Reviewerly/exHarmony huggingface dataset:
Estimates for the number of rational points on simple abelian varieties over finite fields

Let A be a simple Abelian variety of dimension g over the field $$\mathbb {F}_q$$ . The paper provides improvements on the Weil estimates for the size of $$A(\mathbb {F}_q)$$ . For an arbitrary value of q we prove $$(\lfloor (\sqrt{q}-1)^2 \rfloor + 1)^g \le \#A(\mathbb {F}_q) \le (\lceil (\sqrt{q}+1)^2 \rceil - 1)^{g}$$ holds with finitely many exceptions. We compute improved bounds for various small values of q. For instance, the Weil bounds for $$q=3,4$$ give a trivial estimate $$\#A(\mathbb {F}_q) \ge 1$$ ; we prove $$\# A(\mathbb {F}_3) \ge 1.359^g$$ and $$\# A(\mathbb {F}_4) \ge 2.275^g$$ hold with finitely many exceptions. We use these results to give some estimates for the size of the rational 2-torsion subgroup $$A(\mathbb {F}_q)[2]$$ for small q. We also describe all abelian varieties over finite fields that have no new points in some finite field extension.
Privacy-preserving collaborative machine learning on genomic data using TensorFlow

Machine learning (ML) methods have been widely used in genomic studies. However, genomic data are often held by different stakeholders (e.g. hospitals, universities, and healthcare companies) who consider the data as sensitive information, even though they desire to collaborate. To address this issue, recent works have proposed solutions using Secure Multi-party Computation (MPC), which train on the decentralized data in a way that the participants could learn nothing from each other beyond the final trained model.
Gender Recognition from Facial Images using Local Gradient Feature Descriptors

Local gradient feature descriptors have been proposed to calculate the invariant feature vector. These local gradient methods are very fast to compute the feature vector and achieved very high recognition accuracy when combined with the support vector machine (SVM) classifier. Hence, they have been proposed to solve many problems in image recognition, such as the human face, object, plant, and animal recognition. In this paper, we propose the use of the Haar-cascade classifier for the face detection and the local gradient feature descriptors combined with the SVM classifier to solve the gender recognition problem. We detected 4,624 face images from the ColorFERET dataset. The face images data used in gender recognition included 2,854 male and 1,770 female images, respectively. We divided the dataset into train and test set using 2-fold and 10-fold cross-validation. First, we experimented on 2-fold cross-validation, the results showed that the histogram of oriented gradient (HOG) descriptor outperforms the scale-invariant feature transform (SIFT) descriptor when combined with the support vector machine (SVM) algorithm. The accuracy of the HOG+SVM and the SIFT+SVM were 96.50% and 95.98%. Second, we experimented on 10-fold cross-validation and the SIFT+SVM showed high performance with an accuracy of 99.20%. We discovered that the SIFT+SVM method needed more training data when creating the model. On the other hand, the HOG+SVM method provided better accuracy when the training data was insufficient.
Blind quality assessment for tone-mapped images based on local and global features

In order to show high dynamic range (HDR) images by traditional displays, various tone-mapping operators have been designed to convert HDR images into low dynamic range (LDR) images recently. However, how to estimate the visual quality of LDR images effectively is still challenging. In this paper, we propose a novel blind quality assessment method for tone-mapped images with the consideration of naturalness and the perceptual characteristics of human visual system (HVS). First, we design parametric models that describe characteristics of chromatic information in tone-mapped images and extract quality-aware features based on global statistics model to characterize the naturalness of tone-mapped images. Second, motivated by perceptual characteristics that the HVS is highly adaptive to the image texture, we employ local texture features to capture the quality degradation of tone-mapped images. Support vector regression (SVR) is used to train the quality prediction model from features to human ratings. Experimental results indicate that the proposed metric can get better performance in predicting the visual quality of tone-mapped images than the state-of-the-art methods.
Leverage Label and Word Embedding for Semantic Sparse Web Service Discovery

Information retrieval-based Web service discovery approach suffers from the semantic sparsity problem caused by lacking of statistical information when the Web services are described in short texts. To handle this problem, external information is often utilized to improve the discovery performance. Inspired by this, we propose a novel Web service discovery approach based on a neural topic model and leveraging Web service labels. More specifically, words in Web services are mapped into continuous embeddings, and labels are integrated by a neural topic model simultaneously for embodying external semantics of the Web service description. Based on the topic model, the services are interpreted into hierarchical models for building a service querying and ranking model. Extensive experiments on several datasets demonstrated that the proposed approach achieves improved performance in terms of F-measure. The results also suggest that leveraging external information is useful for semantic sparse Web service discovery.
A Survey study on Joint Image Compression - Encryption Methods

