Reviewerly/exHarmony huggingface dataset:
Linear complexity of some sequences derived from hyperelliptic curves of genus 2

For a given hyperelliptic curve C over a finite field with Jacobian JC, we consider the hyperelliptic analogue of the congruential generator defined by Wn = Wn− 1 + D for n ≥ 1 and D, W0 ∈ JC. We show that curves of genus 2 produce sequences with large linear complexity.
Planar k-uniform states: a generalization of planar maximally entangled states

Recently, Doroudiani and Karimipour [Phys. Rev. A 102 012427(2020)] proposed the notation of planar maximally entangled states which are a wider class of multipartite entangled states than absolutely maximally entangled states. There they presented their constructions in the multipartite systems but the number of particles is restricted to be even. Here, we first solve the remaining cases, i.e., constructions of planar maximally entangled states on systems with odd number of particles. In addition, we generalized the PME to the planar k-uniform states whose reductions to any adjacent k parties along a circle of N parties are maximally mixed. We presented a method to construct sets of planar k-uniform states which have minimal support.
Incorporation Of Rejection Criterion - A Novel Technique For Evaluating Semantic Segmentation Systems

Semantic segmentation ‘SS’ evaluation metrics are great tools to assess systems’ performance in terms of pixels’ accuracy and the alignment of segments. SS systems represent the core element of many human-system interaction applications. Thus, improving the comprehensiveness of SS evaluation metrics can accurately reflect the performance of human-system inter-action. Standard metrics ignore pixels’ confidence scores which can carry useful information. Pixels’ scores represent the level of confidence of the system for assigning class labels to image pixels. However, it has not been utilised by any evaluating metric for semantic segmentation systems. We propose to incorporate pixels’ confidence scores with existing metrics to gain better insights into systems’ behaviours. Results show the usefulness of the introduced approach to utilise the pixels’ scores in the evaluation process. Besides, using pixels’ scores thresholding can help to enhance the system performance on a specific task or objects of a particular size.
An Improved Clustering-Based Harmony Search Algorithm (IC-HS)

Due to its easy operation and good performance in solving global optimization problems, a new optimization algorithm named harmony search algorithm has attracted the attention of researchers and has been widely used in many optimization fields. However, the traditional harmony algorithm has some defects, for instance, in the process of optimization, the randomness is too high, which makes it easy to fall into local optimization, and the optimization speed and efficiency are greatly reduced. In order to overcome the defect, we propose a harmony search algorithm based on clustering method in this paper, which makes the distribution of initial value more average and appropriately reduces the randomness of the traditional algorithm. Finally, Sphere Function, Schwefel's Problem 2.22, Schwefel's Problem 2.26, Rastrigrin Function and Schaffer Function are introduced to test and analyze the performance of the algorithm. The results show that, compared with the traditional harmony search algorithm, our method avoids premature convergence and has faster convergence speed and higher efficiency.
Minimum Time for the Evolution to a Nonorthogonal Quantum State and Upper Bound of the Geometric Efficiency of Quantum Evolutions

We present a simple proof of the fact that the minimum time TAB for quantum evolution between two arbitrary states A and B equals TAB=ℏcos−1A|B/ΔE with ΔE being the constant energy uncertainty of the system. This proof is performed in the absence of any geometrical arguments. Then, being in the geometric framework of quantum evolutions based upon the geometry of the projective Hilbert space, we discuss the roles played by either minimum-time or maximum-energy uncertainty concepts in defining a geometric efficiency measure ε of quantum evolutions between two arbitrary quantum states. Finally, we provide a quantitative justification of the validity of the inequality ε≤1 even when the system only passes through nonorthogonal quantum states.
Unsupervised Variability Normalization For Anomaly Detection

Anomaly detectors are necessary to automatize industrial quality control. However, crafting such detectors is difficult due to the complexity and variability of the object even when working only with rigid objects. We show that adding a deep learning normalization step as a preprocessing step to model based detectors allows for better and more robust detections. This self-supervised normalization neural network is trained on non-anomalous data only. The proposed preprocessing method, followed by an automatic detector, achieves state-of-the-art results on rigid objects from the MvTec dataset.
Opportunity for Obtaining an Intuitionistic Fuzzy Estimation for Health-Related Quality of Life Data

In the paper a method for obtaining a degree of happiness of the residents of Burgas city using a real data of 1200 men and women for quality of life is proposed. Analyzing a health status data is very important in identifying and diagnosing diseases. For the determination the degree of happiness an intuitionistic fuzzy assessments (optimistic, strong optimistic, pessimistic and strong pessimistic) on the output of the multilayer perceptron are obtained.
Privacy budget management and noise reusing in multichain environment

