Reviewerly/exHarmony huggingface dataset:
Efficient Shoulder Surfing Resistant PIN Authentication Scheme Based on Localized Tactile Feedback

Conventional PIN authentication schemes cannot resist shoulder-surfing attacks. Therefore, various shoulder-surfing attacks resistant PIN authentication schemes using audio or tactile feedback have been designed for mobile devices. However, the efficiency of most existing shoulder-surfing attacks resistant PIN authentication schemes with sufficient security is unsatisfactory. Recently, devices with touchscreens supporting localized tactile feedback (LTF) have been developed, designed, patented, and/or implemented. It is promising that devices with touchscreens supporting LTF will be widely available in the future. In this paper, we present an efficient shoulder-surfing attacks resistant PIN authentication scheme, VpointsPES, based on LTF. VpointsPES can provide sufficient security and improved usability.
Face Attribute Prediction in Live Video using Fusion of Features and Deep Neural Networks

Face attribute analysis from live video is a valuable aide in biometric-based person identification. This is a challenging task due to variations in lighting, occlusion, pose and other variables. To address it, we propose an effective and robust approach: extract the face features using certain selected layers of the pre-trained Convolutional Neural Network (CNN) models such as AlexNet, GoogleNet and ResNet50. We focus on the intermediate CNN layers, since the reported experimental results suggest that the best results may not always be obtained when extracting deep features using the fully connected layers. Next, we train a linear SVM on the extracted features to perform the attribute classification. We also apply a feature level fusion by concatenating the features extracted from the intermediate layers of the aforementioned networks. Our approach applied on live video achieves an average accuracy of 89.40% using the fused features which is better than the results (between 86.6% and 87%) reported for the CNNs applied only on static images.
Dynamic Data Access Control for Multi-Authority Cloud Storage

Cloud computing have brought a lot of advantages, such as reducing the hardware cost and providing a convenient cloud storage service. More and more people choose to put their private data in the cloud. However, due to data outsourcing and seminal-trusted cloud servers, data access control has becoming a challenging issue. To protect data security and privacy, some ciphertext policy attribute-based encryption (CP-ABE) schemes have been applied to cloud data access control. However, there are two dynamic issues, namely attribute revocation and policy updating, that need to be solved in the existing CP-ABE schemes. In this paper, we propose a fine-grained dynamic multi-authority cloud data access control scheme, which can solve the above two problems. Besides, our scheme can also support user revocation, which make it more practical. The analysis and simulation results show that our scheme is secure in the random oracle model and is efficient.
Blockchain Technology

In this paper, the purpose is to scrutinize the two main obstacles to patent registration, which are associated with novelty and non-obviousness identification and patent pendency. Another purpose is to investigate the potential impact of blockchain technology management on analyzed patent big data results. From the results of this documentary analysis, it can be assumed that blockchain technology management can resolve these two-point limitation and drive the potential for improved patent application searches. The advantage lies in a blockchain advancement that can foresee smart contracts and track the stages of decentralized patent applications. Besides, its function can support AI/software program to exchange big data available for utilization. Nevertheless, some impediments may be anticipated to the patentability on such a blockchain technology: namely, the problem of scope and the hardship of creating a lawful framework for gathering and presenting the blockchain result.
A Unified Multi-output Semi-supervised Network for 3D Face Reconstruction

In this paper, we propose a method to reconstruct fine-grained 3D faces from single images base on a nearly unified multi-output regression network. The network estimates the facial shape, normal and appearance jointly in 2D UV map which preserves spatial adjacency relations among vertexes and provides semantic meaning of each vertex. Three contributions of the proposed method are: 1) we generate the UV map by as-rigid-as-possible parametrization to address the overlapping problem caused by cylindrical unwarp; 2) we directly estimate face normal rather than compute it from the estimated shape to let it catch geometric details from face texture; 3) we propose a post process strategy to generating more realistic faces and to employing the estimated normal. Experiments show that our network is able to learn a uniform appearance and predict more accurate shape from the proposed UV map. Additionally, the post process procedure can improve the quality of facial shapes and add geometric details from estimated normals.
Multi-Hierarchy Attribute Relationship Mining Based Outlier Detection for Categorical Data

