Reviewerly/exHarmony huggingface dataset:
Compact Adaptively Secure ABE for $$\mathsf {NC^1}$$ from k-Lin

We present compact attribute-based encryption (ABE) schemes for $$\mathsf {NC^1}$$ that are adaptively secure under the k-Lin assumption with polynomial security loss. Our KP-ABE scheme achieves ciphertext size that is linear in the attribute length and independent of the policy size even in the many-use setting, and we achieve an analogous efficiency guarantee for CP-ABE. This resolves the central open problem posed by Lewko and Waters (CRYPTO 2011). Previous adaptively secure constructions either impose an attribute “one-use restriction” (or the ciphertext size grows with the policy size), or require q-type assumptions.
Heterogeneous Domain Adaptation via Nonlinear Matrix Factorization

Heterogeneous domain adaptation (HDA) aims to solve the learning problems where the source- and the target-domain data are represented by heterogeneous types of features. The existing HDA approaches based on matrix completion or matrix factorization have proven to be effective to capture shareable information between heterogeneous domains. However, there are two limitations in the existing methods. First, a large number of corresponding data instances between the source domain and the target domain are required to bridge the gap between different domains for performing matrix completion. These corresponding data instances may be difficult to collect in real-world applications due to the limited size of data in the target domain. Second, most existing methods can only capture linear correlations between features and data instances while performing matrix completion for HDA. In this paper, we address these two issues by proposing a new matrix-factorization-based HDA method in a semisupervised manner, where only a few labeled data are required in the target domain without requiring any corresponding data instances between domains. Such labeled data are more practical to obtain compared with cross-domain corresponding data instances. Our proposed algorithm is based on matrix factorization in an approximated reproducing kernel Hilbert space (RKHS), where nonlinear correlations between features and data instances can be exploited to learn heterogeneous features for both the source and the target domains. Extensive experiments are conducted on cross-domain text classification and object recognition, and experimental results demonstrate the superiority of our proposed method compared with the state-of-the-art HDA approaches.
Quantum SDP Solvers: Large Speed-Ups, Optimality, and Applications to Quantum Learning

We give two new quantum algorithms for solving semidefinite programs (SDPs) providing quantum speed-ups. We consider SDP instances with m constraint matrices, each of dimension n, rank at most r, and sparsity s. The first algorithm assumes an input model where one is given access to an oracle to the entries of the matrices at unit cost. We show that it has run time O~(s^2 (sqrt{m} epsilon^{-10} + sqrt{n} epsilon^{-12})), with epsilon the error of the solution. This gives an optimal dependence in terms of m, n and quadratic improvement over previous quantum algorithms (when m ~~ n). The second algorithm assumes a fully quantum input model in which the input matrices are given as quantum states. We show that its run time is O~(sqrt{m}+poly(r))*poly(log m,log n,B,epsilon^{-1}), with B an upper bound on the trace-norm of all input matrices. In particular the complexity depends only polylogarithmically in n and polynomially in r.We apply the second SDP solver to learn a good description of a quantum state with respect to a set of measurements: Given m measurements and a supply of copies of an unknown state rho with rank at most r, we show we can find in time sqrt{m}*poly(log m,log n,r,epsilon^{-1}) a description of the state as a quantum circuit preparing a density matrix which has the same expectation values as rho on the m measurements, up to error epsilon. The density matrix obtained is an approximation to the maximum entropy state consistent with the measurement data considered in Jaynes' principle from statistical mechanics.As in previous work, we obtain our algorithm by quantizing classical SDP solvers based on the matrix multiplicative weight update method. One of our main technical contributions is a quantum Gibbs state sampler for low-rank Hamiltonians, given quantum states encoding these Hamiltonians, with a poly-logarithmic dependence on its dimension, which is based on ideas developed in quantum principal component analysis. We also develop a fast quantum OR lemma with a quadratic improvement in gate complexity over the construction of Harrow et al. [Harrow et al., 2017]. We believe both techniques might be of independent interest.
Meta-Learning without Memorization

The ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods. Meta-learning has emerged as a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks. However, most meta-learning algorithms implicitly require that the meta-training tasks be mutually-exclusive, such that no single model can solve all of the tasks at once. For example when creating tasks for a meta-learned N-way image classifier, we typically randomize the assignment of the image classes to N-way classification labels for each task. If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes. This requirement means that the user must take great care in designing the tasks, for example by shuffling labels or removing task identifying information from the inputs. In some domains, this makes meta-learning entirely inapplicable. In this paper, we address this challenge by designing an information-theoretic meta-regularization objective that places precedence on data-driven adaptation. This causes the meta-learner to decide what should be learned from data and what must be inferred from the input. By doing so, our algorithm can successfully use data from non-mutually-exclusive tasks to efficiently adapt to novel tasks. We demonstrate its applicability to both contextual and gradient-based meta-learning algorithms, and apply it in practical settings where standard meta-learning has been difficult to apply. Our approach substantially outperforms standard meta-learning algorithms in these settings.
DESIGN OF LIGHTWEIGHT CRYPTOGRAPHIC MODEL FOR END-TO-END ENCRYPTION IN IOT DOMAIN