To solve the problem of query restriction in supply-chain financial blockchain system, this paper proposes a privacy budget management and noise reusing method in multichain blockchain environment based on Hyperledger multichannel technology and community clustering algorithm. A historical record book is established to manage the privacy budget according to historical query types, and a differential privacy protection algorithm based on noise reusing is used to generate and reusing noise. In the experiments, we tested the privacy budget loss, data utility and system performance based on the business data-set from chemical supply chain. The experimental results show that the blockchain system with multichain structure based on community clustering algorithm reduces the system data storage space and decreases request processing time effectively. The privacy budget loss of the proposed method is about 1/3 of that of the pure Gaussian mechanism method. After 100 queries, the total amount of noise is about 8% less than that of pure Gaussian mechanism. Besides that, the noise reusing algorithm reduces the loss of the privacy budget and breaks through the query limitation caused by privacy budget wasting.
With One Voice: Composing a Travel Voice Assistant from Repurposed Models

Voice assistants provide users a new way of interacting with digital products, allowing them to retrieve information and complete tasks with an increased sense of control and flexibility. Such products are comprised of several machine learning models, like Speech-to-Text transcription, Named Entity Recognition and Resolution, and Text Classification. Building a voice assistant from scratch takes the prolonged efforts of several teams constructing numerous models and orchestrating between components. Alternatives such as using third-party vendors or re-purposing existing models may be considered to shorten time-to-market and development costs. However, each option has its benefits and drawbacks. We present key insights from building a voice search assistant for Booking.com. Our paper compares the achieved performance and development efforts in dedicated tailor-made solutions against existing re-purposed models. We share and discuss our data-driven decisions about implementation trade-offs and their estimated outcomes in hindsight, showing that a fully functional Machine-Learning product can be built from existing models.
Evolutionary multiple instance boosting framework for weakly supervised learning

Abstract Multiple instance boosting (MILBoost) is a framework which uses multiple instance learning (MIL) with boosting technique to solve the problems regarding weakly labeled inexact data. This paper proposes an enhanced multiple boosting framework—evolutionary MILBoost (EMILBoost) which utilizes differential evolution (DE) to optimize the combination of weak classifier or weak estimator weights in the framework. A standard MIL dataset MUSK and a binary classification dataset Hastie_10_2 are used to evaluate the results. Results are presented in terms of bag and instance classification error and also confusion matrix of test data.
Graph Affinity Network for Few-Shot Segmentation

Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with a few annotations. Previous methods mainly establish the correspondence between support images and query images with global information. However, human perception does not tend to learn a whole representation in its entirety at once. In this paper, we propose a novel network to build the correspondence from subparts, parts and whole. Our network mainly contain two novel designs: we firstly adopt graph convolutional network to make pixels not only contain the information of each pixel itself but also include its contextual pixels, and then a learnable Graph Affinity Module(GAM) is proposed to mine more accurate relationships as well as common object location inference between the support images and the query images. Experiments on the PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> dataset show that our method achieves state-of-the-art performance.
Obstructing DeepFakes by Disrupting Face Detection and Facial Landmarks Extraction

Recent years have seen fast development in synthesizing realistic human faces using AI technologies. AI-synthesized fake faces can be weaponized to cause negative personal and social impact. In this work, we develop technologies to defend individuals from becoming victims of recent AI-synthesized fake videos by sabotaging would-be training data. This is achieved by disrupting deep neural networkDeep neural networks (DNN)-based face detectionFace detection and facial landmark extractionFacial landmark extraction method with specially designed imperceptible adversarial perturbationsAdversarial perturbations to reduce the quality of the detected faces. We empirically show the effectiveness of our methods in disrupting state-of-the-art DNN-based face detectors and facial landmark extractors on several datasets.
An Improved Unbalanced Data Classification Method Based on Hybrid Sampling Approach

The problem of data imbalance has received far- reaching concerns since they could affect the accuracy of classification problem in the area of machine learning. As the minority class instances can be ignored by traditional classifiers, it is necessary to improve the recognition rate of minority instances. Therefore, the paper proposes a new hybrid sampling method to solve the data imbalance problem by enlarging the proportion of minority instances. For the oversampling part, a variant of SMOTE is provided combining methods of LR-SMOTE and CCR (Combined Cleaning and Resampling Algorithm); for the under-sampling part, the Tomek-link method is utilized to complete the task. After the pre-processing stage, the data set is classified by Random Forest (RF). Experimental results show that the novel algorithm effectively enhances the performance of RF on the data set with a higher accuracy.
Analysis of Image Compression Algorithms on Vivado HLS

