Reviewerly/exHarmony huggingface dataset:
Enhanced exclusive-OR and quick response code-based image encryption through incoherent illumination

For practical application of any optical cryptographic technique, good quality decryption is necessary. Exclusive-OR (XOR) operation is a well-known technique for image encryption but faces the problem of quality degradation, while optically implemented. One of the reasons for quality degradation is the presence of laser speckles. The other reason is the noise that induces around image pixels. In this study, we demonstrate an image encryption using XOR operation based on light-emitting diode (LED) and quick response code, which not only reduces the damage caused by speckles but also overcomes the noise problem. The results with green and white LEDs and laser sources have been compared. Overall, the proposed optical cryptosystem is simple, compact, and cost-effective.
Asymptotic statistics for multilayer perceptron with ReLU hidden units

In numerous tasks, deep networks are state of the art. However, they are still not well understood from a statistical point of view. In this article, we try to contribute to filling this gap, and we consider regression models involving deep multilayer perceptrons (MLP) with rectified linear (ReLU) functions for activation units. It is a difficult task to study the statistical properties of such models. The main reason is that in practice these models may be heavily overparameterized. For the sake of simplicity, we focus here on the sum of square errors (SSE) cost function which is the standard cost function for regression purposes. In this framework, we study the asymptotic behavior of the difference between the SSE of estimated models and the SSE of the theoretical best model. This behavior gives us information on the overfitting properties of such models. We use in this paper new methodology introduced to deal with models with a loss of identifiability, i.e. in the case that the true parameter cannot be identified uniquely. Hence, we don’t have to assume that a unique parameter vector realizes the best regression function which seems to be a too strong assumption for heavily overparameterized models. Our results shed new light on the overfitting behavior of MLP models.
Antibunching effect of photons in a two-level emitter-cavity system

A two-level quantum emitter interacting with a cavity field is an important model for investigating the photon blockade (PB) effect. Most work on this topic relies on the assumption that the emitter transition frequency is resonant with the fundamental mode frequency of the cavity. In this work we relax this constraint and reexamine the PB effect in a more general emitter-cavity system with arbitrary emitter and cavity detunings from a driving field. The results show that when the signs of the emitter and cavity detunings are the same, a PB occurs only in the strong-coupling regime, but for opposite signs of the emitter and cavity detunings, strong photon antibunching is observed in both the weak- and strong-coupling regimes, and better antibunching is achieved compared with the case when the signs are the same. More interestingly, we find that this PB arises from quantum interference for both weak and strong nonlinearities. These results deepen our understanding of the underlying mechanism of PBs and may contribute to the construction of single-photon sources with higher purity and better flexibility using two-level emitter-cavity systems.
Pronouns in English and Arabic: A Contrastive Study

Pronouns are defined as being one of the parts of speech that obtain their meaning from other noun phrases in a sentence. This study is descriptive, analytic, and contrastive. It deals with comparing pronouns in English and Arabic by concentrating on the points of difference and similarity between the two languages.&#x0D; &#x0D; This study aims at describing pronouns in both languages and then the comparison is made by defining pronouns, showing their syntactic, semantic, and pragmatic aspects to know to what extent both languages are similar or different in using pronouns.&#x0D; &#x0D; It is hypothesized that the two languages are similar to each other in terms of their syntactic, semantic, and pragmatic aspects of pronouns; pronouns are found in both languages; and there are some points of similarity and difference between the two languages, but the differences outweigh the similarities. &#x0D; &#x0D; English has a clear division of pronouns. Pronouns are dealt with syntactically, semantically and pragmatically. In Arabic the division of pronouns is completely different.
A Deep Learning Approach for Breast Cancer Mass Detection

Breast cancer is the most widespread type of cancer among women.The diagnosis of breast cancer in its early stages is still a significant problem worldwide.The accurate classification and localization of breast mass help in the early detection of the disease, so in the last few years, a variety of CAD systems are developed to enhance breast cancer classification and localization accuracy, but most of them are fully based on handcrafted feature extraction techniques, which affect its efficiency.Currently, deep learning approaches are able to automatically learn a set of high-level features and consequently, they are achieving remarkable results in object classification and detection tasks.In this paper, the pre-trained ResNet-50 architecture and the Class Activation Map (CAM) technique are employed in breast cancer classification and localization respectively.CAM technique exploits the Convolutional Neural Network (CNN) classifiers with Global Average Pooling (GAP) layer for object localization without any supervised information about its location.According to the experimental results, the proposed approach achieved 96% Area under the Receiver Operating Characteristics (ROC) curve in the classification with 99.8% sensitivity and 82.1% specificity.Furthermore, it is able to localize 93.67% of the masses at an average of 0.122 false positives per image on the Digital Database for Screening Mammography (DDSM) data-set.It is worth noting that the pretrained CNN is able automatically to learn the most discriminative features in the mammogram, and then fulfills superior results in breast cancer classification (normal or mass).Additionally, CAM exhibits the concrete relation between the mass located in the mammogram and the discriminative features learned by the CNN.
Connecting Content and Logical Words

