT) is performed, finally the embedding of watermark information is completed by modifying the middle frequency in the frequency domain. When extracting the watermark, Scale-Invariant Feature Transform (SIFT) feature points are firstly used to calculate the image feature points, and then the matching calculation between the feature points is calculated to get the perspective transformation matrix of the photographed image and corrected. The experimental results show the method has the ability to resist the print shooting attack when the watermark has less effect on the image quality. The means of adjustment is also proposed to ensure the wide applicability of the algorithm, which can meet the needs of many application scenarios in real life.
DRAGON: Dynamic Recurrent Accelerator for Graph Online Convolution

Despite the extraordinary applicative potentiality that dynamic graph inference may entail, its practical-physical implementation has been a topic seldom explored in literature. Although graph inference through neural networks has received plenty of algorithmic innovation, its transfer to the physical world has not found similar development. This is understandable since the most preeminent Euclidean acceleration techniques from CNN have little implication in the non-Euclidean nature of relational graphs. Instead of coping with the challenges arising from forcing naturally sparse structures into more inflexible stochastic arrangements, in DRAGON, we embrace this characteristic in order to promote acceleration. Inspired by high-performance computing approaches like Parallel Multi-moth Flame Optimization for Link Prediction (PMFO-LP), we propose and implement a novel efficient architecture, capable of producing similar speed-up and performance than baseline but at a fraction of its hardware requirements and power consumption. We leverage the hidden parallelistic capacity of our previously developed static graph convolutional processor ACE-GCN and expanded it with RNN structures, allowing the deployment of a multi-processing network referenced around a common pool of proximity-based centroids. Experimental results demonstrate outstanding acceleration. In comparison with the fastest CPU-based software implementation available in the literature, DRAGON has achieved roughly 191× speed-up. Under the largest configuration and dataset, DRAGON was also able to overtake a more power-hungry PMFO-LP by almost 1.59× in speed, and at around 89.59% in power efficiency. More importantly than raw acceleration, we demonstrate the unique functional qualities of our approach as a flexible and fault-tolerant solution that makes it an interesting alternative for an anthology of applicative scenarios.
SUPERVOICE

Voice-activated systems are integrated into a variety of desktop, mobile, and Internet-of-Things (IoT) devices. However, voice spoofing attacks, such as impersonation and replay attacks, in which malicious attackers synthesize the voice of a victim or simply replay it, have brought growing security concerns. Existing speaker verification techniques distinguish individual speakers via the spectrographic features extracted from an audible frequency range of voice commands. However, they often have high error rates and/or long delays. In this paper, we explore a new direction of human voice research by scrutinizing the unique characteristics of human speech at the ultrasound frequency band. Our research indicates that the high-frequency ultrasound components (e.g. speech fricatives) from 20 to 48 kHz can significantly enhance the security and accuracy of speaker verification. We propose a speaker verification system, SUPERVOICE that uses a two-stream DNN architecture with a feature fusion mechanism to generate distinctive speaker models. To test the system, we create a speech dataset with 12 hours of audio (8,950 voice samples) from 127 participants. In addition, we create a second spoofed voice dataset to evaluate its security. In order to balance between controlled recordings and real-world applications, the audio recordings are collected from two quiet rooms by 8 different recording devices, including 7 smartphones and an ultrasound microphone. Our evaluation shows that SUPERVOICE achieves 0.58% equal error rate in the speaker verification task, which reduces the best equal error rate of the existing systems by 86.1%. SUPERVOICE only takes 120 ms for testing an incoming utterance, outperforming all existing speaker verification systems. Moreover, within 91 ms processing time, SUPERVOICE achieves 0% equal error rate in detecting replay attacks launched by 5 different loudspeakers. Finally, we demonstrate that SUPERVOICE can be used in retail smartphones by integrating an off-the-shelf ultrasound microphone.
Stay Together to be Strong: How Online Knowledge Sharing Matters

