Reviewerly/exHarmony huggingface dataset:
Harmonic Coding: An Optimal Linear Code for Privacy-Preserving Gradient-Type Computation

We consider the problem of distributedly computing a general class of functions, referred to as gradient-type computation, while maintaining the privacy of the input dataset. Gradient-type computation evaluates the sum of some "partial gradients", defined as polynomials of subsets of the input. It underlies many algorithms in machine learning and data analytics. We propose Harmonic Coding, which universally computes any gradient-type function, while requiring the minimum possible number of workers. Harmonic Coding strictly improves computing schemes developed based on prior works, such as Shamir's secret sharing and Lagrange Coded Computing, by injecting coded redundancy using harmonic progression. It enables the computing results of the workers to be interpreted as the sum of partial gradients and some redundant results, which then allows the cancellation of non-gradient terms in the decoding process. By proving a matching converse, we demonstrate the optimality of Harmonic Coding, even compared to the schemes that are non-universal (i.e., can be designed based on a specific gradient-type function).
On Practical Aspects of Coercion-Resistant Remote Voting Systems

Coercive behaviour is hard to control in the remote electronic voting setting. This is why a number of protocols have been proposed that aim at mitigating this threat. However, these proposals have remained largely academic. This paper takes the practical viewpoint and analyses the most common assumptions that are required by the various schemes, together with the exact level of coercion-resistance they provide.
INTERPOL: Information Theoretically Verifiable Polynomial Evaluation

We study the problem of verifiable polynomial evaluation in the user-server and multi-party setups. We propose INTERPOL, an information-theoretically verifiable algorithm that allows a user to delegate the evaluation of a polynomial to a server, and verify the correctness of the results with high probability and in sublinear complexity. Compared to the existing approaches which typically rely on cryptographic assumptions, INTERPOL stands out in that it does not assume any computational limitation on the server. INTERPOL relies on decomposition of polynomial evaluation into two matrix multiplications, and injection of computation redundancy in the form of locally computed parities with secret coefficients for verification. Furthermore, by generalizing INTERPOL to a multiparty setting consisting of a network of n untrusted nodes, where each node is interested in evaluating the same polynomial, we demonstrate that we can achieve an overall computational complexity comparable to a trusted setup, while guaranteeing information-theoretic verification at each node.
Sustainable development and cryptocurrencies as private money

Cryptocurrencies were initially promoted on their ability to operate in a fair and equitable manner free from financial institutions and governments. This paper evaluates sustainability issues facing cryptocurrencies in their functions as a form of private money. Specifically, we focus on their claims around inequality and poverty reduction using the United Nations Sustainability Development Goals framework. Our findings suggest that although the technologists believed they produced suitable alternatives to traditional money, there are a number of issues in regards to the sustainability of this form of finance. The major areas of the shortfall are around an energy usage perspective, the reward system for transactors to those with access to greater computer power (inequality) and issues over loss sharing when exchanges or user nodes are hacked and digital wallets are lost.
Handwritten optical character recognition by hybrid neural network training algorithm

Handwritten optical character recognition (OCR) is the renowned research area in several fields, like writers identification, bank cheques, and so on. Literature works presented the handwritten OCR for various languages. This paper proposes a hybrid neural network training algorithm for English handwritten OCR. Initially, the noise in the input image is removed using the median filter, and the image is resized. Then, the feature sets, positional, and structural descriptors are extracted from the input image. Once the feature sets are extracted, the proposed FLM-based neural network identifies the handwritten character. The FLM proposed by combining the Firefly and the Levenberg–Marquardt (LM) algorithm for training the neural network. Finally, the proposed FLM-based neural network is integrated within the feed forward neural network, and the classification of character is done with 95% accuracy based on the size of training data, number of hidden neurons and number of hidden layers.
Evaluation of Privacy Risks of Patients’ Data in China: Case Study