The resolution of the cameras is rising day by day due to developments in optical technology. Such high resolution images cause the size of the images to be huge, putting a strain on the available storage. Thus, there is a need to compress the size of the image without losing important data from the actual image. In this paper, we therefore present implementation and a detailed analysis of different lossy image compression algorithms such as DCT (Discrete Cosine Transform), FDCT (Fast Discrete Cosine Transform) and Haar Wavelet Transform. These algorithms are simulated and synthesized using Xilinx's VIVADO HLS platform for a Xilinx Artix 7 family board. These algorithms are accelerated using the pragmas provided by VIVADO HLS to optimize the application. The paper also provides an analysis on the tradeoff between these algorithms and usage of accelerators in the algorithms. We have calculated the Most Significant Error (MSE) and Peak Signal to Noise Ratio (PSNR) and achieved the PSNR value to be in the permissible range of 30dB to 50dB.
Deep-gAnswer: A Knowledge Based Question Answering System

In this demonstration, we present Deep-gAnswer, a knowledge-based question answering system. gAnswer is based on semantic parsing and heuristic rules for entity recognition, relation recognition, and SPARQL generation. By making use of a pre-trained model, we implement new entity and relation recognition networks. Also, it is found that the traditional method works better when information of entity and relation is correctly given. Therefore, we combine entity and relation recognition networks with the previous SPARQL generation process to get Deep-gAnswer. Experimental results show that Deep-gAnswer outperforms the previous one, especially on Chinese dataset.
Self-supervised multi-body scene flow estimation

In this paper, we address the problem of scene flow estimation from consecutive stereo pairs. In contrast to the state-of-the-art supervised learning based methods, we propose a self-supervised learning based pipeline that removes the requirement of large-scale ground truth annotations. Specifically, we employ a shared encoder StereoFlowNet to simultaneously learn optical flow estimation and disparity estimation, which not only achieves a compact network representation but also exploits the inherent connections between optical flow estimation and disparity estimation. To leverage the scene structure and motion representations, we propose to utilize a piece-wise planar model based disparity computation and multiple rigid body motion representation of the dynamic scene. In this way, the geometric and motion constraints play strong regularizations for the underlying problem. Experimental results on benchmarking dataset show that our proposed method achieves state-of-the-art performance in both optical flow and disparity estimation.
Soft-Sign Stochastic Gradient Descent Algorithm for Wireless Federated Learning

Federated learning over wireless networks requires aggregating locally computed gradients at a server where the mobile devices send statistically distinct gradient information over heterogenous communication links. This paper proposes a Bayesian approach for wireless federated learning referred to as soft-sign stochastic gradient descent (soft-signSGD). The idea of soft-signSGD is to aggregate the one-bit quantized local gradients at the server by jointly exploiting i) the prior distributions of the local gradients, ii) the gradient quantizer function, and iii) channel distributions. This aggregation method is optimal in the sense of minimizing the mean-squared error (MSE) under a simplified Gaussian prior assumption on the local gradient. From simulations, we demonstrate that soft-signSGD considerably outperforms the conventional sign stochastic gradient descent algorithm when training and testing neural networks using the MNIST dataset and the CIFAR-10 dataset over heterogeneous wireless networks.
Data Privacy and Security in Social Networks

Social networking creates relationships through the internet and it gains indivisible relation with human life nowadays. Social networking sites and applications handle a large volume of data. As more personal information flows in and out of social networks, data privacy and security in the social network become a topic of discussion and arguments. This chapter emphasizes data privacy and also differentiates privacy from security. Initial sections of the chapter explain privacy in its elementary form as a need and right of a human being under the perspective of anthropology and behavioral science. Personal data privacy, its current scenario, threats and its protection by law and policymaking by various governments around the world are discussed further. The chapter considers social networking beyond networking sites and applications, and hence a discussion on privacy threats for sensitive data which spread across fields such as health data, forensic, smart toys, image and video surveillance is also analyzed. Positives and negatives of social network's underlying technologies, like machine learning, artificial intelligence, data sciences, the internet of things and blockchain are discussed in terms of data privacy. The personal data privacy measures imposed by law that need to be incorporated as the part of privacy policies of organizations or that need to be implemented with the support of data security mechanisms are discussed in the last part of the chapter.
Research on the Training Mode of University Economic and Trade Russian Talents based on Practical Teaching

