Reviewerly/exHarmony huggingface dataset:
Innovation within networks – patent strategies for blockchain technology

Purpose Understanding a technology’s patent landscape, including patent strategies, helps organizations position themselves regarding their innovation and provides insight about a technology’s future direction. This study aims to provide an overview of the blockchain technology patenting trends and outlines an exploratory framework of patenting strategies for blockchain. Design/methodology/approach A total of 3,234 registered patents are analyzed to determine the geographical distribution and identify key actors patenting around the globe. In addition, an empirical study consisting of multiple case studies in the form of ten in-depth interviews with owners/managers of organizations based in North America was conducted to understand organizations’ strategies for patenting the blockchain technology. Findings Several novel insights regarding the strategies are used for blockchain technology patenting. For example, the existence of strong anti-patent sentiment which results in a lack of patenting by start-up organizations or has led to a form of open source patenting strategy. Larger organizations appear to be patenting defensively, and small to medium organizations are primarily patenting to defend their competitive advantage. Practical implications Start-up organizations harboring anti-patent sentiment should consider the open-source patenting strategy to ensure that the collaborative innovation network can continue. They should also consider collaborating with other actors within the network to have a competitive position in the market. Originality/value To the authors’ knowledge, this paper is the first to conduct an empirical study with organizations currently using the blockchain technology to understand patenting strategies used for blockchain.
Hand Gesture Recognition System For Translating Indian Sign Language Into Text And Speech

Communication is merely a means of transferring information from one place, person or group to another. Vocal Communication is the manner in which human being interact with others. Be that because it might, not every one of us isn't able to share our thoughts in verbally due to some physical disabilities. It is very difficult for Deaf and Mute people to convey their thoughts and ideas with normal people. Most of the people does not know sign language which make it difficult for silent people to communicate with others. Some devices are available that convert sign language to text and speech in English, but no device is available for any other language specifically Malayalam. The proposed system will recognize Indian Sign language and convert it into speech and text in 2 languages English and Malayalam and display it on Android phone.
Context-Similarity Collaborative Filtering Recommendation

This article proposes a new method to overcome the sparse data problem of the collaborative filtering models (CF models) by considering the homologous relationship between users or items calculated on contextual attributes when we build the CF models. In the traditional CF models, the results are built only based on data from the user's ratings for items. The results of the proposed models are calculated on two factors: (1) the similar factors based on rating values; (2) the similar factors based on contextual attributes. The findings from the experimentation on two datasets DePaulMovie and InCarMusic, show that the proposed models have higher accuracy than the traditional CF models.
A novel demodulation structure for quadrate modulation signals using the segmentary neural network modelling

In digital communication, the baseband information signal is modulated by the high-frequency carrier to produce a passband signal and applied to the transmission line. QASK (quadrature amplitude-shift keying), QFSK (quadrature frequency-shift keying), and QPSK (quadrature phase-shift keying) modulations from Quadrate type digital modulations are used for high-speed communication in passband digital modulations. They are a widespread modulation for fast and easy data transmission, especially in wireless communication and modem devices. In these modulations, four separate carriers are used, and since each carrier represents 2 bits, the transmission rate doubles compared to conventional digital modulation. This study aims to obtain the baseband signal from quadrate type modulation signals. For this purpose, all 8-bit data between Decimal 0–255 were obtained in QASK, QFSK, and QPSK modulations and signal matrices were formed. The various SNR (signal to noise ratio) values of 5 dB-10 dB-15 dB-20 dB noise were added to these signals to examine the system performance during the modulated signal transmission. The generated signals were given to the demodulation system developed using different methods and tested. The best results were obtained in developed a segmentary NN (Neural Network). In this study, it was observed that modulation signal matrices were given directly to an ANN (Artificial Neural Network) and that the results could not be predicted with the application of noise matrices for testing. Then the modulation signals with the proposed method are divided into four parts. Each segment represents a two-bit piece of data. In the case of a column matrix, four parts were applied as input to the neural network model. In the output of ANN, the result matrix to be predicted is created. Each modulation signal applied to the network input was classified between 0 and 3 at the output. Modulation data-carrying 8 bits are applied to the network in 4 steps and classified. 4 separate classification data from the ANN output is converted back to 2-bit logic. Therefore, signals carrying 8 bits of data are obtained in 4 steps. After the formation of the ANN network, baseband digital signal estimation was performed quickly in 4 steps across each byte modulation signals under different noises coming into the network, and demodulation data was successfully achieved.
Community Detection in Online Social Networks: A Differentially Private and Parsimonious Approach

