Reviewerly/exHarmony huggingface dataset:
A Fast Nearest Neighbor Search Scheme Over Outsourced Encrypted Medical Images

Medical imaging is crucial for medical diagnosis, and the sensitive nature of medical images necessitates rigorous security and privacy solutions to be in place. In a cloud-based medical system for Healthcare Industry 4.0, medical images should be encrypted prior to being outsourced. However, processing queries over encrypted data without first executing the decryption operation is challenging and impractical at present. In this paper, we propose a secure and efficient scheme to find the exact nearest neighbor over encrypted medical images. Instead of calculating the Euclidean distance, we reject candidates by computing the lower bound of the Euclidean distance that is related to the mean and standard deviation of data. Unlike most existing schemes, our scheme can obtain the exact nearest neighbor rather than an approximate result. We, then, evaluate our proposed approach to demonstrate its utility.
Efficient generation of the triplet Bell state between coupled spins using transitionless quantum driving and optimal control

We consider a pair of coupled spins with Ising interaction in the $z$ direction and study the problem of generating efficiently the triplet Bell state. We initially analyze the transitionless quantum driving shortcut to adiabaticity method and point out its limitations when the available duration approaches zero. In this short time limit we explicitly calculate the fidelity of the method and find it to be much lower than unity, no matter how large the available control fields become. We find that there is a lower bound on the necessary time to complete this transfer, set by the finite value of the interaction between the spins. We then use numerical optimal control to find bang-bang pulse sequences, as well as, smooth controls, which can generate high levels of the target Bell state in the minimum possible time. The results of the present work are not restricted only to spin systems, but are expected also to find applications in other physical systems which can be modeled as interacting spins, for example, coupled quantum dots.
AI-Sketcher : A Deep Generative Model for Producing High-Quality Sketches

Sketch drawings play an important role in assisting humans in communication and creative design since ancient period. This situation has motivated the development of artificial intelligence (AI) techniques for automatically generating sketches based on user input. Sketch-RNN, a sequence-to-sequence variational autoencoder (VAE) model, was developed for this purpose and known as a state-of-the-art technique. However, it suffers from limitations, including the generation of lowquality results and its incapability to support multi-class generations. To address these issues, we introduced AI-Sketcher, a deep generative model for generating high-quality multiclass sketches. Our model improves drawing quality by employing a CNN-based autoencoder to capture the positional information of each stroke at the pixel level. It also introduces an influence layer to more precisely guide the generation of each stroke by directly referring to the training data. To support multi-class sketch generation, we provided a conditional vector that can help differentiate sketches under various classes. The proposed technique was evaluated based on two large-scale sketch datasets, and results demonstrated its power in generating high-quality sketches.
Computer-aided detection of mass in digital breast tomosynthesis using a faster region-based convolutional neural network

Digital breast tomosynthesis (DBT) is a newly developed three-dimensional tomographic imaging modality in the field of breast cancer screening designed to alleviate the limitations of conventional digital mammography-based breast screening methods. A computer-aided detection (CAD) system was designed for masses in DBT using a faster region-based convolutional neural network (faster-RCNN). To this end, a data set was collected, including 89 patients with 105 masses. An efficient detection architecture of convolution neural network with a region proposal network (RPN) was used for each slice to generate region proposals (i.e., bounding boxes) with a mass likelihood score. In each DBT volume, a slice fusion procedure was used to merge the detection results on consecutive 2D slices into one 3D DBT volume. The performance of the CAD system was evaluated using free-response receiver operating characteristic (FROC) curves. Our RCNN-based CAD system was compared with a deep convolutional neural network (DCNN)-based CAD system. The RCNN-based CAD generated a performance with an area under the ROC (AUC) of 0.96, whereas the DCNN-based CAD achieved a performance with AUC of 0.92. For lesion-based mass detection, the sensitivity of RCNN-based CAD was 90% at 1.54 false positive (FP) per volume, whereas the sensitivity of DCNN-based CAD was 90% at 2.81 FPs/volume. For breast-based mass detection, RCNN-based CAD generated a sensitivity of 90% at 0.76 FP/breast, which is significantly increased compared with the DCNN-based CAD with a sensitivity of 90% at 2.25 FPs/breast. The results suggest that the faster R-CNN has the potential to augment the prescreening and FP reduction in the CAD system for masses.
Robust Copy–Move Detection of Speech Recording Using Similarities of Pitch and Formant