Images form a significant part of data, particularly in multimedia and video conferencing applications. Many techniques and algorithms for image compression and encryption are proposed to do our need for efficient ways of storing and transmitting large amounts of data. This paper discusses some of the modern methods that combination of image compression and encryption techniques.
Coarse-to-fine Grained Image Splicing Localization Method Based on Noise Level Inconsistency

Image splicing detection is one of the important technologies to protect image content authenticity. In this work, we propose a coarse-to-fine grained image splicing localization method. The proposed method is based on the fact is that the noise level of the splicing image region is inconsistent with that of the original image region. In the proposed method, we used Laplace operator to extract the local noise of the image, and estimated the local correlation feature by using the prediction residual. Then, we use the clustering algorithm for coarse grained splicing localization to get suspicious spliced regions, and use an approach of connected region expansion/corrosion for fine grained detection to get the precise splicing regions. Compared with the existing noise-based image splicing localization methods, the proposed method has high detection accuracy and stronger robustness to content-preserving manipulations.
Self-Aware Neural Network Systems: A Survey and New Perspective

Neural network (NN) processors are specially designed to handle deep learning tasks by utilizing multilayer artificial NNs. They have been demonstrated to be useful in broad application fields such as image recognition, speech processing, machine translation, and scientific computing. Meanwhile, innovative self-aware techniques, whereby a system can dynamically react based on continuously sensed information from the execution environment, have attracted attention from both academia and industry. Actually, various self-aware techniques have been applied to NN systems to significantly improve the computational speed and energy efficiency. This article surveys state-of-the-art self-aware NN systems (SaNNSs), which can be achieved at different layers, that is, the architectural layer, the physical layer, and the circuit layer. At the architectural layer, SaNNS can be characterized from a data-centric perspective where different data properties (i.e., data value, data precision, dataflow, and data distribution) are exploited. At the physical layer, various parameters of physical implementation are considered. At the circuit layer, different logics and devices can be used for high efficiency. In fact, the self-awareness of existing SaNNS is still in a preliminary form. We propose a comprehensive SaNNS from a new perspective, that is, the model layer, to exploit more opportunities for high efficiency. The proposed system is called as MinMaxNN, which features model switching and elastic sparsity based on monitored information from the execution environment. The model switching mechanism implies that models (i.e., min and max model) dynamically switch given different inputs for both efficiency and accuracy. The elastic sparsity mechanism indicates that the sparsity of NNs can be dynamically adjusted in each layer for efficiency. The experimental results show that compared with traditional SaNNS, MinMaxNN can achieve 5.64× and 19.66% performance improvement and energy reduction, respectively, without notable loss of accuracy and negative effects on developers' productivity.
The effect of surface gravity on line-depth ratios in the wavelength range 0.97–1.32 µm

ABSTRACT A line-depth ratio (LDR) of two spectral lines with different excitation potentials is expected to be correlated with the effective temperature (Teff). It is possible to determine Teff of a star with a precision of tens of Kelvin if dozens or hundreds of tight LDR–Teff relations can be used. Most of the previous studies on the LDR method were limited to optical wavelengths, but Taniguchi and collaborators reported 81 LDR relations in the YJ band, 0.97–1.32 µm, in 2018. However, with their sample of only 10 giants, it was impossible to account for the effects of surface gravity and metallicity on the LDRs well. Here, we investigate the gravity effect based on YJ-band spectra of 63 stars including dwarfs, giants, and supergiants observed with the WINERED spectrograph. We found that some LDR–Teff relations show clear offsets between the sequence of dwarfs and those of giants/supergiants. The difference between the ionization potentials of the elements considered in each line pair and the corresponding difference in the depths can, at least partly, explain the dependency of the LDR on the surface gravity. In order to expand the stellar parameter ranges that the LDR method can cover with high precision, we obtained new sets of LDR–Teff relations for solar-metal G0–K4 dwarfs and F7–K5 supergiants, respectively. The typical precision that can be achieved with our relations is 10–30 K for both dwarfs and supergiants.
Estimating Kullback-Leibler Divergence Using Kernel Machines