Community detection is an effective approach to unveil relationships among individuals in online social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties, such as advertisement companies and hospitals, with access to social networks for different purposes, which can easily lead to a privacy breach. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topology and the users' attributes. We show that with additional prior knowledge, community detection can be performed by querying the information of only a fraction of instead of the entire population. In particular, we first propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Then, we propose a parsimonious node affiliation recovery (NAR) algorithm, which is also differentially private, to unveil the community affiliation information of the whole population based on that of the limited number of queried individuals by solving a sparse optimization problem. Through both theoretical analysis and experimental validation using synthetic and real-world social networks, we demonstrate that the proposed DPCD scheme detects social communities under the modest privacy budget. In addition, we show the effectiveness of NAR to perform community detection by querying a limited number of individuals in social networks.
From Charge to Spin and Spin to Charge: Stochastic Magnets for Probabilistic Switching

As the rapid pace of Moore's Law has been slowing down, there has been intense activity to “reinvent the transistor.” An emerging paradigm is to complement the existing complementary metal-oxide-semiconductor (CMOS) technology with new functionalities, rather than finding a drop-in replacement for it. In this article, we discuss such a complementary approach that we call probabilistic spin logic (PSL) based on the concept of a probabilistic or p-bit. p-bits fluctuate between 0 and 1 and can be imagined in between deterministic bits that are either 0 or 1 and quantum bits that are a superposition of 0 and 1. Interconnected circuits built out of p-bits (p-circuits) can be broadly useful for machine learning and quantum computing in the solution of problems that conventional CMOS may not be particularly suited for. Although such p-bits can be implemented using standard CMOS technology, we will show that the inherent physics of nanomagnets can naturally provide an energy efficient and scalable p-bit implementation through the use of low-barrier magnetic tunnel junctions (MTJs). In this article, we provide a general description of p-bits and p-circuits and discuss their applications. We review experimental progress toward constructing p-bits and p-circuits exploiting the inherent stochasticity of nanomagnets, from a physics/device/circuits perspective. In particular, we identify building blocks for “write” and “read” operations that can be used in different combinations to construct functional p-bits and p-circuits. Finally, we discuss the prospects and challenges of PSL as an emerging, unconventional computing paradigm for a beyond CMOS era.
Feature selection for hierarchical classification via joint semantic and structural information of labels

Hierarchical Classification is widely used in many real-world applications, where the label space is exhibited as a tree or a Directed Acyclic Graph (DAG) and each label has rich semantic descriptions. Feature selection, as a type of dimension reduction technique, has proven to be effective in improving the performance of machine learning algorithms. However, many existing feature selection methods cannot be directly applied to hierarchical classification problems since they ignore the hierarchical relations and take no advantage of the semantic information in the label space. In this paper, we propose a novel feature selection framework based on semantic and structural information of labels. First, we transform the label description into a mathematical representation and calculate the similarity score between labels as the semantic regularization. Second, we investigate the hierarchical relations in a tree structure of the label space as the structural regularization. Finally, we impose two regularization terms on a sparse learning based model for feature selection. Additionally, we adapt the proposed model to a DAG case, which makes our method more general and robust in many real-world tasks. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework for hierarchical classification domains.
No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