Abstract Content words (e.g. nouns and adjectives) are generally connected: there are no gaps in their denotations; no noun means ‘table or shoe’ or ‘animal or house’. We explore a formulation of connectedness which is applicable to content and logical words alike, and which compares well with the classic notion of monotonicity for quantifiers. On a first inspection, logical words satisfy this generalized version of the connectedness property at least as well as content words do — that is, both in terms of what may be observed in the lexicons of natural languages (although our investigations remain modest in that respect) and in terms of acquisition biases (with an artificial rule learning experiment). This reduces the putative differences between content and logical words, as well as the associated challenges that these differences would pose, e.g., for learners.
ABNE: An Attention-Based Network Embedding for User Alignment Across Social Networks

User alignment across social networks can facilitate more information/knowledge transferring across networks and thereby benefit several applications, including social link prediction, cross-domain recommendation, and information diffusion.Several works try to learn a common subspace for networks by preserving the structural proximities, such that different contribution weights of neighbors are ignored as users were always connected by unweighted edges.In this paper, we propose an attention-based network embedding model that exploits the social structures for user alignment.In particular, two main components are contained in our model framework: a masked graph attention mechanism which tries to learn the alignment task driven attention weights by the supervision of pre-aligned user pairs, and an embedding algorithm tries to learn a common vector space by explicitly modeling the weighted contribution probabilities between follower-ships and followee-ships.With the learned weights and embeddings transferring between these two components, we construct a unified model for user embedding and alignment.Stochastic gradient descent and negative sampling are adopted for efficient learning and scalability.The extensive experiments on real-world social network data sets demonstrate the effectiveness and robustness of the proposed model compared with several state-of-the-art methods.
Adaptive deep metric embeddings for person re-identification under occlusions

Person re-identification (ReID) under occlusions is a challenging problem in video surveillance. Most of existing person ReID methods take advantage of local features to deal with occlusions. However, these methods usually independently extract features from the local regions of an image without considering the relationship among different local regions. In this paper, we propose a novel person ReID method, which extracts the discriminative feature representation of the pedestrian image based on Long Short-Term Memory (LSTM), dealing with the problem of occlusions. In particular, the multi-directional spatial encoded local features are developed to learn the spatial dependencies between the local regions by taking advantage of LSTM. Furthermore, we propose a novel loss (termed the adaptive nearest neighbor loss) based on the classification uncertainty to effectively reduce intra-class variations while enlarging inter-class differences within the adaptive neighborhood of the sample. The proposed loss enables the deep neural network to adaptively learn discriminative metric embeddings, which significantly improve the generalization capability of recognizing unseen person identities. Extensive comparative evaluations on challenging person ReID datasets demonstrate the significantly improved performance of the proposed method compared with several state-of-the-art methods.
Deep Differential Convolutional Network for Single Image Super-Resolution

The deep convolutional neural networks and residual networks have shown great success and high-quality reconstruction for single image super-resolution. It is clearly seen that among the best-known super-resolution models, deep learning-based methods demonstrate state-of-the-art performance. In this paper, we propose a deep differential convolutional network (DCN) for single image super-resolution (SRDCN). The proposed DCN is a novel convolutional network, which is composed of convolutional layers, parametric rectified linear units (PReLU), and the identity skip connection. Different from other deep learning-based methods which complete the reconstruction by learning the mapping function between low-resolution and high-resolution images, the proposed algorithm makes changes to the way of reconstruction. In the proposed network, we use DCN to obtain the reconstructed images and the differences between the low-resolution and reconstructed images in the reconstruction process. Then the differences combined with the original low-resolution image and the reconstructed image that from the last DCN are used for final reconstruction. In addition, the loss function is more rationally designed and optimized in this paper. The proposed loss function contains three parts of loss: feature loss, style loss, and mean squared error (MSE) loss. These losses will be used to supervise the structure and content of the reconstructed image. The experimental results prove that the proposed model is superior to many state-of-the-art super-resolution methods in terms of both peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM).
Hierarchical Bit-Level Image Encryption Based on Chaotic Map and Feistel Network