Recently, a method called the Mutual Information Neural Estimator (MINE) that uses neural networks has been proposed to estimate mutual information and more generally the Kullback-Leibler (KL) divergence between two distributions. The method uses the Donsker-Varadhan representation to arrive at the estimate of the KL divergence and is better than the existing estimators in terms of scalability and flexibility. The output of MINE algorithm is not guaranteed to be a consistent estimator. We propose a new estimator that instead of searching among functions characterized by neural networks searches the functions in a Reproducing Kernel Hilbert Space. We prove that the proposed estimator is consistent. We carry out simulations and show that when the datasets are small the proposed estimator is more reliable than the MINE estimator and when the datasets are large the performance of the two methods are close.
Edge and Cloud Collaborative Entity Recommendation Method towards the IoT Search

There are massive entities with strong denaturation of state in the physical world, and users have urgent needs for real-time and intelligent acquisition of entity information, thus recommendation technologies that can actively provide instant and precise entity state information come into being. Existing IoT data recommendation methods ignore the characteristics of IoT data and user search behavior; thus the recommendation performances are relatively limited. Considering the time-varying characteristics of the IoT entity state and the characteristics of user search behavior, an edge-cloud collaborative entity recommendation method is proposed via combining the advantages of edge computing and cloud computing. First, an entity recommendation system architecture based on the collaboration between edge and cloud is designed. Then, an entity identification method suitable for edge is presented, which takes into account the feature information of entities and carries out effective entity identification based on the deep clustering model, so as to improve the real-time and accuracy of entity state information search. Furthermore, an interest group division method applied in cloud is devised, which fully considers user's potential search needs and divides user interest groups based on clustering model for enhancing the quality of recommendation system. Simulation results demonstrate that the proposed recommendation method can effectively improve the real-time and accuracy performance of entity recommendation in comparison with traditional methods.
Robust and fast filtering method for enhancement of two-dimensional quality-guided path unwrapping algorithms

This paper presents a new filtering algorithm for reducing phase noise based on the bicubic interpolation method (BIM). The unwrapped phase map accuracy is enhanced by the combination of BIM with a conventional unwrapping algorithm. The bicubic interpolation filtering (BIF) and bicubic interpolation smoothed filtering (BISF) methods are two powerful low-pass filters. The simulation shows that the BIF and BISF convert the initial noise distribution to Gaussian distribution. The Mirau interferometer is used to improve the performance of the proposed filtering algorithms. The root mean square error between two quality-guided path unwrapping algorithms and BISF method is estimated at approximately 31 nm.
Secure Data Management in Internet-of-Things Based on Blockchain

As Internet-of-Things (IoT) infrastructure has significantly dominated our life such as smart homes, hospitals, vehicles, and farms, efficient and secure IoT data management have been a new challenge to handle massive volumes of data and a myriad of IoT devices. To solve these problems, blockchain has become a critical technology to create a decentralized and publicly accessible network for immutable and verifiable data transaction for IoT. This paper proposes an efficient and secure data management system by using the blockchain technology to issue certificates for IoT devices and to retrieve data by using the certificates in any locations. The certified IoT devices in blockchain provide user privacy and data integrity for users while giving efficient data storage and retrieval through our blockchain-based IoT certificate management system. The certificates issued to the IoT devices hold data generated by the device and store it on the blockchain. We evaluate our proposed system on Ethereum to measure transaction cost and speed by issuing and verifying certificates according to the amount of data stored.
Smart Contract Derivatives

The abilities of smart contracts today are confined to reading from their own state. It is useful for a smart contract to be able to react to events and read the state of other smart contracts. In this paper, we devise a mechanism by which a derivative smart contract can read data, observe the state evolution, and react to events that take place in one or more underlying smart contracts of its choice. Our mechanism works even if the underlying smart contract is not designed to operate with the derivative smart contract. Like in traditional finance, derivatives derive their value (and more generally state) through potentially complex dependencies. We show how derivative smart contracts can be deployed in practice on the Ethereum blockchain without any forks or additional assumptions. We leverage any NIPoPoWs mechanism (such as FlyClient or superblocks) to obtain succinct proofs for arbitrary events, making proving them inexpensive for users. The latter construction is of particular interest, as it forms the first introspective SPV client: an SPV client for Ethereum in Ethereum. Last, we describe applications of smart contract derivatives which were not possible prior to our work, in particular the ability to create decentralized insurance smart contracts which insure an underlying on-chain security such as an ICO, as well as futures and options.
Simple Yet Effective Way for Improving the Performance of Lossy Image Compression

