Reviewerly/exHarmony huggingface dataset:
Pushing the Limits of Valiant’s Universal Circuits: Simpler, Tighter and More Compact

A universal circuit (UC) is a general-purpose circuit that can simulate arbitrary circuits (up to a certain size n). Valiant provides a k-way recursive construction of UCs (STOC 1976), where k tunes the complexity of the recursion. More concretely, Valiant gives theoretical constructions of 2-way and 4-way UCs of asymptotic (multiplicative) sizes $$5n\log n$$ and $$4.75 n\log n$$ respectively, which matches the asymptotic lower bound $$\varOmega (n\log n)$$ up to some constant factor. Motivated by various privacy-preserving cryptographic applications, Kiss et al. (Eurocrypt 2016) validated the practicality of 2-way universal circuits by giving example implementations for private function evaluation. Günther et al. (Asiacrypt 2017) and Alhassan et al. (J. Cryptology 2020) implemented the 2-way/4-way hybrid UCs with various optimizations in place towards making universal circuits more practical. Zhao et al. (Asiacrypt 2019) optimized Valiant's 4-way UC to asymptotic size $$4.5 n\log n$$ and proved a lower bound $$3.64 n\log n$$ for UCs under Valiant's framework. As the scale of computation goes beyond 10-million-gate ( $$n=10^7$$ ) or even billion-gate level ( $$n=10^9$$ ), the constant factor in UC's size plays an increasingly important role in application performance. In this work, we investigate Valiant's universal circuits and present an improved framework for constructing universal circuits with the following advantages. We implement the 2-way universal circuit and evaluate its performance with other implementations, which confirms our theoretical analysis.
3D printing, policing and crime

This article examines the implications of advanced manufacturing technology, more commonly known as three dimensional (3D) printing, for policing and crime, notably the dissemination of digital design files and the use of 3D printers to produce illicit firearms.The application and rapid evolution of 3D printing technology has created new challenges for law and regulation, and represents an interesting security paradox, albeit one which until now has received scant attention in the criminological or policing literature.On the one hand, 3D printing denotes a significant shift in the creation and use of objects, ranging from food to body parts, and more controversially, weaponry (Fleschner McMullen 2014).On the other, the use of this technology to create items such as firearms and weapons signifies a potential safety, security, and legal challenge (Blackman 2013).We explore the emergence of 3D printing and its use to create firearms along with the theoretical challenges to legal design and enforcement presented by this decentralised technology.We also present some empirical data on instances of 3D printed firearms and firearm parts being detected internationally, and some jurisdictions' legal and policy responses.We conclude by considering that any regulation of 3D printed firearms must be based on a robust evidence base and take proper account of citizens' rights, but also that any national regulation will be in tension with the transnational and decentralised nature of the technology.
Sketching for Motzkin’s Iterative Method for Linear Systems

Projection-based iterative methods for solving large over-determined linear systems are well-known for their simplicity and computational efficiency. It is also known that the correct choice of a sketching procedure (i.e., preprocessing steps that reduce the dimension of each iteration) can improve the performance of iterative methods in multiple ways, such as, to speed up the convergence of the method by fighting inner correlations of the system, or to reduce the variance incurred by the presence of noise. In the current work, we show that sketching can also help us to get better theoretical guarantees for the projection-based methods. Specifically, we use good properties of Gaussian sketching to prove an accelerated convergence rate of the sketched relaxation (also known as Motzkin's) method. The new estimates hold for linear systems of arbitrary structure. We also provide numerical experiments in support of our theoretical analysis of the sketched relaxation method.
Robustness of Word and Character N-gram Combinations in Detecting Deceptive and Truthful Opinions

Opinions in reviews about the quality of products or services can be important information for readers. Unfortunately, such opinions may include deceptive ones posted for some business reasons. To keep the opinions as a valuable and trusted source of information, we propose an approach to detecting deceptive and truthful opinions. Specifically, we explore the use of word and character n-gram combinations, function words, and word syntactic n-grams (word sn-grams) as features for classifiers to deal with this task. We also consider applying word correction to our utilized dataset. Our experiments show that classification results of using the word and character n-gram combination features could perform better than those of employing other features. Although the experiments indicate that applying the word correction might be insignificant, we note that the deceptive opinions tend to have a smaller number of error words than the truthful ones. To examine robustness of our features, we then perform cross-classification tests. Our latter experiments results suggest that using the word and character n-gram combination features could work well in detecting deceptive and truthful opinions. Interestingly, the latter experimental results also indicate that using the word sn-grams as combination features could give good performance.
UAV Autonomous Trajectory Planning in Target Tracking Tasks via a DQN Approach