Purpose Subjective quality assessment of displayed magnetic resonance (MR) images plays a key role in diagnosis and the resultant treatment. Therefore, this study aims to introduce a new no‐reference (NR) image quality assessment (IQA) method for the objective, automatic evaluation of MR images and compare its judgments with those of similar techniques. Methods A novel NR‐IQA method was developed. The method uses a sequence of scaled images filtered to enhance high‐frequency components and preserve low‐frequency parts. Since the human visual system (HVS) is sensitive to local image variations and local features often mimic the attraction of the HVS to high‐frequency image regions, they were detected in the filtered images and described. Then, the statistics of obtained descriptors were used to build a quality model via the Support Vector Regression method. Results The method was compared with 21 state‐of‐the‐art techniques for NR‐IQA on a new dataset of 70 distorted MR images assessed by 31 experienced radiologists, using typical evaluation criteria for the comparison of NR measures. The introduced method significantly outperforms the compared approaches, in terms of the correlation with human judgments. Conclusions It is demonstrated that the presented NR‐IQA method for the assessment of MR images is superior to the state‐of‐the‐art NR techniques. The method would be beneficial for a wide range of image processing applications, assessing their outputs and affecting the directions of their development.
Optimized contrast enhancement for tumor detection

Abstract Magnetic resonance imaging (MRI) is a real assistant for doctors. It provides rich information about anatomy of human body for precise diagnosis of a diseases or disorder. But it is quite challenging to extract relevant information from low contrast and poor quality MRI images. Poor visual interpretation is a hindrance in correct diagnosis of a disease. This creates a strong need for contrast enhancement of MRI images. Study of existing literature shows that conventional techniques focus on intensity histogram equalization. These techniques face the problems of over enhancement, noise and unwanted artifacts. Moreover, these are incapable to yield the maximum entropy and brightness preservation. Thus ineffective in diagnosis of a defect/disease such as tumor. This motivates the authors to propose the contrast enhancement model namely optimized double threshold weighted constrained histogram equalization. The model is a pipelined approach that incorporates Otsu's double threshold method, particle swarm optimized weighted constrained model, histogram equalization, adaptive gamma correction, and Wiener filtering. This algorithm preserves all essential information recorded in an image by automatically selecting an appropriate value of threshold for image segmentation. The proposed model is effective in detecting tumor from enhanced MRI images.
A Secure Biometrics and PUFs-Based Authentication Scheme With Key Agreement For Multi-Server Environments

The emergence of multi-server authentication key protocol schemes provides a viable environment for users to easily access the services of multiple legitimate servers through a single registration. Biometric identification technology has the characteristics of forgery difficulty, duplication difficulty and guess difficulty, etc. Therefore, it is an indispensable authentication technology in smart card-based user authentication protocol. There are many shortcomings in the existing schemes based on biometrics, including leakages of biometrics information, smart card theft attack, lack of user anonymity, user impersonation attack, server impersonation, and so on. To overcome these shortcomings, we propose a new user authentication and key agreement scheme in the multi-server environment. To some extent, we not only are able to guarantee the communication security between the user and the servers, but also ensure the physical security of the smart card and biometrics information. In this respect, we use lightweight cryptographic primitives, such as Physically Unclonable Functions (PUFs), Fuzzy extractor and One-way hash functions, and so on. The proposed scheme can effectively protect user's anonymity without the use of password and provide mutual authentication and key agreement in the multi-server environment. Subsequently, we used informal analysis, Burrows-Abadi-Needham Logic (BAN-Logic) proof, and a widely accepted Real-Or-Random model to prove the security and robustness of proposed scheme. Finally, our authentication protocol can protect the security of communication.
Provably Secure Group Signature Schemes From Code-Based Assumptions

We solve an open question in code-based cryptography by introducing two provably secure group signature schemes from code-based assumptions. Our basic scheme satisfies the CPA-anonymity and traceability requirements in the random oracle model, assuming the hardness of the McEliece problem, the Learning Parity with Noise problem, and a variant of the Syndrome Decoding problem. The construction produces smaller key and signature sizes than the previous group signature schemes from lattices, as long as the cardinality of the underlying group does not exceed 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">24</sup> , which is roughly comparable to the current population of the Netherlands. We develop the basic scheme further to achieve the strongest anonymity notion, i.e., CCA-anonymity, with a small overhead in terms of efficiency. The feasibility of two proposed schemes is supported by implementation results. Our two schemes are the first in their respective classes of provably secure groups signature schemes. Additionally, the techniques introduced in this work might be of independent interest. These are a new verifiable encryption protocol for the randomized McEliece encryption and a novel approach to design formal security reductions from the Syndrome Decoding problem.
A New Replay Attack Against Automatic Speaker Verification Systems