Copy-move forgery on very short speech segments, followed by post-processing operations to eliminate traces of the forgery, presents a great challenge to forensic detection. In this paper, we propose a robust method for detecting and locating a speech copy-move forgery. We found that pitch and formant can be used as the features representing a voiced speech segment, and these two features are very robust against commonly used post-processing operations. In the proposed algorithm, we first divide the speech recording into voiced speech segments and unvoiced speech segments. We then extract the pitch sequence and the first two formant sequences as the feature set of each voiced speech segment. Dynamic time warping is applied to compute the similarities of each feature set. By comparing the similarities with a threshold, we can detect and locate copy-move forgeries in speech recording. The extensive experiments show that the proposed method is very effective in detecting and locating copy-move forgeries, even on a forged speech segment as short as one voiced speech segment. The proposed method is also robust against several kinds of commonly used post-processing operations and background noise, which highlights the promising potential of the proposed method as a speech copy-move forgery localization tool in practical forensics applications.
Structure preservation and distribution alignment in discriminative transfer subspace learning

Domain adaptation (DA) is one of the most promising techniques for leveraging existing knowledge from a source domain and applying it to a related target domain. Most DA methods mainly focus on learning a common subspace for the two domains by exploiting either the statistical property or the geometric structure independently to reduce the domain distribution difference. However, these two properties are complementary to each other, and jointly exploring them could yield optimal results. Inspired by the theoretical results of DA, in this paper, we propose structure preservation and distribution alignment (SPDA) in discriminative transfer subspace learning, which embeds the source domain classification error and reduction and domain distribution alignment into a single framework for optimization. SPDA learns an appropriate projection matrix, by which (1) the source domain classification error can be reduced; and (2) the source and target domain data are projected into a common subspace, where the domain distributions are well aligned and each target datum can be linearly reconstructed using the data from the source domain. To reduce the source domain classification error, an ε-dragging technique that relaxes the strict binary label matrix is introduced to enlarge the distance between two data points from different classes. Further, the global subspace structure and the local geometric structure are preserved by imposing a low-rank constraint and a sparse constraint, respectively, on the reconstruction coefficient matrix. Moreover, the space relationship of the samples is preserved using a graph regularization method. In addition, marginal and conditional distributions between the domains are minimized to further reduce the domain shift statistically. We formulate source domain classifier design, geometric structure preservation, and distribution alignment as a rank-minimization problem, and we design an effective optimization algorithm based on the alternating direction method of multipliers (ADMM) to solve this problem. The functions and roles of each term in this framework are analyzed. The results of extensive experiments conducted on five datasets show that SPDA outperforms several state-of-the-art approaches and exhibits classification performance comparable with that of modern deep DA methods.
A Layer-Based Sequential Framework for Scene Generation with GANs

The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities. Therefore, it is a very challenging task to generate vivid scenes of similar complexity using computers. In this work, we present a scene generation framework based on Generative Adversarial Networks (GANs) to sequentially compose a scene, breaking down the underlying problem into smaller ones. Different than the existing approaches, our framework offers an explicit control over the elements of a scene through separate background and foreground generators. Starting with an initially generated background, foreground objects then populate the scene one-by-one in a sequential manner. Via quantitative and qualitative experiments on a subset of the MS-COCO dataset, we show that our proposed framework produces not only more diverse images but also copes better with affine transformations and occlusion artifacts of foreground objects than its counterparts.
Exploring Turkish Pre-Service Teachers’ Perceptions and Views of Digital Literacy

This study aims to investigate digital literacy perceptions of preservice teachers, and to identify quantity characteristics of first and second year preservice teachers’ digital literacy at a public university in Turkey. That is, pre-service teachers’ perceptions of digital literacy and their patterns of knowledge and use of digital literacy were measured within the scope of this study. Multiple sources of data, including a Likert scale and open-ended questions were collected to address the topic. Various statistical techniques and tests such as ANOVA, t-test, and Tukey HSD test were used in the analysis of the obtained data. Qualitative data was analyzed by using the content analysis method. The findings indicated that the model showed a good fit with the data and standardized regression weights indicated that attitude, technical, cognitive, and social factors were significant predictors of digital literacy. In addition, it was identified that the pre-service teachers had high and positive perceptions of digital literacy. However, it was observed in the open-ended questions that they lack the refined cognitive skills to find, evaluate, create, and communicate. The findings were then discussed in light of the relevant literature.
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification

The use of background knowledge is largely unexploited in text classification tasks. This paper explores word taxonomies as means for constructing new semantic features, which may improve the performance and robustness of the learned classifiers. We propose tax2vec, a parallel algorithm for constructing taxonomy-based features, and demonstrate its use on six short text classification problems: prediction of gender, personality type, age, news topics, drug side effects and drug effectiveness. The constructed semantic features, in combination with fast linear classifiers, tested against strong baselines such as hierarchical attention neural networks, achieves comparable classification results on short text documents. The algorithm’s performance is also tested in a few-shot learning setting, indicating that the inclusion of semantic features can improve the performance in data-scarce situations. The tax2vec capability to extract corpus-specific semantic keywords is also demonstrated. Finally, we investigate the semantic space of potential features, where we observe a similarity with the well known Zipf’s law.
Eye gaze pattern analysis for fatigue detection based on GP-BCNN with ESM

This paper presents a robust fatigue detection system based on binocular consistency, which integrates artificial modulation into deep learning to guide the learning process and removes the extreme cases of dynamic objects through screening mechanism. Specifically, we first build a dual-stream bidirectional convolutional neural network (BCNN) for eye gaze pattern detection, which uses binocular consistency for information interaction. Then we incorporate vectorized local integral projection features which named projection vectors and Gabor filters into BCNN to construct GP-BCNN that not only enhances the resistance of deep learned features to the orientation and scale changes, but strengthens the learning of texture information. Finally, an eye screening mechanism (ESM) based on pupil distance is proposed to eliminate the detected errors caused by the occluded eyes when the lateral face is detected. Demonstrated by introducing binocular consistency and artificial modulation to convolutional neural network (CNN), GP-BCNN improves the widely used CNNs architectures and yields a 2.9% promotion in the average accuracy rate compared with the results obtained by CNN alone. Our approach obtains the state-of-the-art results in fatigue detection and has the generalization potential in general image recognition tasks.
An Efficient Certificateless Aggregate Signature Scheme Without Pairings for Healthcare Wireless Sensor Network

Wireless sensor network (WSN) can be widely used in the field of health care since it has many advantages, such as low cost, high efficiency, low latency, and so on. In healthcare wireless sensor network (HWSN), the patients' health information needs to be transmitted to health professionals in real time through the Internet, so it is vital to ensure the integrity and privacy of medical information. Aggregate signature (AS) cannot only provide message integrity and authentication for multiple users but also drastically improve the efficiency of signature transmission and verification. Certificateless public key cryptography (CLPKC) is superior to traditional public key infrastructure and identity-based cryptography. We combine the advantages of CLPKC and AS and construct a certificateless aggregate signature (CLAS) scheme without using pairings. Our CLAS scheme realizes the protection of the privacy and integrity of healthcare information for multiple patients in HWSN for the first time. Under the assumption of elliptic curve discrete logarithm, the proposed CLAS scheme is existentially unforgettable against two types of adversaries. In addition, the scheme has better performance than other CLAS schemes as for the security, communication overhead, and computational costs. So it is more suitable for green HWSN.
Resident Evil: Understanding Residential IP Proxy as a Dark Service

An emerging Internet business is residential proxy (RESIP) as a service, in which a provider utilizes the hosts within residential networks (in contrast to those running in a datacenter) to relay their customers' traffic, in an attempt to avoid server- side blocking and detection. With the prominent roles the services could play in the underground business world, little has been done to understand whether they are indeed involved in Cybercrimes and how they operate, due to the challenges in identifying their RESIPs, not to mention any in-depth analysis on them. In this paper, we report the first study on RESIPs, which sheds light on the behaviors and the ecosystem of these elusive gray services. Our research employed an infiltration framework, including our clients for RESIP services and the servers they visited, to detect 6 million RESIP IPs across 230+ countries and 52K+ ISPs. The observed addresses were analyzed and the hosts behind them were further fingerprinted using a new profiling system. Our effort led to several surprising findings about the RESIP services unknown before. Surprisingly, despite the providers' claim that the proxy hosts are willingly joined, many proxies run on likely compromised hosts including IoT devices. Through cross-matching the hosts we discovered and labeled PUP (potentially unwanted programs) logs provided by a leading IT company, we uncovered various illicit operations RESIP hosts performed, including illegal promotion, Fast fluxing, phishing, malware hosting, and others. We also reverse engi- neered RESIP services' internal infrastructures, uncovered their potential rebranding and reselling behaviors. Our research takes the first step toward understanding this new Internet service, contributing to the effective control of their security risks.
Financial and economic development link in transitional economies: a spectral Granger causality analysis 1991?2017