Outlier detection for categorical data is very important in many practical scenarios, such as intrusion detection, fraud detection, early detection of diseases, etc. However, there is no inherent difference measure for categorical data. The differences are hidden in complex attribute value relationships. Existing methods do not properly handle the internal relationship and external relationship of attributes, resulting in low accuracy of outlier detection.This paper proposes a novel unsupervised outlier detection method for categorical data based on Multi-Hierarchy Attribute Relationship Mining (MHARM). It detects outliers by mining the hierarchical and complex relationships between attribute values. MHARM first calculates the internal relationship. It processes each attribute independently via an information-theoretic difference to get an internal distance matrix. Then it handles different subhierarchy of external relationship. It divides attributes into two clusters, using mutual information as the correlation measure. For the external relationship of intra-cluster attributes, it iteratively updates an external distance matrix by using an entropy weighted Earth Mover's Distance (EMD) and the internal distance until convergence; for the external relationship of inter-cluster attributes, the joint entropy weighted sum is obtained to be the whole difference between objects. Finally, MHARM uses the sum of whole difference between objects as the outlier score, sorting it for outlier detection. Experimental results show that MHARM has an average AUC value of 13.84% higher than the state-of-the-art methods and significantly reduced the detection volume multiples (dvM) for unearthing 90% outliers on the given nine data sets.
Arabic Search Results Disambiguation: A Set of Benchmarks

Web search engines aim at retrieving relevant results in response to a user information need. The query expressing the user information need can be ambiguous by potentially referring to different meanings or senses. Search results clustering (SRC) attempts to disambiguate query results by grouping them into groups of sense-relevant clusters. Little research was done on Arabic SRC, and one important reason may be the lack of quality benchmarks for SRC testing and evaluation. The main contribution of this paper is to introduce a set of benchmarks for Arabic SRC, called AMBIGArabic to aid in performing SRC experiments. The benchmarks include manually labeled datasets and a dataset based on blind relevance feedback (BRF). The designed benchmarks were used in a series of SRC experiments we performed and the results were encouraging. The benchmarks are being made available for use by researchers working on Arabic SRC.
Distance Learning in Higher Education: Technological Aspect

The presented study analyzes the development of distance higher education. Distance learning, previously available via correspondence between students and teachers, has undergone a radical transformation into online education (e-learning), which uses learning management systems as well as other online and digital tools. Aim . The study aims to structure the major elements of directions for the development of distance learning in Russian and foreign higher educational institutions. Tasks . The author examines the existing types of distance learning and their effects on student activity, exploring the essence of requirements for the successful implementation of distance learning systems. Methods . This study uses analytical methods and methods for structuring and systematizing information. The author makes reference to the works of other researchers on distance learning and e-learning, uses legal information and materials from Internet sources. Results . Application of information and communication technologies (ICT) in education has great potential. It is necessary to thoroughly analyze technological and digital systems that ensure the efficiency distance learning. There are many platforms, both open and private, for distance learning. Conclusions. Realization of innovative potential will improve the level of higher education in Russia and its prestige in the global market, while implementation of innovative research programs will mitigate the problem of brain drain.
Hierarchical Multi-dimensional Attention Model for Answer Selection

Answer selection is an important subtask of the question answering domain in natural language processing(NLP) applications. In this task, attention mechanism is a widely used technique which focuses on the context information and interrelationship between different words in the sentences to allocate different weight and enhance feature. However, the natural characteristics of words themselves are not fully excavated, thus the performance may be limited to a certain extent. In this paper, we propose a novel Hierarchical Multidimensional Attention (HMDA) model to address this issue. Especially, HMDA proposes a new kind of attention mechanism, word-attention, a true individual attention which can enhance the implied meaning of the word itself to extract features from word level which are more unique. Then HMDA uses global co-attention to better utilize word-attention and capture more common similar features. In order to utilize this attention-based semantic information on different granularities differently, HMDA designs a multi-layer structure which makes full use of all attention mechanisms by embedding attention features to model hierarchically. HMDA obtains various fine-grained information between question and candidate answers and avoids information loss. Empirically, we demonstrate that our proposed model can consistently outperform the state-of-the-art baselines under different evaluation metrics on all TrecQA, WikiQA and InsuranceQA datasets.
Computer-Generated Geometry Proofs in a Learning Context

Given its formal, logical, and spatial propertiesProperties, geometry is well suited to teaching environments that include dynamic geometryDynamic geometry systems (DGSs) Dynamic Geometry Environment (DGE)/Dynamic Geometry Systems (DGS) , geometry automated theorem provers (GATPs), and repositories of geometric problems. These tools enable students to explore existing knowledge in addition to creating new constructionsConstruction and testing new conjecturesConjecture. In this chapter, we trace the evolution of current automatic proving technologies, how these technologies are beginning to be used by geometry practitioners in general to validate geometric conjecturesConjecture and generate proofs with naturalNatural language languageLanguage and visual rendering, and foresee their evolution and applicability in an educational setting.
The knowledge of knots: an interdisciplinary literature review