In an organisational crisis, employees often face role conflicts and job insecurity as well as cynicism and emotional exhaustion. This study aims to investigate the role of online knowledge sharing as an internal marketing solution that connects employees with role conflict or a sense of job insecurity to support each other to enhance job performance. Data were collected in Vietnam with 281 eligible responses from those who worked during the pandemic. The results indicated that role conflict positively influenced job insecurity, knowledge donating, and knowledge collecting. Job insecurity had a significant impact on knowledge donating and job performance. Knowledge donating and collecting significantly affected job performance and mediated the impact of role conflict and job insecurity on job performance. The competitive mediation of knowledge donating and knowledge collecting was identified. Emotional exhaustion and cynicism were found to moderate the influence of role conflict on knowledge donating and collecting in this study.
Education management at primary education during the Covid-19 crisis from the perspective of families (<i>La gestión académica de los centros de Educación Primaria durante la pandemia de Covid-19 desde el punto de vista de las familias</i>)

The Covid-19 crisis has affected education management, and now, family members must accomplish tasks previously performed by the school. This study aims to examine it in primary education from the perspective of 638 families with children at that level. It uses a quantitative methodological approach. Results show that most families have maintained their already established work routines. However, families must now combine them with the supervision of the children’s school tasks, which falls mainly to mothers, regardless of their working situation. However, a significant number of families are not attending to their children’s school tasks adequately, which could lead to severe inequalities in the education field. This situation offers an opportunity to establish fairer and more balanced strategies and synergies.
Fuzzy-Based Approach for Clustering Data with Multivalued Features

In analysis of data, objects have mostly been characterized by a set of characteristics known as attributes, which together contained only one value for each object. Besides that, a few attributes in reality could include with more than a single value; such as from a human beside multiple profession characterizations, practises, communication methods, and capabilities, in addition to shipping addresses, of that kind of attributes are referred to as multivalued attributes and are typically regarded as null attributes when data is processed employing machine learning procedures. Throughout this article, another similarity mechanism is introduced that is defined around including multivalued characteristics which can be used for grouping. We propose a model to analyse each factor’s relative prominence for different data collection challenges in order to enable the selection among the most suited multivalued elements. The suggested methodology is a clustering technique for development and evolution that employs fuzzy c-means clustering and retains the new and more effective membership component by implementing the proposed similarity metric. Clustering of multivalued variables using fuzzy c-means is the efficient grouping criteria that results; any methodology to group-related data appears viable. The results show that our assessment not only improves previous segmentation methods on the multivalued cluster-based architecture but also helps in the improvement of the standard similarity metrics.
SoK

In mobile security research, one of the many challenges is the lack of trusted user interfaces (TUI). Currently, users are not able to reliably identify which screen content is genuine and which is potentially manipulated. The concepts of trusted displays and user interfaces, which offer a high confidence in the trustworthiness of screen contents, are not universally implemented on current consumer devices. In this paper, we systematically analyze the developments in the field of TUIs on mobile devices over seven years. We present a new taxonomy to define and categorize challenges and issues in current UI designs, with a focus on issues that negatively affect the security of the whole OS. In addition, we suggest directions where contributions in research could solve these issues.
Single Image Defogging Method Based on Improved Generative Adversarial Network

Aiming at the problems of blurred image content, reduced contrast, color distortion, loss of information, and degradation of scene images caused by haze weather, an improved end-to-end single image defogging method is proposed. This method can learn transmission map, atmospheric light value and dehazing at the same time, including two stages: physical stage and deep learning stage. Then two kinds of modules are introduced in the second stage, including; attention mechanism and inception. The experimental results show that compared with the benchmark method of this article, the dehazing rate of psnr and ssim in the indoor and outdoor data sets are improved by 0.1 and 2.0 points, respectively. To mention the effectiveness of the method.
DeDa: A DeFi-enabled Data Sharing and Trading System

Without relying on centralized intermediaries to participate in financial services such as exchanging and lending assets, Decentralized Finance (DeFi) has set off a new paradigm of asset self-management. On the other hand, data as an emerging asset becomes increasingly significant in our daily life. How to self-manage our data assets to raise their value deserves further exploration. Inspired by DeFi, we envision a decentralized eco-system/community where people can donate or trade data end-to-end for the public good and/or maximize data value. This paper explores this vision by building a DeFi-based data sharing and trading system. In order to explore the actual value of data in the market, we propose an elastic data pricing scheme that can dynamically adjust data prices based on data usage and timing. Finally, we implement and integrate these critical components into a prototyping system deployed onto Ethereum - the current leading blockchain system, and evaluate the system service performance, smart contracts functionality, and the robustness of the data pricing scheme.
Improving the Deep-Learning-Based Differential Distinguisher and Applications to Simeck