Patient privacy is a ubiquitous problem around the world. Many existing studies have demonstrated the potential privacy risks associated with sharing of biomedical data. Owing to the increasing need for data sharing and analysis, health care data privacy is drawing more attention. However, to better protect biomedical data privacy, it is essential to assess the privacy risk in the first place.In China, there is no clear regulation for health systems to deidentify data. It is also not known whether a mechanism such as the Health Insurance Portability and Accountability Act (HIPAA) safe harbor policy will achieve sufficient protection. This study aimed to conduct a pilot study using patient data from Chinese hospitals to understand and quantify the privacy risks of Chinese patients.We used g-distinct analysis to evaluate the reidentification risks with regard to the HIPAA safe harbor approach when applied to Chinese patients' data. More specifically, we estimated the risks based on the HIPAA safe harbor and limited dataset policies by assuming an attacker has background knowledge of the patient from the public domain.The experiments were conducted on 0.83 million patients (with data field of date of birth, gender, and surrogate ZIP codes generated based on home address) across 33 provincial-level administrative divisions in China. Under the Limited Dataset policy, 19.58% (163,262/833,235) of the population could be uniquely identifiable under the g-distinct metric (ie, 1-distinct). In contrast, the Safe Harbor policy is able to significantly reduce privacy risk, where only 0.072% (601/833,235) of individuals are uniquely identifiable, and the majority of the population is 3000 indistinguishable (ie the population is expected to share common attributes with 3000 or less people).Through the experiments based on real-world patient data, this work illustrates that the results of g-distinct analysis about Chinese patient privacy risk are similar to those from a previous US study, in which data from different organizations/regions might be vulnerable to different reidentification risks under different policies. This work provides reference to Chinese health care entities for estimating patients' privacy risk during data sharing, which laid the foundation of privacy risk study about Chinese patients' data in the future.
An Investigation of Chinese Older Adults' Self-Directed English Learning Experience Using Mobile Apps

A majority of research on mobile-assisted language learning focuses on traditional English language learners: thus, little attention has been paid to older adult learners. The purpose of the study is to explore the learning experiences of Chinese older adults using the free and popular English learning mobile apps, Duolingo/Hello English, Baicizhan, and Liulishuo, in a self-directed learning (SDL) context. A 17-week sequential mixed-methods study was designed. 55 older adults from age 45 to 85 participated. The informed grounded theory was used and Saldana's coding techniques for qualitative analysis. Quantitative data were analyzed using descriptive statistics and paired sample t-tests. Findings demonstrate that older adults persisted in learning using mobile apps for 17 weeks and increased their vocabulary significantly. Finally, a transformational learning model called MISAPP was created based on the empirical data and the SDL theory.
A robust steganographic technique based on improved chaotic-range systems

This paper presents a novel chaos-based technique of steganography in spatial domain. In the last decade, chaos theory has gained utmost importance in multimedia security applications. Generally, 1-D chaotic maps are employed because of computational ease and structural simplicity but their limited chaotic range is an obstacle. In the proposed work, we model the nonlinear combinations of 1-D chaotic maps. These chaotic systems possess chaotic behavior throughout the domain. We, for the first time, propose an effective application of these improved chaotic systems in steganography. These newly synthesized systems are used to embed secret information in the least significant bits (LSBs) of the host image. By comparing with some recent models, we prove that involving improved chaotic systems in steganographic approach really produces extraordinary outcomes. We determine the strength of our steganographic algorithm through the most significant statistical analyses such as information entropy, correlation, contrast, energy, homogeneity, peak signal to noise ratio (PSNR) and mean square error (MSE). We further prove the robustness of the anticipated technique against several image processing attacks. The upshot of these analysis techniques shows that our algorithm is highly reliable and produces coherent results.
A Study of Query Performance Prediction for Answer Quality Determination

We study a constrained retrieval setting in which either a single qualitative answer is provided as a response to a user-query or none. Given a user-query and the "best" answer that was retrieved from the underlying search engine, we wish to determine whether or not to accept it. To address this challenge, we propose an answer quality determination approach which leverages a novel set of answer-level query performance prediction (QPP) features, derived from a couple of recent discriminative QPP frameworks. Using various search benchmarks with both ad-hoc retrieval and non-factoid question answering (QA) tasks, we demonstrate the effectiveness of our approach.
An imprecise deep forest for classification

An imprecise deep forest classifier, which can be regarded as a modification of the deep forest proposed by Zhou and Feng, is presented in the paper. In the proposed classifier, the precise class probabilities at leaf nodes of decision trees in the deep forest are replaced with interval-valued probabilities produced by Walley’s imprecise Dirichlet model. The obtained sets of probabilities are averaged over all trees in a random forest as the imprecise probability masses. In order to use the stacking algorithm in the deep forest, a number of the class probability distributions are generated from sets of the random forest class probabilities such that decision trees of the next level of the deep forest cascade are trained on the basis of the extended training set which is added by new generated class distributions. Numerical examples illustrate how the incorporated imprecision may lead to outperforming results, especially, when the training set is rather small. The proposed classifier is the first modification of the deep forest, which efficiently deals with small datasets and takes into account a lack of sufficient training data. The ideas underlying the classifier may be a basis for development of a set on new machine learning models for small datasets.
Distributed Asynchronous Extended Target Tracking Using Random Matrix