Research background: The relationship between financial development and economic growth has been attracting attention in the field of economics since the times of the ?great moderation?. Previous empirical studies still fail to put forward a general conclusion on whether and how financial development affects economic growth. This is particularly true due to the lack of empirical research on the matter in question for countries in transition.&#x0D; Purpose of the article: This study aims to contribute to bridging the gap in the financial development-growth nexus in transitional economies. Understanding the mechanism behind financial development and economic growth should assist policymakers in the design of efficient economic policies or avoiding/alleviating financial cycles.&#x0D; Methods: Using Granger causality test in frequency domain, which shows to have more power over standard time domain Granger causality test, as well as gross domestic product (GDP) and the monetary base (M2 ? intermediate money), we investigated the finance-growth relationship in 19 Central, East, and Southeast European countries (CESEE) from 1991 to 2017.&#x0D; Findings &amp; Value added: Study results show that financial development is important for growth in CESEE countries, thus supporting the ?supply-leading? theories in general for countries in the sample. Our findings indicate that the relationship between financial development and economic growth exists in CESEE countries (with one exception ? the Czech Republic) ranging from unidirectional (Albania, Bosnia and Hercegovina, Belarus, Estonia, Macedonia, Russia, Turkey), to bi-directional spectral Granger causality (Bulgaria, Croatia, Hungary, Kazakhstan, Latvia, Lithuania, Poland, Romania, Slovenia, Slovakia, Ukraine).
Time‐critical data management in clouds: Challenges and a Dynamic Real‐Time Infrastructure Planner (DRIP) solution

Summary The increasing volume of data being produced, curated, and made available by research infrastructures in the environmental science domain require services that are able to optimize the delivery and staging of data for researchers and other users of scientific data. Specialized data services for managing data life cycle, for creating and delivering data products, and for customized data processing and analysis all play a crucial role in how these research infrastructures serve their communities, and many of these activities are time‐critical—needing to be carried out frequently within specific time windows. We describe our experiences identifying the time‐critical requirements of environmental scientists making use of computational research support environments. We present a microservice‐based infrastructure optimization suite, the Dynamic Real‐Time Infrastructure Planner, used for constructing virtual infrastructures for research applications on demand. We provide a case study whereby our suite is used to optimize runtime service quality for a data subscription service provided by the Euro‐Argo using EGI Federated Cloud and EUDAT's B2SAFE services, and to consider how such a case study relates to other application scenarios.
An anonymous entropy-based location privacy protection scheme in mobile social networks

The popularization of mobile communication devices and location technology has spurred the increasing demand for location-based services (LBSs). While enjoying the convenience provided by LBS, users may be confronted with the risk of privacy leakage. It is very crucial to devise a secure scheme to protect the location privacy of users. In this paper, we propose an anonymous entropy-based location privacy protection scheme in mobile social networks (MSN), which includes two algorithms K-DDCA in a densely populated region and K-SDCA in a sparsely populated region to tackle the problem of location privacy leakage. The K-DDCA algorithm employs anonymous entropy method to select user groups and construct anonymous regions which can guarantee the area of the anonymous region formed be moderate and the diversity of the request content. The K-SDCA algorithm generates a set of similar dummy locations which can resist the attack of adversaries with background information. Particularly, we present the anonymous entropy method based on the location distance and request contents. The effectiveness of our scheme is validated through extensive simulations, which show that our scheme can achieve enhanced privacy preservation and better efficiency.
Quantum Image Steganography Protocol Based on Quantum Image Expansion and Grover Search Algorithm

Quantum image steganography is one of the important research branches of quantum secure communications.In this paper, a large payload quantum image steganography protocol based on quantum image expansion and the Grover search algorithm is proposed.The new algorithm adopts quantum log-polar image (QUALPI) representation to prepare the quantum image before introducing a quantum expansion technique to form the superposition of multiple image copies with the same size angle difference as the carrier.Then, it embeds a secret message into one quantum image copy with a specific rotation angle encoded.In order to accurately extract the secret message embedded, the Grover search algorithm is used to locate the correct quantum image copy.Based on the quantum uncertainty and quantum non-cloning theorems, the new algorithm can not only achieve good imperceptibility and security but also large payload due to the algorithm's good coding scalability.The experimental results by performing MATLAB simulation prove the conclusions.