Digitalization rapidly connected the entire world. Everyday an enormous volumes of digital data produced by billions of intelligent devices which requires safe transmission over internet. If we look into embedded environment, handling massive volume of data is impractical for low power and low memory devices which leads to lightweight concept. The proposed lightweight model includes many symmetric key sequentially. The model follows fiestel network structure where 64 bits input block divided by two 32 bits blocks. Then every half undergoes through various symmetric key algorithms like TE (Triangular Encryption), RPPT (Recursive Pared Parity Technique), RPSPNC( Recursive Positional Substitution on Prime-Nonprime of Cluster), TB(Transformation of Bits) and bits rotation process. A triangular bit sequence generated by TE and from there various encryption as well as decryption techniques[1] have generated by reading bits in a certain order. RPPT encrypts bits by executing logical OR of successive bits. Bit swapping technique is used by TB for encryption and decryption. RPSPNC interchanges bits on the basis of prime-non prime bit position and considers any in between bit sequence as a cipher text. Lastly two resultant sub-blocks are merged to produce cipher text of 64 bits. To check the acceptance of the proposed model, comparisons take place with popular symmetric key algorithm AES and one embedded algorithm RPPT+TB. Software parameters like entropy, n-gram(4-gram), non-homogeneity, histogram are analysed. Hardware analysis of the model ensures us that it falls into lightweight domain by comparing the GE (Gate equivalent) with the ISO /IEC standard value ranges between 1000-2000GE.
Research on Key Technologies of Logistics Information Traceability Model Based on Consortium Chain

In order to trace the logistics information and make use of the characteristics of the Consortium Blockchain, a logistics information traceability model based on the Consortium chain is proposed.The research focuses on two key technologies in the model, namely Multi-center Practical Byzantine Fault Tolerance (MCPBFT) consensus algorithm and Information Matching mechanism.In MCPBFT algorithm, many nodes involved in logistics information are divided into multiple consensus sets, and the consistency protocol is improved into two phases based on PBFT algorithm.In the information matching mechanism, the authenticity of the recorded on-chain information is ensured by matching the updated logistics information with the information stored on the chain in advance.By analyzing the two key technologies, the feasibility and superiority of these two technologies are explained.Among them, MCPBFT algorithm can effectively improve the efficiency of consensus and ensure that the logistics information can be updated in time; the information matching mechanism can be safely applied to the traceability model and improve the practicability of the traceability model.
ICLab: A Global, Longitudinal Internet Censorship Measurement Platform

Researchers have studied Internet censorship for nearly as long as attempts to censor contents have taken place. Most studies have however been limited to a short period of time and / or a few countries; the few exceptions have traded off detail for breadth of coverage. Collecting enough data for a comprehensive, global, longitudinal perspective remains challenging.In this work, we present ICLab, an Internet measurement platform specialized for censorship research. It achieves a new balance between breadth of coverage and detail of measurements, by using commercial VPNs as vantage points distributed around the world. ICLab has been operated continuously since late 2016. It can currently detect DNS manipulation and TCP packet injection, and overt "block pages" however they are delivered. ICLab records and archives raw observations in detail, making retrospective analysis with new techniques possible. At every stage of processing, ICLab seeks to minimize false positives and manual validation.Within 53,906,532 measurements of individual web pages, collected by ICLab in 2017 and 2018, we observe blocking of 3,602 unique URLs in 60 countries. Using this data, we compare how different blocking techniques are deployed in different regions and/or against different types of content. Our longitudinal monitoring pinpoints changes in censorship in India and Turkey concurrent with political shifts, and our clustering techniques discover 48 previously unknown block pages. ICLab's broad and detailed measurements also expose other forms of network interference, such as surveillance and malware injection.
Virtualidad como herramienta de apoyo a la presencialidad: Análisis desde la mirada estudiantil