Knots can be found and used in a variety of situations in the 3D world, such as in vines, in the DNA, polymer chains, electrical wires, in mountaineering, seamanship and when ropes or other flexible objects are involved for exerting forces and holding objects in place. Research on knots as topological entities has contributed with a number of findings, not only of interest to pure mathematics, but also to statistical mechanics, quantum physics, genetics, and chemistry. Yet, the cognitive (or algorithmic) aspects involved in the act of tying a knot are a largely uncharted territory. This paper presents a review of the literature related to the investigation of knots from the topological, physical, cognitive and computational (including robotics) standpoints, aiming at bridging the gap between pure mathematical work on knot theory and macroscopic physical knots, with an eye to applications and modeling.
Vector Quantization of High-Dimensional Speech Spectra Using Deep Neural Network

This paper proposes a deep neural network (DNN) based framework to address the problem of vector quantization (VQ) for high-dimensional data. The main challenge of applying DNN to VQ is how to reduce the binary coding error of the auto-encoder when the distribution of the coding units is far from binary. To address this problem, three fine-tuning methods have been adopted: 1) adding Gaussian noise to the input of the coding layer, 2) forcing the output of the coding layer to be binary, 3) adding a non-binary penalty term to the loss function. These fine-tuning methods have been extensively evaluated on quantizing speech magnitude spectra. The results demonstrated that each of the methods is useful for improving the coding performance. When implemented for quantizing 968-dimensional speech spectra using only 18-bit, the DNN-based VQ framework achieved an averaged PESQ of about 2.09, which is far beyond the capability of conventional VQ methods.
The GMM and I-Vector Systems Based on Spoofing Algorithms for Speaker Spoofing Detection

Automatic Speaker Verification (ASV) systems are more vulnerable to being attacked than other biometric systems, such as speech synthesis, voice conversion, and replay. In this paper, two frameworks (Gaussian mixture model based and i-vector based) are used to detect a variety of specific attack types. Three scoring methods (probabilistic normalization, linear regression and support vector machine) are used for the Gaussian Mixture Model (GMM) model. And three different classifiers (cosine distance, probabilistic linear discriminant analysis, support vector machine) are used for the i-vector system. Furthermore, the cosine classifier based on the i-vector system which uses three scoring methods is proposed in this paper. Experiments on the ASVspoof 2019 challenge logical access scenario show that the GMM classifier with the Support Vector Machines (SVM) scoring method based on different spoofing algorithms obtains the best performance on the evaluation set with EER of 7.03%. Moreover, SVM scoring is also useful for improving the i-vector system based on different spoofing algorithms.
A Shortcut-Stacked Document Encoder for Extractive Text Summarization

While doing summarization, human needs to understand the whole document, rather than separately understanding each sentence in the document. However, inter-sentence features within one document are not adequately modeled by previous neural network-based models that almost use only one layer recurrent neural network as document encoder. To learn high quality context-aware representation, we propose a shortcut-stacked document encoder for extractive summarization. We use multiple stacked bidirectional long short-term memory (LSTM) layers and add shortcut connections between LSTM layers to increase representation capacity. The shortcut-stacked document encoder is built on a temporal convolutional neural network-based sentence encoder to capture the hierarchical structure of the document. Then sentence representations encoded by document encoder are fed to a sentence selection classifier for summary extraction. Experiments on the well-known CNN/Daily Mail dataset show that the proposed model outperforms several recently proposed strong baselines, including both extractive and abstractive neural network-based models. Furthermore, the ablation analysis and position analysis also demonstrate the effectiveness of the proposed shortcut-stacked document encoder.
Encrypt 3D Model Using Transposition, Substitution, Folding, and Shifting (TSFS)

With the development of digital signal processing and communication networks in the past few years, using 3D object types in a wide range of applications via the internet has greatly grown. Studying 3D object protection is of high importance for the aim of maintaining confidentiality. In this paper, a new 3D objects model encryption method using Transposition Substitution Folding and Shifting (TSFS) algorithm is proposed. TSFS algorithm is utilized with 3 distinct and strong keys after 2 sub-steps for the algorithm of key generation and expansion via column shifting. With the implementation of the improved version of the algorithm, an effective and strong security system is achieved to make 3D objects more secure.
OSTB: Optimizing Fairness and Efficiency for Coflow Scheduling without Prior Knowledge