In CRYPTO’2019, Gohr firstly combined deep learning with differential cryptanalysis and obtained the deeplearning-based differential distinguishers on round-reduced Speck32/64. In this paper, we introduce the repeated experiment techniques to improve the deep-learning-based differential distinguishers in Gohr’s work. To prove the effectiveness of our techniques, we apply our improved deep-learning-based differential distinguisher scheme into round-reduced Simeck32/64 and obtain long to 15-round deep-learning-based differential distinguisher. To the best of our knowledge, this is the longest-round distinguisher for Simeck32/64 when compared with the 14round differential distinguisher obtained by traditional wisdom.
Assessment of Holographic Environment for Learning Sensing Technologies in CEM Education

Despite the benefits and suitability of mixed reality as a learning tool for spatial and haptic delivery of educational content, the adaptability of mixed reality for teaching sensing technologies in construction education remains ambiguous. Sensing technologies have potentials to advance construction performance as highlighted by several studies and construction industry reports. To equip future workforce with the required skills, it is important they experientially deploying these technologies on the jobsite. However, institutions are experiencing different constraints such as weather, schedule, and COVID-19 restrictions to provide future workforce with required hands-on learning. The study argues the pedagogical value of a mixed reality learning environment for teaching sensing technologies through a usability study with construction engineering and management students who are familiar with sensing technologies. The study employed quantitative and subjective users’ evaluation through a cognitive walkthrough of the learning environment using a mixed reality head-mounted display. Subjective data were obtained using think-aloud protocol and questionnaires on users’ experience. Eye tracking device inbuilt in the mixed reality head-mounted display was employed to monitor the eye gaze movements and provide quantitative data of users’ cognitive workload. Results from eye tracking, think-aloud protocol and questionnaires were triangulated to validate findings from the study. The study reveals the ease of use and effectiveness of the environment for learning sensing technologies. The characteristics, benefits, and deficiencies of the learning environment as a pedagogical tool in construction education are further highlighted.
Weighted Semi-supervised MRI Segmentation with Paired Consistency and Entropy Minimization

U-net is very popular in medical image segmentation. However, the labels of training sets are always lacking for supervised learning. Thus, semi-supervised learning which utilizes unlabeled data shows advantages when dealing with such label-scarce situations. In this paper, we utilize U-net to tackle the challenge of segmenting MR images in semi-supervised setting. Specifically, we introduce paired consistency (PC) as data augmentation method. Also, we propose an entropy minimization (EM) term which is flexible and easy for calculation to boost the semi-supervised learning. Specifically, experiments show that our semi-supervised framework with PC and EM outperforms supervised U-net by %0.3 and %0.5 on OASIS and Hammers datasets, respectively. Also, entropy minimization accelerates the convergence of training, namely, reducing nearly %50 of training time, which is beneficial to real-world application.
Blockchain-based Reputation Evaluation Using Game Theory in Social Networking

Reputation evaluation is one of vital elements in contemporary social network-based applications. A common type of evaluation method is relying on the third party or a community-based rating. However, this type of method is encountering an issue caused by fake comments posted by adversaries. In this paper, we propose a blockchain-based reputation evaluation approach that uses game theory to address the ghost commentator issue. Over the blockchain network, our approach establishes a reputation-based personal credit evaluation system, in which participants' strategies and payoffs are modeled. A smart contract-based deposit incentive mechanism is developed. We have implemented an experimental evaluation to show the evidence to the performance of our approach.
Inductive Vulnerability Detection via Gated Graph Neural Network

Vulnerability detection is an essential means to ensure the normal operation of various software tools and system security. The Recurrent Neural Networks (RNNs) have achieved remarkable results in vulnerability detection, but the sequence-based code representation has great limitations in feature expression and propagation. In this paper, we propose a fine-grained code vulnerability detection framework based on Gated Graph Neural Network (GGNN). Firstly, we process the source code into fine-grained slices. Secondly, graph embedding of code slices is constructed by clustering neighborhood information. Finally, GGNN is used to learn the syntax and semantic information of vulnerability codes for graph-level classification. Furthermore, we theoretically analyze that GGNN has a strong inductive learning ability. This means that the model requires only a small amount of training data to obtain sufficient advanced features, which is significant for vulnerability detection tasks that are difficult to collect data sets. We carry out conventional experiments and inductive experiments with manually collected data sets, and the results show that the framework is superior to RNNs in vulnerability detection performance. Moreover, our framework performs better than RNNs under inductive conditions.
Set of Support, Demodulation, Paramodulation: A Historical Perspective

