Reviewerly/exHarmony huggingface dataset:
Contextualized Knowledge Base Sense Embeddings in Word Sense Disambiguation

Contextualized sense embedding has been shown to carry useful semantic information to improve the final results of various Natural Language Processing tasks. However, it is still challenging to integrate them with the information of the knowledge base, which is one lack in current state-of-the-art representations. This integration is helpful in NLP tasks, specifically in the lexical ambiguity problem. In this paper, we present C-KASE (Contextualized-Knowledge base Aware Sense Embedding), a novel approach to producing sense embeddings for the lexical meanings within a lexical knowledge base. The novel difference of our representation is the integration of the knowledge base information and the input text. This representation lies in a space that is comparable to that of contextualized word vectors. C-KASE representations enable a simple 1-Nearest-Neighbour algorithm to perform as well as state-of-the-art models in the English Word Sense Disambiguation task. Since this embedding is specified for each individual knowledge base, it also outperforms in other similar tasks, i.e., Wikification and Named Entity Recognition. The results of comparing our method with recent state-of-the-art methods show the efficiency of our method.
Realtime Face Mask Detection using Machine Learning

The coronavirus COVID-19 causes a global health problem and therefore effective methods of wearing masks in common place according to the WHO. The COVID-19 epidemic forced governments all over the world impose many restrictions. Reports show that wearing mask while working significantly decrease the chance of getting affected. Effective and economical way to use Artificial Intelligence to develop some useful in production settings. The model using the face mask detection machine will be created. The detection database contains a mask and in addition to the facial images, we will use Open Computer Vision to achieve face mask detection in the webcam.
BS-KGS: Blockchain Sharding Empowered Knowledge Graph Storage

Utilizing blockchain-based storage in knowledge graph construction is considered an alternative to ensure both security and traceability of files. Sharding is a promising solution to improve blockchain throughput by concurrently processing transactions. However, there are two key challenges in sharding, namely, difficulties in generating transaction allocation strategy and security threats. This paper addresses above issues and proposes a blockchain sharding-empowered knowledge graph storage (BS-KGS) scheme. To achieve optimal transaction sharding strategies, we construct a dynamic programming algorithm that considers multiple resource constraints. Moreover, our approach guarantees transaction security via implementing several methods, including Verifiable Random Function (VRF), asset mortgage, and Practical Byzantine Fault Tolerance (PBFT) consensus. Evaluation results have demonstrated that our method is efficient in transaction sharding.
SmartCrawler: A Three-Stage Ranking Based Web Crawler for Harvesting Hidden Web Sources

Web crawlers have evolved from performing a meagre task of collecting statistics, security testing, web indexing and numerous other examples. The size and dynamism of the web are making crawling an interesting and challenging... | Find, read and cite all the research you need on Tech Science Press
Mouse on a Ring: A Mouse Action Scheme Based on IMU and Multi-Level Decision Algorithm

The traditional mouse has been used as a main tool for human-computer interaction for more than 50 years. However, it has become unable to cater to people’s need for mobile officing and all-weather use due to its reliance on the support of a two-dimensional plane, poor portability, wearisomeness, and other problems. In this paper, we propose a portable ring-type wireless mouse scheme based on IMU sensors and a multi-level decision algorithm. The user only needs to operate in the air with a smart ring worn on the middle finger of their right hand to realize the interactive function of a mouse. The smart ring first captures changes in the finger’s attitude angle to reflect how the cursor position changes. And, it captures the rapid rotation of the user’s palm to the left and right to achieve mouse clicking. In addition, a multi-level decision algorithm is developed to improve the response speed and recognition accuracy of the virtual mouse. The experimental results show that the virtual mouse has a target selection accuracy of over 96%, which proves its practicability in real-world applications. This virtual mouse is expected to be used as a portable and reliable tool for multi-scenario human-computer interaction applications in the future.
A Catalog of Speculative Playful Urban Technology Ideas

In this paper, we present an annotated portfolio of speculative playful urban technology concepts. Our portfolio builds on and illustrates in designerly ways the findings from our research into the playful potential of urban spaces. We begin by describing two Situated Play Design [1] interventions where we identified play potentials of urban spaces, i.e., playful things that people already do in public spaces and might have socio-emotional value. We suggest that these play potentials could inspire the design of future smart city technology that pays more attention to the social, emotional, and cultural role of urban spaces in human life. To show that potential and foreground the inspirational value of our findings, we present a catalog of speculative design ideas that embody the design directions and play potentials we identified in our research. We hope to contribute to an existing and exciting body of work aimed at inspiring the design of increasingly playful and socio-emotionally sustainable urban technology.
Blink Detection and Duration Estimation by Using Adaptive Threshold with Considering Individual Difference