Fair and efficient coflow scheduling has witnessed an increasing wave of popularity in improving application-level network performance in data-parallel clusters. With coflow size information, previous solutions perform well in simultaneously achieving fairness and fast completion for coflows. However, as coflow size is difficult to obtain in practice, scheduling coflows without prior information attracts much attention recently. Despite the prevalence, existing information-agnostic solutions either solely focus on fairness or fast completion. In this paper, we make an attempt to achieve both fairness and fast completion when scheduling coflows without prior knowledge of coflow size. Specifically, we propose an information-agnostic coflow scheduler-OSTB, which strives to achieve a better isolation guarantee in the long run toward fair network sharing while improving the efficiency by reducing the average CCT (Coflow Completion Time). The key idea of OSTB is to intelligently give smaller coflows more opportunities to send data, while still guaranteeing that the wider and larger coflows have available bandwidth all the time. Large-scale trace-driven simulations demonstrate that OSTB incurs 59% fewer delayed coflows than NC-DRF when both of them use the isolation-optimal solution (e.g, DRF) as baseline. Meanwhile, OSTB is 1.2× faster than Aalo in terms of the average CCT.
Propose Image Encryption Watermarking Algorithm Based on Frequency and Geometric Transform

Presently, multi-day data security is the greatest issue of challenging over the world. There are various systems accessible for image and data security; watermarking is one of them. This paper proposes a new technique for watermarking encryption image by using LSB and based on the frequency and geometric transform. In this work, the Discrete Wavelet Transform (DWT) technique is used to deal with the cover image at the frequency domain without spatial domain where late studies have shown the strength of this type of transform. The rotation geometric transforms applying on the logo image to preserve it and to protect it from eavesdropping and unauthorized entities. The rotate transform considers the type of disguise. Therefore, to increase the security of the watermarking system, it should be considered to also apply Rivest Cipher 4 (RC4) encryption algorithms on logo image before perform watermarking embedding. In finally Least Significant Bit (LSB) is used to embed a logo image in the cover image. The demonstrated experiment is based on Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE), the DWT achieves better results as compared to other methods when the rate of PSNR reached from 50 dB to 60 dB.
Comparison of Radar Signal Sorting Method Between Single and Multi-parameter Based on

With the increasing complexity of radar waveform design, traditional radar signals such as fixed-frequency, jagged, and jitter are becoming less and less common in modern radar design, and complex repeat frequency patterns such as agile and random repeat frequency become the current radar. Therefore, the sorting algorithm based on PRI has been difficult to adapt to the radar signal sorting of complex systems. In addition, the PRI-based sorting algorithm also has the problems of slow speed and poor sorting effect. In contrast, multi-parameter-based sorting method could achieve better results.
Secure Information Transmission Framework in Wireless Body Area Networks

Secured information transmission is one of the critical challenges in Wireless Body Area Networks (WBAN). The main motivation of this paper is secure transmission of highly sensitive patient information with reduced computation cost and storage requirements. In this paper, we integrate Ciphertext-Policy Attribute Based Encryption (CPABE) with Constant Size Ciphertext (CPABE-CSC) and Blowfish algorithm for secure transmission of sensitive patient information. The experimental results show the efficiency of the proposed scheme in terms of computation cost and ciphertext size when compared with existing schemes.
Intelligent Watermarking System Based on Soft Computing

Digital watermarking for various multimedia contents is an effective solution for copyright protection. The techniques of 3D Watermarking carefully insert some secret bits of data in 3D object. These techniques contain contradictory parameters as watermarked object quality (invisibility), capacity and watermark robustness. Thus these parameters should be expressed in fitness functions then trying to estimate their value via utilizing an optimization technique. Thus this paper suggests an optimized watermarking algorithm dependent on non-dominated sorting genetic algorithm II (NSGA-II) optimizer. In this framework, watermark is hided in low frequency coefficients in spectrum domain utilizing an inserting technique dependent on Dither Modulation (DM) technique. The aim of optimization is to look for optimal quantization step, so as to enhance both quality and robustness. As well as, performance of proposed algorithm is analyzed in expressions of signal to noise ratio and correlation coefficient. The investigational results indicate that proposed algorithm can accomplish a good robustness for most of included studied attacks in this research.
Generating a Lexicon for the Hijazi Dialect in Arabic

We present a methodology for creating a lexicon for a low-resource Arabic dialect in Saudi Arabia: Hijazi. We show the differences between the Hijazi dialect and Modern Standard Arabic. We annotate articles and tweets using recruited native speakers. We create a lexicon of Hijazi adapted from two resources: Sebawai and Quranic Arabic Corpus. The lexicon is created both manually and automatically by using Hijazi morphology. We detail the methodology to build this lexicon and present results of an evaluation of the corpus formation process.