Abstract This article is a tribute to the scientific legacy of automated reasoning pioneer and JAR founder Lawrence T. (Larry) Wos. Larry’s main technical contributions were the set-of-support strategy for resolution theorem proving, and the demodulation and paramodulation inference rules for building equality into resolution. Starting from the original definitions of these concepts in Larry’s papers, this survey traces their evolution, unearthing the often forgotten trails that connect Larry’s original definitions to those that became standard in the field.
An unmanned aerial vehicle identification and tracking system based on weakly supervised semantic segmentation technology

This paper presents an unmanned aerial vehicle (UAV) identification and tracking system aimed at monitoring UAVs based on weakly supervised semantic segmentation. A camera is equipped with a pan–tilt to collect images for semantic segmentation network in real time. The GrabCut+ algorithm and annotation boxes are employed to generate the UAV “pseudo pixel labels” for supervised model learning and reduce labelling costs. A new loss function combining the focus loss function and dice loss function is designed to balance positive and negative samples and improve the segmentation effect. The Mixup method is introduced for model training to prevent overfitting and enhance the generalization ability of the model. The semantic segmentation network outputs the prediction results by a fully connected conditional random field to smooth the target image. Furthermore, a region-based tracking method is proposed to solve the hysteresis problem of the pan–tilt control system and improve the system tracking performance. Finally, an experiment based on a dataset is carried out to prove the effectiveness of the segmentation algorithm with 66.3% mIoU. Considering that 10% of the central area of view is specified as the view centre, a UAV falling in the centre of the field accounts for more than 80% of this view area, demonstrating the real-time effectiveness of the designed UAV identification and tracking system.
COMSATS Face: A Dataset of Face Images with Pose Variations, Its Design, and Aspects

Due to the three-dimensional formation and flexibility, a human face may appear different in numerous events. Researchers are developing robust and efficient algorithms for face detection, face recognition, and face expression analysis, causing several difficulties due to face poses, illumination, face expression, head orientation, occlusion, hairstyle, etc. To determine the effectiveness of the algorithms, it needs to be tested using a specific benchmark of face images/databases. Face pose is an important factor that severely reduces the recognition ability. In this paper, two contributions are made: (i) a dataset of face images with multiple poses is introduced. The dataset includes 850 images of 50 individuals under 17 different poses (0°, 5°, 10°, 15°, 20°, 25°, 30°, 35°, 55°, -5°, -10°, -15°, -20°, -25°, -30°, -35°, -55°). These images were captured closed to real-world conditions in the time span of five months in COMSATS University, Abbottabad Campus. Face images included in this dataset can reveal the efficiency and robustness of future face detection and face recognition algorithms. (ii) A comparative analysis of three face recognition algorithms such as PAL, PCA, and LDA is presented based on the proposed face database.
Provable Cloud Data Transfer with Efficient Integrity Auditing for Cloud Computing

Due to the promising market prospects, more and more enterprises invest cloud storage and offer on-demand data storage services, which are characterized by distinct quality; thus, users would dynamically change the cloud service providers and transfer their outsourced data. However, the original cloud server might not honestly transfer the outsourced data for saving overhead, and the outsourced data might be polluted during the transfer process. Therefore, how to achieve secure outsourced data transfer has become a primary concern of users. To solve this issue, we put forward a solution for the issue of provable cloud data transfer, which can also simultaneously realize efficient data integrity auditing. By taking the advantages of Merkle sum hash tree (MSHT), our presented scheme can satisfy verifiability without dependency on a third party auditor (TPA). Meanwhile, the formal security proof can demonstrate that our presented scheme meets all of the expected security requirements. Finally, we implement our presented scheme and offer the precise performance evaluation, which can intuitively show the high-efficiency and practicality of our presented scheme in the real-world applications.