In this paper, to avoid chosen-plaintext attacks and to enhance the security, SHA-256 hash value of the plain image is introduced to produce the initial values and parameters of the chaotic system, thus the produced keys and the ciphered image are highly sensitive to changes of the plain image. Based on the plain image related chaotic sequences, a hierarchical bit-level image encryption scheme is proposed by using an “Incomplete” Feistel network. In this scheme, the left four most significant bits (MSB) and the right four least significant bits (LSB) of each pixel are encrypted differently by the “Incomplete” Feistel network. During the encryption process, different bitplanes of gray image and color image are encrypted differently by the keystreams generated from the Logistic-Sine system (LSS) and the Lorenz system. The theoretical analysis and experiment results show that the proposed encryption algorithm is effective and secure enough to be used for image encryption.
Deep Reinforcement Learning for Target Searching in Cognitive Electronic Warfare

The recent appreciation of deep reinforcement learning (DRL) arises from its successes in many domains, but the applications of DRL in practical engineering are still unsatisfactory, including optimizing control strategies in cognitive electronic warfare (CEW). CEW is a massive and challenging project, and due to the sensitivity of the data sources, there are few open studies that have investigated CEW. Moreover, the spatial sparsity, continuous action, and partially observable environment that exist in CEW have greatly limited the abilities of DRL algorithms, which strongly depend on state-value and action-value functions. In this paper, we use Python to build a 3-D space game named Explorer to simulate various CEW environments in which the electronic attacker is an unmanned combat air vehicle (UCAV) and the defender is an observation station, both of which are equipped with radar as the observation sensor. In our game, the UCAV needs to accomplish the task of detecting the target as early as possible to perform follow-up tracking and guidance tasks. To allow an "infant" UCAV to understand what "target searching" is, we train the UCAV's maneuvering strategies by means of a well-designed reward shaping, a simplified constant accelerated motion control, and a deep deterministic policy gradient (DDPG) algorithm based on a generative model and variational Bayesian estimation. The experimental results show that when the operating cycle is 0.2 s, the search success rate of the trained UCAV in 10000 episodes is improved by 33.36% compared with the benchmark, and the target destruction rate is similarly improved by 57.84%.
Secure Peer-to-Peer Communication Based on Blockchain

Nowadays, electronic messaging is the most used network application, and the authentication between users is a vital property. The most commonly employed approaches to ensure this property are PKI and S/MIME email encryption protocols, but indeed they are facing multiples security threats, such as the MITM attack and EFAIL attack. The blockchain is an innovative technology that overcomes these threats and allows to decentralize sensitive operations while preserving a high level of security. It eliminates the need for trusted intermediaries. The blockchain is accessible to all network nodes and keeps track of all transactions already made. The goal of our work is to propose a secure messaging solution based on the blockchain technology. In this paper, we explain why blockchain would make communications more secure, and we propose a model design for blockchain-based messaging maintaining the performance and security of data recorded on the blockchain, using a smart contract to verify the identities and their associated public keys, and validate the user’s certificate. The system is entirely decentralized and allows users to exchange messages securely.
Layer-based Privacy and Security Architecture for Cloud Data Sharing

The management of data while maintaining its utility and preservation of security scheme is a matter of concern for the cloud owner. In order to minimize the overhead at cloud service provider of applying security over each document and then send it to the client, we propose a layered architecture. This approach maintains security of the sensitive document and privacy of its data sensitivity. To make a balance between data security and utility, the proposed approach categorizes the data according to its sensitivity. Perseverance of various categorization requires different algorithmic schemes. We set up a cloud distributed environment where data is categorized into four levels of sensitivity; public, confidential, secret, top secret and a different approach has been used to preserve the security at each level. At the most sensitive layers i.e. secret and top secret data, we made a provision to detect the faulty node that is responsible for data leakage. Finally, experimental analysis is carried out to analyze the performance of the layered approach. The experimental results show that time taken (in ms) in processing 200 documents of size 20 MB is 437, 2239, 3142, 3900 for public, confidential, secret and top secret data respectively when the documents are distributed among distinct users, which proves the practicality of the proposed approach.
Privacy-preserving data search with fine-grained dynamic search right management in fog-assisted Internet of Things