This paper investigates the real-time detection and duration estimation of blink with considering the individual difference. Based on Kalman filter and depth feature thresholds, an adaptive threshold method which can achieve real-time detection is derived to account for the characteristics variation in different individuals and periods. Moreover, the resultant method can not only specify the position of the blink, but also estimate its duration. For blink detection task, the results are compared with the fixed threshold method and some other methods tested by the same datasets. The experiment results confirm that the proposed method is superior to the fixed threshold method in detection performance, and the results compared with other methods show its promising potential in balancing real-time and detection performance. For duration estimation task, the results show the effectiveness of the proposed method evaluated by Mean absolute error.
Performance Investigation of Bifacial Module Based Time Varying Multilevel Solar Panel System

Performance assessment and energy study of bifacial module-based time-varying multi-level solar panel system (MSPS) is represented in this paper. Necessary modifications of ASHRAE clear day solar flux model evaluate the performance of the proposed bifacial module-based system. Systematic implementation of The MSPS occupies 33% less area. This study orderly observes the performance rate of using the bifacial module in the MSPS system. The rear side of the module adds extra diffused and reflected irradiance resulting in a 63.62% more energy harness than the conventional mono-facial fixed system. Furthermore, the performance analysis shows that the designed system can harnesses 30.05% more energy than the mono-facial based multi-level solar panel system.
Lane Change Detection Using Naturalistic Driving Data

Lane Change (LC) detection is the foundation of LC studies using real-world data. Most current studies use rule-based methods for LC detection from experimental data. In this study, we propose a learning-based method to detect LC using large-scale naturalistic driving data. The dataset is analyzed using big data analytics method, and the potential LC maneuvers are extracted. The LC detection is reformulated as a one-class classification problem, and an autoencoder-based anomaly detection method is developed to solve it. The proposed method is robust to data noises and can achieve better detection performance than the one-class Support Vector Machine (SVM). This work lays the groundwork for future LC studies, such as driving behaviour modeling and traffic safety solutions.
WARX: efficient white-box block cipher based on ARX primitives and random MDS matrix

White-box cryptography aims to provide secure cryptographic primitives and implementations for the white-box attack model, which assumes that an adversary has full access to the implementation of the cryptographic algorithms. Real-world applications require highly efficient and secure white-box schemes, whereas the existing proposals cannot meet this demand. In this paper, we design a new white-box block cipher based on addition/rotation/XOR (ARX) primitives and random maximal distance separable (MDS) matrix, white-box ARX (WARX), aiming for efficient implementations in both black- and white-box models. The implementation of WARX in the black-box model is nine times faster than SPNbox-16 from ASI-ACRYPT'16, and the implementation in the white-box model is more efficient than SPNbox-16 and WEM from CT-RSA'17. Moreover, the security of WARX in both black- and white-box models is analyzed, which ensures its practical applicability. The design of WARX shows that ARX primitives and random linear layer can improve the efficiency of a white-box block cipher. This article may inspire more provably secure and efficient white-box block ciphers and help to narrow the gap between provably secure white-box schemes from academia and highly applicable schemes in great demand from industry.
C2VNet: A Deep Learning Framework Towards Comic Strip to Audio-Visual Scene Synthesis

Advances in technology have propelled the growth of methods and methodologies that can create the desired multimedia content. "Automatic image synthesis" is one such instance that has earned immense importance among researchers. In contrast, audio-video scene synthesis, especially from document images, remains challenging and less investigated. To bridge this gap, we propose a novel framework, Comic-to-Video Network (C2VNet), which evolves panel-by-panel in a comic strip and eventually creates a full-length video (with audio) of a digitized or born-digital storybook. This step-by-step video synthesis process enables the creation of a high-resolution video. The proposed work's primary contributions are; (1) a novel end-to-end comic strip to audio-video scene synthesis framework, (2) an improved panel and text balloon segmentation technique, and (3) a dataset of a digitized comic storybook in the English language with complete annotation and binary masks of the text balloon. Qualitative and quantitative experimental results demonstrate the effectiveness of the proposed C2VNet framework for automatic audio-visual scene synthesis.
Failure Modes and Effects Analysis (FMEA) of Honeypot-Based Cybersecurity Experiment for IoT