The incorporation of information and communication technologies in education is characterized by the use of hypermedia, knowledge building, student-centered learning, personalization and the facilitator teacher.One of the ways of applying these technologies in higher education is virtuality as a complement to face-to-face activities.The investigative purpose of the study was to unveil the students' meanings about the incorporation of virtuality as a support tool for classroom attendance.It was an interpretive field work, carried out at the Libertador Experimental Pedagogical University of Venezuela.The semi-structured interview and the content analysis technique were used with the support of the Atlas.tiprogram.The informants were the participants of the Curriculum of the Rural Education specialty, of the 2017-I academic period.As a result, respondents perceive the incorporation of virtuality as an excellent, dynamic and innovative presence support tool.Concluding that, the active participation of students, collaborative work and communication between the group; the motivation received by the group and teacher; and overcoming limitations, they constitute the interaction that characterizes virtual work as a humanizer.
A Framework for Adversarially Robust Streaming Algorithms

We investigate the adversarial robustness of streaming algorithms. In this context, an algorithm is considered robust if its performance guarantees hold even if the stream is chosen adaptively by an adversary that observes the outputs of the algorithm along the stream and can react in an online manner. While deterministic streaming algorithms are inherently robust, many central problems in the streaming literature do not admit sublinear-space deterministic algorithms; on the other hand, classical space-efficient randomized algorithms for these problems are generally not adversarially robust. This raises the natural question of whether there exist efficient adversarially robust (randomized) streaming algorithms for these problems. In this work, we show that the answer is positive for various important streaming problems in the insertion-only model, including distinct elements and more generally $F_p$-estimation, Fp-heavy hitters, entropy estimation, and others. For all of these problems, we develop adversarially robust (1+ε)-approximation algorithms whose required space matches that of the best known non-robust algorithms up to a poly(log n, 1/ε) multiplicative factor (and in some cases even up to a constant factor). Towards this end, we develop several generic tools allowing one to efficiently transform a non-robust streaming algorithm into a robust one in various scenarios.
Federated Meta-Learning for Fraudulent Credit Card Detection

Credit card transaction fraud costs billions of dollars to card issuers every year. Besides, the credit card transaction dataset is very skewed, there are much fewer samples of frauds than legitimate transactions. Due to the data security and privacy, different banks are usually not allowed to share their transaction datasets. These problems make traditional model difficult to learn the patterns of frauds and also difficult to detect them. In this paper, we introduce a novel framework termed as federated meta-learning for fraud detection. Different from the traditional technologies trained with data centralized in the cloud, our model enables banks to learn fraud detection model with the training data distributed on their own local database. A shared whole model is constructed by aggregating locallycomputed updates of fraud detection model. Banks can collectively reap the benefits of shared model without sharing the dataset and protect the sensitive information of cardholders. To achieve the good performance of classification, we further formulate an improved triplet-like metric learning, and design a novel meta-learning-based classifier, which allows joint comparison with K negative samples in each mini-batch. Experimental results demonstrate that the proposed approach achieves significantly higher performance compared with the other state-of-the-art approaches.
Evaluating the Usable-Security of Healthcare Software Through Unified Technique of Fuzzy Logic, ANP and TOPSIS

The current research efforts in the security domain are essentially inspired by the intention to ensure both healthcare software's security and usability.Practitioners are continuously working on techniques that can optimise security while affording the ease of use.Inspite of the numerous efforts by the developers and security experts in this context, the level of usability-security is not the same as it should be.Furthermore, it is important to do more work on the most effective strategies for evaluating and increasing the usable-security of healthcare technology.Quantitative usability and security assessment is an important part of increasing the standards of software security especially with regard to software used in healthcare.This study's objective, in particular, is to evaluate the usability-security of healthcare software.Thereafter, based on the empirical analysis of comprehensive data, the study recommends the strategies that would facilitate in designing healthcare software with optimum security while preserving its' usability.Decision makers often find it difficult to integrate highly secure software with maximum usability while developing the software.Hence, this research endeavour enlists the assimilation of usability and safety with their contributory attributes.Moreover, this study proposes the use of a novel technique of combining the Analytic Network Method (ANP), Fuzzy Sets (FS), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to test usability together with security.The researchers employed the suggested methodology on six applications of the hospital management systems in Mecca, Saudi Arabia, to ascertain the utility of the FS-AHP-TOPSIS hybrid solution technique.Usability results along with security assessment corroborated that the most accurate and effective method for evaluating the usable-security of healthcare applications will be this innovative hybrid approach.Most relevantly, these findings will be useful in the security management without impacting the usability for the end users.
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal

This work considers the sample and computational complexity of obtaining an $\eps$-optimal policy in a discounted Markov Decision Process (MDP), given only access to a generative model. In this model, the learner accesses the underlying transition model via a sampling oracle that provides a sample of the next state, when given any state-action pair as input. We are interested in a basic and\nunresolved question in model based planning: is this na\ive ``plug-in'' approach --- where we build the maximum likelihood estimate of the transition model in the MDP from observations and then find an optimal policy in this empirical MDP --- non-asymptotically, minimax optimal? Our main result answers this question positively. With regards to computation, our result provides a simpler\napproach towards minimax optimal planning: in comparison to prior model-free results, we show that using \emph{any} high accuracy, black-box planning oracle in the empirical model suffices to obtain the minimax error rate. The key proof technique uses a leave-one-out analysis, in a novel ``absorbing MDP'' construction, to decouple the statistical dependency issues that arise in the analysis of model-based planning; this construction may be helpful more generally.
Modelling potentially toxic elements in forest soils with vis–NIR spectra and learning algorithms

The surface organic horizons in forest soils have been affected by air and soil pollutants, including potentially toxic elements (PTEs). Monitoring of PTEs requires a large number of samples and adequate analysis. Visible–near infrared (vis–NIR: 350–2500 nm) spectroscopy provides an alternative method to conventional laboratory measurements, which are time-consuming and expensive. However, vis–NIR spectroscopy relies on an empirical calibration of the target attribute to the spectra. This study examined the capability of vis–NIR spectra coupled with machine learning (ML) techniques (partial least squares regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and a deep learning (DL) approach called fully connected neural network (FNN) to assess selected PTEs (Cr, Cu, Pb, Zn, and Al) in forest organic horizons. The dataset consists of 2160 samples from 1080 sites in the forests over all the Czech Republic. At each site, we collected two samples from the fragmented (F) and humus (H) organic layers. The content of all PTEs was higher in horizon H compared to F horizon. Our results indicate that the reflectance of samples tended to decrease with increased PTEs concentration. Cr was the most accurately predicted element, regardless of the algorithm used. SVMR provided the best results for assessing the H horizon (R2 = 0.88 and RMSE = 3.01 mg/kg for Cr). FNN produced the best predictions of Cr in the combined F + H layers (R2 = 0.89 and RMSE = 2.95 mg/kg) possibly due to the larger number of samples. In the F horizon, the PTEs were not predicted adequately. The study shows that PTEs in forest soils of the Czech Republic can be accurately estimated with vis–NIR spectra and ML approaches. Results hint in availability of a large sample size, FNN provides better results.
“Now i can see me” designing a multi-user virtual reality remote psychotherapy for body weight and shape concerns

Recent years have seen a growing research interest towards designing computer-assisted health interventions aiming to improve mental health services.Digital technologies are becoming common methods for diagnosis, therapy, and training.With the advent of lower-cost VR head-mounteddisplays (HMDs) and high internet data transfer capacity, there is a new opportunity for applying immersive VR tools to augment existing interventions.This study is among the first to explore the use of a Multi-User Virtual Reality (MUVR) system as a therapeutic medium for participants at high-risk for developing Eating Disorders.This paper demonstrates the positive effect of using MUVR remote psychotherapy to enhance traditional therapeutic practices.The study capitalises on the opportunities which are offered by a MUVR remote psychotherapeutic session to enhance the outcome of Acceptance and Commitment Therapy, Play Therapy and Exposure Therapy for sufferers with body shape and weight concerns.Moreover, the study presents the design opportunities and challenges of such technology, while strengths on the feasibility, and the positive user acceptability of introducing MUVR to facilitate remote psychotherapy.Finally, the appeal of using VR for remote psychotherapy and its observed positive impact on both therapists and participants is discussed.
Privacy-preserving using homomorphic encryption in Mobile IoT systems

The data privacy concerns are increasingly affecting the Internet of things (IoT) and artificial intelligence (AI) applications, in which it is very challenging to protect the privacy of the underlying data. In recent, the advancements in the performances of homomorphic encryption (HE) make it possible to help protect sensitive and personal data in IoT applications using homomorphic encryption based schemes. This paper proposed a practical homomorphic encryption scheme that can enable data users in IoT systems to securely operate data over encrypted data, which can effectively protect the privacy of key data in the system. The experimental results demonstrated the effectiveness proposed scheme.
What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encoding

In recent years, pre-trained Transformers have dominated the majority of NLP benchmark tasks. Many variants of pre-trained Transformers have kept breaking out, and most focus on designing different pre-training objectives or variants of self-attention. Embedding the position information in the self-attention mechanism is also an indispensable factor in Transformers however is often discussed at will. Hence, we carry out an empirical study on position embedding of mainstream pre-trained Transformers mainly focusing on two questions: 1) Do position embeddings really learn the meaning of positions? 2) How do these different learned position embeddings affect Transformers for NLP tasks? This paper focuses on providing a new insight of pre-trained position embeddings by feature-level analysis and empirical experiments on most of iconic NLP tasks. It is believed that our experimental results can guide the future works to choose the suitable positional encoding function for specific tasks given the application property.