In recent years, with the improvement of Russia's economic situation, various exchanges and cooperation between China and Russian-speaking countries have become increasingly frequent and Russian language in China once again presents a strong momentum of development. It is the sound momentum of the continuous development of economic and trade cooperation between China and Russia that has provided a broad prospect for the cultivation of Russian language talents. Based on this, this paper puts forward the exploration and research on the training mode of University Economic and trade Russian talents based on practice teaching. Through the application of Russian professional practice teaching and personnel training mode aspects of exploration, and to investigate the current situation of economic and trade Russian demand, on the current training mode for economic and trade of Russian majors are analyzed, and put forward to set up the training model in the foreign trade with higher vocational characteristic of Russian, this paper related to the practice teaching of cultivating talents of foreign trade of Russian education teaching method, to build economic and trade with practical characteristics of Russian personnel training mode, and proposes the rationalization proposal. Studies have shown that the practice teaching has an irreplaceable role in modern teaching, Russian professional practice teaching according to the specific training target and cultivation specifications to Russian people, on the basis of the practice teaching must be according to the dual requirements of higher vocational education and foreign language education, emphasis on practice teaching, improve the quality of practice teaching, to develop the foreign trade of Russian social needs talents.
Efficiently Mining Large Gradual Patterns Using Chunked Storage Layout

Existing approaches for extracting gradual patterns become inefficient in terms of memory usage when applied on data sets with huge numbers of objects. This inefficiency is caused by the contiguous nature of loading binary matrices into main memory as single blocks when validating candidate gradual patterns. This paper proposes an efficient storage layout that allows these matrices to be split and loaded into/from memory in multiple smaller chunks. We show how HDF5 (Hierarchical Data Format version 5) may be used to implement this chunked layout and our experiments reveal a great improvement in memory usage efficiency especially on huge data sets.
Accurate Compensation Makes the World More Clear for the Visually Impaired

Visual impairment is one of the most serious social and public health problems in the world, therefore, it is of great theoretical and practical significance to study the image enhancement algorithms for the visually impaired, which is the basis for the development of assistive devices. In this paper, a general deep learning based image enhancement framework for the visually impaired is proposed, which can be used to enhance images to compensate for any visually impaired symptom that can be modeled. Take central vision loss as an example, we first model the central vision loss based on the contrast sensitivity function (CSF) specified by clinical indicator Pelli-Robson score and logMAR visual acuity, and then use the proposed framework to generate an image enhancement method aiming at compensating for the central vision loss. Both the simulation experiment and the patient experiment show the superiority of the proposed image enhancement method designed for the central vision loss, which also validates the effectiveness of the proposed framework.
A new near-lossless image information hiding algorithm with controlled hiding capacity

Abstract Image information hiding technology can embed more data without increasing the amount of transmitted data. But in most cases, the information embedding rate is different for different carrier images. To increase the embedding rate, the data will be damaged. In order to improve this problem, a new near-lossless image information hiding algorithm with controlled hiding capacity is proposed, which is referred to as NLH. This method makes the pre-processed data in a specific range to facilitate information hiding by interval transformation and data mapping. According to analysis and calculation, the lossless performance of the proposed algorithm in this paper is better than the LSB algorithm. And the result of simulation shows that this method has a fixed information embedding rate (18.75%/1.5bpp) without more transmitted data, and the original image is restored losslessly or nearly losslessly with the improved transmission efficiency.
A novel approach on the joint de-identification of textual and relational data with a modified mondrian algorithm

Traditional approaches for data anonymization consider relational data and textual data independently. We propose rx-anon, an anonymization approach for heterogeneous semi-structured documents composed of relational and textual attributes. We map sensitive terms extracted from the text to the structured data. This allows us to use concepts like k-anonymity to generate a joint, privacy-preserved version of the heterogeneous data input. We introduce the concept of redundant sensitive information to consistently anonymize the heterogeneous data. To control the influence of anonymization over unstructured textual data versus structured data attributes, we introduce a modified, parameterized Mondrian algorithm. We evaluate our approach with two real-world datasets using a Normalized Certainty Penalty score, adapted to the problem of jointly anonymizing relational and textual data. The results show that our approach is capable of reducing information loss by using the tuning parameter to control the Mondrian partitioning while guaranteeing k-anonymity. As rx-anon is a framework approach, it can be reused and extended by other anonymization algorithms, privacy models, and textual similarity metrics.