Failure Modes and Effects Analysis (FMEA) is the process of identifying potential failure modes, their causes and effects associated with a product, process or system. In this paper, we discuss the application of FMEA in the design of cybersecurity experiments using a medium interaction server honeypot in an Internet of Things (IoT) environment. Through FMEA analysis, we identify the factors affecting the outcome or contributing to the potential failures of the cybersecurity experiment. We discuss the causes of failures, their effects and how to minimise or mitigate them.
Understanding patient reviews with minimum supervision

Understanding patient opinions expressed towards healthcare services in online platforms could allow healthcare professionals to respond to address patients' concerns in a timely manner. Extracting patient opinion towards various aspects of health services is closely related to aspect-based sentiment analysis (ABSA) in which we need to identify both opinion targets and target-specific opinion expressions. The lack of aspect-level annotations however makes it difficult to build such an ABSA system. This paper proposes a joint learning framework for simultaneous unsupervised aspect extraction at the sentence level and supervised sentiment classification at the document level. It achieves 98.2% sentiment classification accuracy when tested on the reviews about healthcare services collected from Yelp, outperforming several strong baselines. Moreover, our model can extract coherent aspects and can automatically infer the distribution of aspects under different polarities without requiring aspect-level annotations for model learning.
Recursive MDS matrices over finite commutative rings

Recursive MDS matrices are used for the design of linear diffusion layers in lightweight cryptographic applications. Most of the works on the construction of recursive MDS matrices either consider matrices over finite fields or block matrices over GL(m,F2). In the first case, there have been works on the direct construction of recursive MDS matrices. The latter case is hard to deal with because of its non-commutative nature. There has not been any serious attempt to look for recursive MDS matrices over finite commutative rings, in particular over local rings of even characteristic. In this work, we present several methods for the construction of recursive MDS companion matrices over finite commutative rings. The main tools are the simple expressions for the determinant of (generalized) Vandermonde and linearized matrices. We show that the determinant of a linearized matrix over a finite commutative ring of prime characteristic can be expressed in a simple form. We discuss a technique called subring construction with which MDS matrices over product rings can be constructed using MDS matrices over subrings. We give a few examples of recursive MDS companion matrices over local rings of even characteristic. We also discuss some results on the nonexistence of recursive MDS matrices over certain rings for some parameter choices.
Asymptotically most powerful tests for random number generators

The problem of constructing the most powerful test for random number generators (RNGs) is considered, where the generators are modelled by stationary ergodic processes. At present, RNGs are widely used in data protection, modelling and simulation systems, computer games, and in many other areas where the generated random numbers should look like binary numbers of a Bernoulli equiprobable sequence. Another problem considered is that of constructing effective statistical tests for random number generators (RNG). Currently, effectiveness of statistical tests for RNGs is mainly estimated based on experiments with various RNGs. We find an asymptotic estimate for the p-value of an optimal test in the case where the alternative hypothesis is a known stationary ergodic source, and then describe a family of tests each of which has the same asymptotic estimate of the p-value for any (unknown) stationary ergodic source. This model appears to be acceptable for binary sequences generated by physical devices that are used in cryptographic data protection systems.
Retrieving Data Permutations from Noisy Observations: High and Low Noise Asymptotics

This paper considers the problem of recovering the permutation of an n-dimensional random vector X observed in Gaussian noise. First, a general expression for the probability of error is derived when a linear decoder (i.e., linear estimator followed by a sorting operation) is used. The derived expression holds with minimal assumptions on the distribution of X and when the noise has memory. Second, for the case of isotropic noise (i.e., noise with a diagonal scalar covariance matrix), the rates of convergence of the probability of error are characterized in the high and low noise regimes. In the low noise regime, for every dimension <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> , the probability of error is shown to behave proportionally to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sigma$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sigma$</tex> is the noise standard deviation. Moreover, the slope is computed exactly for several distributions and it is shown to behave quadratically in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> . In the high noise regime, for every dimension <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> , the probability of correctness is shown to behave as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1/\sigma$</tex> , and the exact expression for the rate of convergence is also provided.
Fast Dual-Field ECDSA Accelerator with Increased Resistance against Horizontal SCA Attacks

Elliptic Curve Cryptography (ECC) is one of the common used standard methods for encrypting and signing messages which is essential when it comes to IoT communication. In this paper we discuss the side channel analysis attack resistance of our fast dual-field ECDSA accelerator. We present our implementation of a design supporting four different NIST Elliptic Curves to allow the reader to understand the discussion of the resistance aspects. Our key findings concerning the resistance are that the dual field design is by far more resistant against SCA than individual designs for B-curves. Here we were able to determine only two key candidates with a correctness above 85% for B-233 compared to the individual design of B-233 for which 6 key candidates with a correctness of more than 95% were revealed. In addition we were able to retrieve 4 candidates with a correctness of more than 97% for P-224 and even 3 fully correct key candidates for P-256. This is especially important as the design for ECs over GF(p) is realized using atomic patterns which is considered in the literature to ensure resistance against horizontal side-channel analysis attacks.
An Improved YOLO Algorithm for Object Detection in All Day Scenarios

Object detection is one of the widest studies in computer vision. And there are many useful algorithms for this task. Though most of the studies are effective in the daytime or dark scene, they are not applicable in some special cases that need to carry out object detection in the scenarios for both daytime as well as night such as rescue scenarios in serious disasters of earthquake, landslide, mountain torrent, flood and on. To solve this issue, we attend to study the object detection algorithm in the scenarios for both daytime as well as dark scene. Considering the high efficiency of the YOLO algorithm under sufficient light conditions as well as the needs of object detection under poor light, this paper proposes an improved end-to-end YOLOv3 network under dark conditions. The main idea is to integrate the YOLOv3 object detection network and the Retinex image enhancement network to ensure that the image be easier to perform feature extraction and object recognition. Extensive experiments were conducted on several public datasets. The results show that compared with the traditional YOLOv3 object detection model, the proposed algorithm can improve the mean average precision from 53.23% to 59.79% in the object detection task with poor light.
Secure Data Sharing with Efficient Key Update for Industrial Cloud-based Access Control

Sharing data in industrial cloud requires both secure and flexibility management of data shared among users in the industry. Users should have ability to access authorized portion of data from smart edge devices with integrated secure protocol for interacting with cloud storage. Among cryptographic-based access control solutions, ciphertext-policy attribute-based encryption (CP-ABE) is recognized as a suitable solution for supporting secure and fine-grained data access control for outsourced data. However, the attribute revocation is a major drawback of CP-ABE since it introduces subsequent costs such as ciphertext re-encryption, user key re-generation, and key re-distribution. Existing revocable CP-ABE based access control models have focused on minimizing the cost of communication and computation cost for ciphertext re-encryption. Unfortunately, the key update issue is subject to re-key generation as traditional CP-ABE does. In essence, key update becomes crucial when there are a high number of users accessing shared data in the cloud. In this paper, we propose a lightweight access control model featured with the efficient key updating scheme. Finally, the performance evaluation is conducted to demonstrate the efficiency of our proposed scheme.
Enhanced lightweight and secure session key establishment protocol for smart home inhabitants

AbstractAbstractThe concept behind the wireless communication and the world of information technologies is to create an environment for the users such that they can easily access the smart lifestyle and get interacted with the social world. Session key should be secure enough as every sensor is using a small token to authenticate every network. We are proposing a new, efficient, secure and light-weight protocol in smart home environments to overcome these shortcomings. The quantity demand and investment in research and development, while the other model focuses on a more realistic relationship between the quantity demand and the price.Subject Classification: 94A60 Cryptography [See also 11T71, 14G50, 68P25, 81P94]Keywords: Smart home environmentCryptanalysisAuthenticationSession key
Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation

Kinship verification is a long-standing research challenge in computer vision. The visual differences presented to the face have a significant effect on the recognition capabilities of the kinship systems. We argue that aggregating multiple visual knowledge can better describe the characteristics of the subject for precise kinship identification. Typically, the age-invariant features can represent more natural facial details. Such age-related transformations are essential for face recognition due to the biological effects of aging. However, the existing methods mainly focus on employing the single-view image features for kinship identification, while more meaningful visual properties such as race and age are directly ignored in the feature learning step. To this end, we propose a novel deep collaborative multi-modal learning (DCML) to integrate the underlying information presented in facial properties in an adaptive manner to strengthen the facial details for effective unsupervised kinship verification. Specifically, we construct a well-designed adaptive feature fusion mechanism, which can jointly leverage the complementary properties from different visual perspectives to produce composite features and draw greater attention to the most informative components of spatial feature maps. Particularly, an adaptive weighting strategy is developed based on a novel attention mechanism, which can enhance the dependencies between different properties by decreasing the information redundancy in channels in a self-adaptive manner. Moreover, we propose to use self-supervised learning to further explore the intrinsic semantics embedded in raw data and enrich the diversity of samples. As such, we could further improve the representation capabilities of kinship feature learning and mitigate the multiple variations from original visual images. To validate the effectiveness of the proposed method, extensive experimental evaluations conducted on four widely-used datasets show that our DCML method is always superior to some state-of-the-art kinship verification methods.