In multiple sensor extended target tracking problems, asynchronous measurements are inevitable, since sensors usually have distinct sampling rates and initial sampling times. This paper presents a new distributed extended target tracking algorithm with asynchronous measurements for multiple sensor scenarios. A distributed Bayesian estimation scheme for asynchronous measurements using random matrix framework is derived. We also proposed an effective implementation using particle filtering. Compressed Gaussian Mixture approximations of extended state distributions are exchanged and fused between neighbor sensors. The temporal evolution of elliptic extent parameters can be obtained explicitly in our algorithm. Simulations show reasonable performance with a significant reduction of communication costs for small size systems compared with the centralized algorithm.
Application research based on improved genetic algorithm in cloud task scheduling

Task scheduling in the cloud environment is a hot issue in current research. Aiming at the task scheduling problem in cloud environment, this paper analyses the scheduling model of cloud tasks, proposed an improved genetic algorithm (PGA) based on phagocytosis, changed the crossover operation of st andard genetic algorithm (GA), formed a sub-chromosome individual after phagocytosis of two mother chromosomes, another individual was generated randomly, and the new individual generated after phagocytosis is determined by the fitness function and the load-balancing standard deviation, so that the evolution process can ensure a high proportion of high-quality individuals in the population. Ensure the diversity of the population. Then a multi-population hybrid coevolutionary genetic algorithm (MPHC_GA) is adopted, which uses the Min-Min algorithm to generate initial multiple sub-populations, and these sub-populations are evolved by standard genetic algorithm (GA) and improved genetic algorithm (PGA) based on phagocytosis. The simulation results show that the proposed algorithm is effective in cloud task scheduling.
RFR-DLVT: a hybrid method for real-time face recognition using deep learning and visual tracking

ABSTRACT In this paper, we proposed a hybrid method based on Deep Learning (DL) and visual tracking, RFR-DLVT, to achieve effective face recognition (FR). First, video sequences are divided into reference frames (RFs) and non-reference frames (NRFs). Then, the target face is identified through the DL-based FR method in RFs. In the meantime, the Kernelized-correlation-filters-based visual tracking method is used in NRFs to speed up FR. Our proposed method is tested on common data sets and achieves better performance. Particularly, RFR-DLVT has an accuracy of 99.6% and an efficiency of 30 (FPS) in the real-time FR on real-life surveillance videos.
An Assumption-Free Approach to the Dynamic Truncation of Ranked Lists

In traditional retrieval environments, a ranked list of candidate documents is produced without regard to the number of documents. With the rise in interactive IR as well as professional searches such as legal retrieval, this results in a substantial ranked list which is scanned by a user until their information need is satisfied. Determining the point at which the ranking model has low confidence in the relevance score is a challenging, but potentially very useful, task. Truncation of the ranked list must balance the needs of the user with the confidence of the retrieval model. Unlike query performance prediction where the task is to estimate the performance of a model based on an initial query and a given set documents, dynamic truncation minimizes the risk of viewing a non-relevant document given an external metric by estimating the confidence of the retrieval model using a distribution over its already calculated output scores, and subsequently truncating the ranking at that position. In this paper, we propose an assumption-free approach to learning a non-parametric score distribution over any retrieval model and demonstrate the efficacy of our method on Robust04, significantly improving user defined metrics compared to previous approaches.
Partitives and Partitivity

The paper offers a broad overview of several structures that fall under the umbrella term of “partitives”. It surveys previous analyses and proposes a treatment based on two null operators, PART% and PARTPRO, with different syntactic and semantic properties. The approach is argued to outperform previous theories in its ability to cover conjunctions, mass cases and contrastive differences across Italian and English. Crowd-sourced grammaticality judgments and corpus searches are used to supplement informal judgments and try to shed light on a complex set of facts.
Anderson and Krathwohl's Two-Dimensional Taxonomy Applied to Task Creation and Learning Assessment