Fog computing, as an assisted method for cloud computing, collects Internet of Things (IoT) data to multiple fog nodes on the edge of IoT and outsources them to the cloud for data search, and it reduces the computation cost on IoT nodes and provides fine-grained search right management. However, to provide privacy-preserving IoT data search, the existing searchable encryptions are very inefficient as the computation cost is too high for the resource-constrained IoT ends. Moreover, to provide dynamic search right management, the users need to be online all the time in the existing schemes, which is impractical. In this paper, we first present a new fog-assisted privacy-preserving IoT data search framework, where the data from each IoT device is collected by a fog node, stored in a determined document and outsourced to the cloud, the users search the data through the fog nodes, and the fine-grained search right management is maintained at document level. Under this framework, two searchable encryption schemes are proposed, i.e., Credible Fog Nodes assisted Searchable Encryption (CFN-SE) and Semi-trusted Fog Nodes assisted Searchable Encryption (STFN-SE). In CFN-SE scheme, the indexes and trapdoors are generated by the fog nodes, which greatly reduce the computation costs at the IoT devices and user ends, and fog nodes are used to support offline users’ key update. In STFN-SE scheme, the semi-trusted fog nodes are used to provide storage of encrypted key update information to assist offline users’ search right update. In both schemes, no re-encryption of the keywords is needed in search right updates. The performance evaluations of our schemes demonstrate the feasibility and high efficiency of our system.
Effects of knowledge management practices on innovation in SMEs

The management of knowledge assets is crucial for gaining competitive advantage and has a huge strategic importance for the firms.Knowledge management has become one of the emerging fields in today's research world and has turned out to be a major concern for the organizations as it plays a crucial role in the growth and development of the organization.Knowledge management is a new concept that is why it is gaining increased attention among small and large organizations.In this study three important knowledge management practices are discussed and the necessary insights regarding knowledge management processes and their positive impacts within an organization are provided.The study also brings forth the relationships which knowledge management processes have with radical innovation in small and medium enterprises.The explanatory method of research and quantitative type of research to test the hypothesis of research was used to carry out the study and survey type was involved by using questionnaire.The knowledge management and innovation instruments were adapted from previous researchers.This study's target population consisted of small and medium-sized enterprises that included service sectors in Quetta, Balochistan.A convenient sampling was applied to collect the necessary data from SMEs.A total of 850 firms were communicated and requested to participate in this survey but 300 (35.6% response rate) accepted to fill out the survey questionnaire.The study utilized structural equation modelling to examine numerous complex cause and effect relationships between variables.The results indicate a positive association between Knowledge management processes and radical innovation.The positive link between Knowledge management processes and innovation indicates the importance and value of knowledge management in achieving competitive advantage through innovation.
Towards a Performance Evaluation of Private Blockchain Frameworks using a Realistic Workload

The blockchain is currently under the spotlight of trending technologies. It adds security to private applications in several areas of knowledge, and its versatility results in the development of multiple frameworks to meet the requirements of each application. Thus, it is a key challenge to ensure that blockchain frameworks provide security, access control, and high performance to applications. In this paper, we evaluate two frameworks for blockchain development, Parity, and Multichain, which provide configuration and permission flexibility. Our evaluation is a comprehensive comparison between the frameworks, focusing on the analysis of transaction-validation time, transaction-mining time, transaction-seek time and block-seek time. To this end, we deploy peer-to-peer private permissioned networks, in which the frameworks generate the blockchain applications. For each framework, we provide a realistic workload, based on the distribution of probability of interarrival time of transactions on the Bitcoin network. The results show that each framework stands out under specific criteria, and their design decisions imply restrictions on features that are critical for creating secure and efficient blockchain applications.
Improved Parallel Resampling Methods for Particle Filtering

Particle filter techniques are common methods used to estimate the evolving state of nonlinear, non-Gaussian time-variant systems by utilizing a periodic sequence of noisy measurements.The accuracy of particle filter methods has often been shown to be superior to other state estimation techniques, such as the extended Kalman filter (EKF), for many applications.Unfortunately, the high computational cost and highly nondeterministic runtime behavior of particle filters often preclude their use in hard, real-time environments, where filter response must meet the strict timing requirements of the application.Particle filter algorithms are composed of three main stages: prediction, update, and resampling.General purpose graphics processing units (GPGPUs) have been successfully employed in previous research to accelerate the computation of both the prediction and update stages by exploiting their natural fine-grain parallelism.This research focuses on accelerating the resampling stage for GPGPU execution, which has been much more difficult to parallelize due to it's apparent inherent sequentially.This paper introduces a novel GPGPU implementation of the systematic and stratified resampling algorithms that exploit the monotonically increasing nature of the prefix-sum and the evolutionary nature of the particle weighting process to allow the re-indexing portion of the algorithms to occur in a two-phase, multi-threaded manner.This resulting measured factor of performance improvement for the systematic and stratified algorithms was 15x and 32x, respectively, over the serial implementations.