For unmanned aerial vehicles (UAVs) to find safe and valid paths in a complex environment is a challenging and complicated work. However, many existing approaches of that are based on the specific scenarios. When the scenario is unknown, the algorithm would be noneffective. To improve the efficiency of the path planning method, the DQN approach is utilized in the target tracking tasks. The issue is modeled as follows: the UAV can be seen as the agent, who needs to find a safe and valid way to the destnation. The process of that can be designed as a Markov decision problem. As a deep reinforcenment learning way, DQN has deep networks to approximate the Q value accurately, and the Q(λ) algorithm makes it possible to make the right decisions in path planning process. It is a remarkable fact that even if the environment is unknown, DQN approach is also effective. Numerical simulations are provided to show the effectiveness of the suggested system.
Forensic Verification of Health Data From Wearable Devices Using Anonymous Witnesses

The use of wearable devices, such as smartwatches, glasses, clothes, and fitness bracelets is increasing at an ever-growing pace. Major corporations and insurance companies have started mandating their use for their employees and clients. Data from such devices have begun to feature in settlement claims and as evidence in courts as well, requiring it to be irrefutable and tamper-proof. Lack of protection for personal data as well as the contextual information such as location tracking and its use by law enforcement agencies is raising serious privacy concerns among the general public and civil liberty advocates. In this article, we propose a novel scheme to secure the wearable sensor's communication through its crowdsourced logging by neighboring wearable and smart devices called witnesses preserving the contextual information (such as time and location) as well. To ensure witness privacy, gateway and witness devices use the reciprocity property of wireless medium between them to generate pairs of closely matching link signatures, which not only provide the proof of presence for the witnesses in the vicinity but also act as their time-varying pseudonyms. We demonstrate the feasibility and efficacy of our scheme through the prototype implementation using real wireless devices, and via simulation and experimental results.
Hardware Evolution Based on Improved Simulated Annealing Algorithm in Cyclone V FPSoCs

Evolvable hardware (EHW) is an emerging area of research that uses evolutionary algorithms (EAs) to construct circuits without manual intervention.However, this technique confronts two major issues: the evolution efficiency of structures and the computational efficiency of EAs.To address these issues, we construct a novel virtual reconfigurable circuit (VRC) based on artificial neural network (ANN) architecture and develop a promising EA based on improved simulated annealing (ISA) for the research of EHW.Here, ISA escapes the local optimization through an inner loop and expands new search space through an outer loop.Furthermore, several strategies are proposed for the representation, perturbation and selection of solutions (individuals) to reduce the computational burden throughout the entire evolution process.The use of an Intel Cyclone V field programmable system-on-chip (FPSoC) further accelerates the execution of the algorithm and provides designers with much more high-level processing power than that provided by soft-cores.The obtained results show that the proposed method achieves high-speed processing and improves computational efficiency.The results also show that the method has good flexibility and scalability.
Object Classifications by Image Super-Resolution Preprocessing for Convolutional Neural Networks

Blurred small objects produced by cropping, warping, or intrinsically so, are challenging to detect and classify.Therefore, much recent research is focused on feature extraction built on Faster R-CNN and follow-up systems.In particular, RPN, SPP, FPN, SSD, and DSSD are the layered feature extraction methods for multiple object detections and small objects.However, super-resolution methods, as explored here, can improve these image analyses working on before or after convolutional neural networks.Our methods are focused on building better image qualities into the original image components so that these feature extraction methods become more effective when applied later.Our super-resolution preprocessing resulted in better deep learning in the number of classified objects, especially for small objects when tested on the VOC2007, MSO, and COCO2017 datasets.
A comparison study on nonlinear dimension reduction methods with kernel variations: Visualization, optimization and classification

Because of high dimensionality, correlation among covariates, and noise contained in data, dimension reduction (DR) techniques are often employed to the application of machine learning algorithms. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and their kernel variants (KPC A, KLDA) are among the most popular DR methods. Recently, Supervised Kernel Principal Component Analysis (SKPCA) has been shown as another successful alternative. In this paper, brief reviews of these popular techniques are presented first. We then conduct a comparative performance study based on three simulated datasets, after which the performance of the techniques are evaluated through application to a pattern recognition problem in face image analysis. The gender classification problem is considered on MORPH-II and FG-NET, two popular longitudinal face aging databases. Several feature extraction methods are used, including biologically-inspired features (BIF), local binary patterns (LBP), histogram of oriented gradients (HOG), and the Active Appearance Model (AAM). After applications of DR methods, a linear support vector machine (SVM) is deployed with gender classification accuracy rates exceeding 95% on MORPH-II, competitive with benchmark results. A parallel computational approach is also proposed, attaining faster processing speeds and similar recognition rates on MORPH-II. Our computational approach can be applied to practical gender classification systems and generalized to other face analysis tasks, such as race classification and age prediction.
A Security-Oriented Architecture for Federated Learning in Cloud Environments