Search tasks play an important role in the study and development of interactive information retrieval (IIR) systems. Prior work has examined how search tasks vary along dimensions such as the task's main activity, end goal, structure, and complexity. Recently, researchers have been exploring task complexity from the perspective of cognitive complexity---related to the types (and variety) of mental activities required by the task. Anderson & Krathwohl's two-dimensional taxonomy of learning has been a commonly used framework for investigating tasks from the perspective of cognitive complexity. A&K's 2D taxonomy involves a cognitive process dimension and an orthogonal knowledge dimension. Prior IIR research has successfully leveraged the cognitive process dimension of this 2D taxonomy to develop search tasks and investigate their effects on searchers' needs, perceptions, and behaviors. However, the knowledge dimension of the taxonomy has been largely ignored. In this conceptual paper, we argue that future IIR research should consider both dimensions of A&K's taxonomy. Specifically, we discuss related work, present details on both dimensions of A&K's taxonomy, and explain how to use the taxonomy to develop search tasks and learning assessment materials. Additionally, we discuss how considering both dimensions of A&K's taxonomy has important implications for future IIR research.
CodedSketch: Coded Distributed Computation of Approximated Matrix Multiplication

In this paper, we propose CodedSketch, as a distributed straggler-resistant scheme to compute an approximation of the multiplication of two massive matrices. The objective is to reduce the recovery threshold, defined as the total number of worker nodes that the master node needs to wait for to be able to recover the final result. To exploit the fact that only an approximated result is required, in reducing the recovery threshold, some sorts of pre-compression are required. However, compression inherently involves some randomness that would lose the structure of the matrices. On the other hand, considering the structure of the matrices is crucial to reduce the recovery threshold. In CodedSketch, we use count-sketch, as a hash-based compression scheme, on the rows of the first and columns of the second matrix, and a structured polynomial code on the columns of the first and rows of the second matrix. This arrangement allows us to exploit the gain of both in reducing the recovery threshold. To increase the accuracy of computation, multiple independent count-sketches are needed. This independency allows us to theoretically characterize the accuracy of the result and establish the recovery threshold achieved by the proposed scheme. To guarantee the independency of resulting count-sketches in the output, while keeping its cost on the recovery threshold minimum, we use another layer of structured codes.
Comparative of conventional and alternative Digital Image Correlation techniques for 3D modal characterisation

The integration of Fringe Projection and 2D-DIC is a low-cost optical technique which employs a single camera for full-field 3D displacement measurements using RGB colour encoding to filter coexisting fringe and speckle patterns out from a single image. In this work, this technique was explored together with conventional 3D-DIC for challenging modal analysis on a large aeronautical panel under random excitation. Modal identification was performed using the Least-Squares Complex Exponential approach. Multiple degree-of-freedom methods have been avoided in previous works or just employed in one direction due to high memory requirements to process 3D full-field data. In this work, the 3D modal characterisation was addressed with especial mention to mode shapes. Hardware limitations makes FP + DIC less sensitive than 3D-DIC and hence more suitable for lower frequencies. Nevertheless, the obtained modal parameters showed high agreement and hence FP + DIC demonstrated to be a cost-effective alternative for 3D modal analyses.
Cryptanalysis of a One-Time Code-Based Digital Signature Scheme

We consider a one-time digital signature scheme recently proposed by Persichetti and show that a successful key recovery attack can be mounted with limited complexity. The attack we propose exploits a single signature intercepted by the attacker, and relies on a statistical analysis performed over such a signature, followed by information set decoding. We assess the attack complexity and show that a full recovery of the secret key can be performed with a work factor that is far below the claimed security level. The efficiency of the attack is motivated by the sparsity of the signature, which leads to a significant information leakage about the secret key.
Extending resources for avoiding overloads of mixed‐criticality tasks in cyber‐physical systems

With the increasing number of services and industries including nuclear, chemical, aerospace, and automotive sectors in cyber-physical systems (CPSs), systems are being severely overloaded. CPSs comprises mixed-critical tasks which are of either safety-critical (high) or non-safety critical (low). In traditional task scheduling, most of the existing scheduling algorithms provide poor performance for high-criticality tasks when the system experiences overload and do not show explicit separation among different criticality tasks to take advantage of using cloud resources. Here, we propose a framework to schedule the mixed-criticality tasks by analyzing their deadlines and execution times which leverage the performance of parallel processing through OpenMP. The proposed framework introduces a machine learning-based prediction for a task offloading in the cloud. Moreover, it illustrates to execute a selected number of low-criticality tasks in the cloud while the high-criticality tasks are run on the local processors during the system overload. As a result, the high-criticality tasks meet all their deadlines and the system achieves a significant improvement in the overall execution time and better throughput. In addition, the experimental results employing OpenMP show the effectiveness of using the partitioned scheduling over the global scheduling method upon multiprocessor systems to achieve the tasks isolation.