With the increasing popularity of automatic speaker verification (ASV), the reliability of ASV systems has also gained importance.ASV is vulnerable to various spoofing attacks, especially replay attacks.Thus, recent public competitions and studies based on spoofing attack detection for ASV have mainly focused on the detection of replay attacks.Generally, replayed speech includes the attributes of one playback and two recording devices: the playback device, the recording device used by the attacker, and the recording device embedded in any system to verify input utterances.Therefore, the main attributes differentiating a replayed speech from the genuine speech are the attributes of the playback and the recording devices used by the attacker.In this paper, we propose a novel replay attack and its defense through observation of the general speech-spoofing process.The proposed attack includes only the attribute of one recording device embedded in an ASV system; genuine speech passes through the recording device only once, and the replayed speech produced for the proposed attack passes through the same recording device twice.Because the proposed attack is feasible, it can be considered a new task for replay countermeasures in the training process in order to develop a robust ASV protection system.The experimental results show that this novel replay attack cannot be detected by several of the existing state-of-the-art replay attack detection systems.Furthermore, the new attack can be detected by the same systems successfully if they are retrained with an appropriate dataset designed for the new task.
Spatial-spectral weighted nuclear norm minimization for hyperspectral image denoising

Hyperspectral images (HSIs) are inevitably corrupted by various kinds of noise due to the instrumental and environmental factors. This degradation of HSI data affects the subsequent applications of these images. Despite the extensive research conducted into HSI denoising, satisfactory results under heavy noise levels have not yet been obtained. Assuming that the latent clean HSI holds the low-rank (LR) property while the noisy component does not, we propose a two-step Spatial-Spectral Weighted Nuclear Norm Minimization (SSWNNM) algorithm for HSI Denoising. Considering the LR property across the spectra, a Weighted Nuclear Norm Minimization (WNNM) algorithm is conducted to recover the spectral LR structure. In the spatial domain, nonlocal similar cubic patches are found and stacked into an LR matrix, which contains the local detailed spatial texture information. We further utilize Multi-channel Weighted Nuclear Norm Minimization (MCWNNM) to recover this spatial LR matrix. Experiments conducted on simulated and real HSI data demonstrate that the proposed denoising method outperforms state-of-the-art methods, both in terms of visual quality and several quantitative assessment indices.
A Meta-learning approach for recommending the number of clusters for clustering algorithms

One of the main challenges in Clustering Analysis is choosing the optimal number of clusters. A typical methodology is to evaluate a validity index over the data and to optimize it as a function of the number of clusters. However, this process can have a high computational cost. In this work, we introduce a new approach for recommending the number of clusters for a particular dataset by using Meta-learning. As the predictive performance of the meta-models induced by Meta-learning is affected by how datasets are described by meta-features, we propose a new set of meta-features able to improve the predictive performance of meta-models used for recommending the number of clusters. Experimental results show that the proposed approach provides a good recommendation of the number of clusters. Additionally, the proposed meta-feature obtains better results than meta-features for clustering tasks found in the literature.
Transferable Linear Discriminant Analysis

Linear discriminant analysis (LDA) has been widely used as the technique of feature exaction. However, LDA may be invalid to address the data from different domains. The reasons are as follows: 1) the distribution discrepancy of data may disturb the linear transformation matrix so that it cannot extract the most discriminative feature and 2) the original design of LDA does not consider the unlabeled data so that the unlabeled data cannot take part in the training process for further improving the performance of LDA. To address these problems, in this brief, we propose a novel transferable LDA (TLDA) method to extend LDA into the scenario in which the data have different probability distributions. The whole learning process of TLDA is driven by the philosophy that the data from the same subspace have a low-rank structure. The matrix rank in TLDA is the key learning criterion to conduct local and global linear transformations for restoring the low-rank structure of data from different distributions and enlarging the distances among different subspaces. In doing so, the variations of distribution discrepancy within the same subspace can be reduced, i.e., data can be aligned well and the maximally separated structure can be achieved for the data from different subspaces. A simple projected subgradient-based method is proposed to optimize the objective of TLDA, and a strict theory proof is provided to guarantee a quick convergence. The experimental evaluation on public data sets demonstrates that our TLDA can achieve better classification performance and outperform the state-of-the-art methods.