Federate architectures are able to provide a scalable and shared environment for the distributed training and testing of machine learning applications. However, federated learning architectures shows potential security vulnerabilities in particular to the data poisoning attacks, incoming from participants to the federation. In such a scenario, a malicious participant may inject bad data in order to sabotage the result of the training. Such attacks may provide both a downgrade of the general performance of the learned model and may compromise the fairness of the machine learning application. As such applications are growing in criticality, such learning models must face with security and privacy as well as with scalability issues. The aim of the paper is to improve federated models by providing an architecture base on cloud computing which grants these additional features. The paper also discusses technical details, in particular, the usage of blockchain schemes to provide integrity and homomorphic cryptography for guaranteeing privacy.
Comparison of Scale Invariant Feature Transform and Speed Up Robust Feature for Image Forgery Detection Copy Move

Copy move is one of the techniques used to fake images. Many studies have developed methods to detect displacement of counterfeit copies of images. Some researchers use methods for the process of extracting features in an image such as SIFT and Speed Up Robust Feature. This study aims to compare the results of the detection of copy move image forgery using the SIFT algorithm and Speed Up Robust Feature. Testing will be done is in terms of accuracy and execution time. At the pre-processing stage, the image decomposition process is done using the Discrete Wavelet Transform (DWT) method. In this research there are 5 types of testing, namely without transformation, scale transformation, rotation transformation, lighting transformation, and blur transformation. Modeling the program code will use the MATLAB programming language. By using the transformation process, there are more key points, but fewer key points. Based on testing that has been done that the SIFT algorithm has a higher accuracy than SURF. The difference in the 1st test was 16.32%, the difference in the 2nd test was 9.96%, the difference in the 3rd test was 13.32%, the difference in the 4th test was 13.44%, and the difference in the 2nd test 5 of 1.87 %. To test the execution time, the SURF algorithm has a faster time compared to the SIFT. The difference in testing 1, 2, and 3 amounted to 1.97 seconds, the difference in the 4th test was 0.7 seconds and the difference in the fifth test was 0.62 seconds.
SPD Data Dictionary Learning Based on Kernel Learning and Riemannian Metric

The use of regional covariance descriptors to generate feature data represented by Symmetric Positive Definite (SPD) matrices from images or videos has become increasingly common in machine learning. However, SPD data itself does not constitute a vector space, and dictionary learning involves a large number of linear operations, so dictionary learning cannot be performed directly on SPD data. For this reason, a more common method is to map the SPD data to the Reproducing Kernel Hilbert Space (RKHS). The so-called kernel learning is to find the most suitable RKHS for specific tasks. RKHS can be uniquely generated by a kernel function. Therefore, RKHS learning can also be considered as kernel learning. In this article, there are two main contributions. The first contribution is to propose a framework which based on Kernel Learning and Riemannian Metric (KLRM). Usually the learnable kernel function framework is to learn some parameters in the kernel function. The second contribution is dictionary learning by applying KLRM to SPD data. The SPD data is transformed into the RKHS generated by KLRM, and RKHS after training provides the most suitable working space for dictionary learning. Under the proposed framework, we design a positive definite kernel function, which is defined by the Log-Euclidean metric. This function can be transformed into a corresponding Riemannian kernel. The experimental results provided in this paper is compared with other state-of-the-art algorithms for SPD data dictionary learning and show that the proposed algorithm achieves better results.
Time-aspect-sentiment Recommendation Models Based on Novel Similarity Measure Methods

The explosive growth of e-commerce has led to the development of the recommendation system. The recommendation system aims to provide a set of items that meet users’ personalized needs through analyzing users’ consumption records. However, the timeliness of purchasing data and the implicity of feedback data pose severe challenges for the existing recommendation methods. To alleviate these challenges, we exploit the user’s consumption records from the perspectives of user and item, by modeling the data on both item and user level, where the item-level value reflects the grade of item, and the user-level value reflects the user’s purchase intention. In this article, we collect the description information and the reviews of the items from public websites, then adopt sentiment analysis techniques to model the similarities on user level and item level, respectively. In particular, we extend the traditional latent factor model and propose two novel methods— I tem L evel Similarity M atrix F actorization (ILMF) and U ser L evel Similarity M atrix F actorization (ULMF)—by introducing two novel similarity measure methods. In ILMF and ULMF, the consistency between latent factors and explicit aspects is naturally incorporated into learning latent factors of the users and items, such that we can predict the users’ preferences on different items more accurately. Moreover, we propose I tem- U ser L evel Similarity M atrix F actorization (IULMF), which combines these two methods to study their contributions on the final performance. Experimental evaluations on the real datasets show that our methods outperform the baseline approaches in terms of both the precision and NDCG.
An Ensemble Net of Convolutional Auto-Encoder and Graph Auto-Encoder for Auto-Diagnosis

Effective auto-diagnosis assistants can benefit our healthcare system in various aspects, such as, saving labor cost, sharing knowledge among the crowd, and timely supporting the patients. However, the existing auto-diagnosis models are ineffective due to issues caused by information island, poor information coding, and inefficient informative retrieval. To address these issues, this article presents a diagnosis assistant that is designed and implemented to manage abundant historical inquiries between patients and doctors. The core of the auto-diagnosis system is a novel model called ensemble net of convolutional auto-encoder and graph auto-encoder (EN-C+GAE) which can be trained using historical data and generate a list of candidate diagnoses for a doctor to select. The experimental results show that the proposed approach outperforms the counterparts in generating more fluent and relevant diagnoses. The proposed system also shows its potential in real-world deployment in healthcare scenarios.
Graph-based selective rank fusion for unsupervised image retrieval

Nowadays, there is a great variety of visual features available for image retrieval tasks. While fusion strategies have been established as a promising alternative, an inherent difficulty in unsupervised scenarios is the task of selecting the features to combine. In this paper, a Graph-based Selective Rank Fusion is proposed. The graph is used to represent the effectiveness estimation of features and the complementarity among them. The selected combinations are defined by the Connected Components of the graph. High-effective retrieval results were achieved through a comprehensive experimental evaluation considering different public datasets, dozens of features and comparisons with related methods. Relative gains up to +54.73% were obtained in relation to the best isolated feature.
Segmentation method of multiple sclerosis lesions based on 3D‐CNN networks

Histopathology image segmentation is an important area in the field of computer aided diagnosis using image processing. The segmentation of Multiple sclerosis (MS) lesions from MR images can establish the basis for subsequent lesion reconstruction, volume estimation, and course evaluation. This study proposes a method for automatically segmenting MS lesions based on 3D convolutional neural network (CNN). The method is divided into two stages, each of which includes two convolution layers and two pooling layers. The alternative lesion voxels are selected in the first stage, while in the second stage, the final lesion voxels are segmented from the lesion voxels which are obtained in the first stage by restricting the conditions. The method has been tested on the MICCAI 2008 and 2016 datasets and compared to the other baseline methods. The experiment results show that the method has better performance than the other baseline methods on different evaluation indicators, including dice similarity coefficient, absolute difference in lesion volume, true positive rate, false positive rate, and predictive positivity value.
Beyond Artificial Reality

With billions of active social media accounts and millions of live video cameras, live new big data offer many opportunities for smart applications. However, the main consumers of the new big data have been humans. We envision the research on live knowledge , to automatically acquire real-time, validated, and actionable information. Live knowledge presents two significant and diverging technical challenges: big noise and concept drift. We describe the EBKA (evidence-based knowledge acquisition) approach, illustrated by the LITMUS landslide information system. LITMUS achieves both high accuracy and wide coverage, demonstrating the feasibility and promise of EBKA approach to achieve live knowledge.
Variational Conditioning of Deep Recurrent Networks for Modeling Complex Motion Dynamics

This work introduces stochastic models to address the problem of complex motion generation.Long-term motion generation is the primary task in several fields; however, less work has happened in this direction and is restricted to single-person activities.Looking forward, this work strives to solve the problem of two-person 3D motion generation.Single-person motion generation is comparatively simpler than twoperson, where the complexity is significantly higher.Error-propagation during motion generation is a key challenge.Current approaches often fail to keep the predicted skeletons on the manifold of valid poses.Another challenge is when the model restricts its learning to the training distribution.This leads to stagnation in generated moves.To this end, an end-to-end hierarchical auto-regressive model is proposed for efficient long-term motion generation.It is further constrained by an alignment network to reduce prediction errors.Proposed approach shows comparable results on prediction tasks, while outperforming the state-of-the-art on long-term motion generation.
An analysis of mapping strategies for storing RDF data into NoSQL databases

RDF and SPARQL are increasingly considered in a broad range of information management scenarios. Governments, large corporations, startups, open data initiatives and other organizations are using RDF as a data model with the purpose of sharing information and knowledge in several domains. In such scenario, scalability is the main issue for virtually all the recently proposed triplestores. Thus, many triplestores are employing NoSQL databases on their storage layers. In this paper we present a survey about mapping strategies for storing RDF data into NoSQL databases. Most of them considers the query response as the main measure for the quality of the approach. Thus, the impact of the mapping design is hidden behind optimization, partitioning and indexing issues. The main contribution of this survey is to discuss the benefits and disadvantages of each strategy.