Lossy image compression methods with deep neural network (DNN) include a quantization process between encoder and decoder networks as an essential part to increase the compression rate. However, the quantization operation impedes the flow of gradient and often disturbs the optimal learning of the encoder, which results in distortion in the reconstructed images. To alleviate this problem, this paper presents a simple yet effective way that enhances the performance of lossy image compression without imposing training overhead or modifying the original network architectures. In the proposed method, we utilize an auxiliary branch called a shortcut which directly connects the encoder and decoder. Since the shortcut does not include the quantization process, it supports the optimal learning of the encoder by flowing the accurate gradient. Furthermore, to assist the decoder which should handle additional feature maps obtained via the shortcut, we also propose a residual refinement unit (RRU) following the quantizer. The experimental results show that the image compression network trained with the proposed method remarkably improves the performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and multi-scale structural similarity (MS-SSIM).
Geometric Range Search on Encrypted Data With Forward/Backward Security

This article presents two dynamic symmetric searchable encryption schemes for geometric range search. Our constructions are the first to provide forward/backward security in the context of SSE-based schemes supporting geometric range search. Besides, we define a security notion called content privacy. This security notion captures the leakages that are critical in the context of geometric range search but not considered by forward/backward security. Content privacy eliminates the leakage on the updated points of the database during both search and update. Due to the inherent leakages associated with range queries, none of the existing related works can support content privacy, whereas the design of our constructions avoids such leakages. When compared to the state-of-the-art schemes, our constructions provide a higher level of security and practical efficiency supported by our experimental results.
Data Optimization CNN Accelerator Design on FPGA

Image understanding is becoming a vital feature in ever more applications ranging from medical diagnostics to autonomous vehicles. Many applications demand for embedded solutions that integrate into existing systems with power constraints and tight real-time. Convolutional Neural Networks (CNNs) presently achieve record-breaking accuracies in all image understanding benchmarks, but have a very high computational complexity. Modern high-end FPGA generations feature hundreds of thousands of configurable logic blocks, and additionally include an abundance of hardened functional units which enable fast and efficient implementations of common functions. Many researchers have proposed their CNN accelerator prototypes on FPGA. But one problem of the stateof-the-art designs is that they have not solved the data dependence problem well. Data dependency is an important factor affecting accelerator performance. Current designs solve data dependence problem by adding hardware module on FPGA. But this approach has little effect and leads to increased hardware complexity. In this paper, we propose an optimization on the data arrangement in CNN. Which solves the data dependence in CNN by rearranging the data. The rearranged data is stored in a hardware-friendly form. By this way, our accelerator can apply pipeline technology better than current designs. We validate our approach on Xilinx Zynq XC-7Z045 board. The experimental results show that our approach has obvious advantages in terms of hardware resource consumption and bandwidth compare to state-of-the-art designs.
Statistical Insights and Fraud Techniques for Telecommunications Sector in Egypt

National Telecommunications Regulatory Authority (NTRA) has investigated the telecommunication performance in the service providers in Egypt between the periods of February 2018 till February 2019. Fraud is a multi-billions pounds' problem around the world. The problem is the huge financial loss of revenue that can affect the trustworthiness and performance of the telecommunication corporate. Egypt is one of the developing countries that are witnessing a rapid-growth development in its telecommunications infrastructure. As a result of the increment rate of corporate and internet users, fraudsters are exploiting the vulnerabilities to gain illegal revenue through the subscribers. The telecommunications sector in Egypt has suffered in the last two decades from different types of telecom fraud cases which have damaged the financial sector in both telecommunication side and governmental side. In this paper, we present: a) Statistical insights on users, mobile operators and Internet companies for the telecommunications sector in Egypt in 2018/2019. b) An illustration for the common fraudulent methodologies that are used against both users and telecom operators in Egypt. c) A discussion on the consequences of fraud cases with a suggested framework as a proactive methodology to prevent the escalated threat of telecom fraud.
Knowledge graph entity typing via learning connecting embeddings

Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs. Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference. Accordingly, we build two novel embedding models to realize the mechanisms. Afterward, a joint model via connecting them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs. Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing.