Reviewerly/exHarmony huggingface dataset:
Single-shot optical multiple-image encryption by jointly using wavelength multiplexing and position multiplexing

A single shot large-capacity optical multiple-image encryption method based on wavelength multiplexing and position multiplexing is proposed. In the encryption process of the proposed method, multiple plane waves of different wavelengths are adopted to illuminate secret images that are placed at different positions along the optical axis. All the secret images are encoded into a single grayscale intensity-only image that is recorded by a monochromic camera by applying a diffractive-imaging based double random phase encoding (DRPE) system. In the decryption process, high accuracy images are decrypted without crosstalk from the intensity image through a multimode phase retrieval algorithm and a two-step iterative shrinkage/thresholding (TwIST) algorithm. The feasibility of the proposed method is demonstrated by numerical simulations.
A Secure Framework for Communication in Internet of Things Application using Hyperledger based Blockchain

In the era of the Internet of Things(loT) smart devices are connected with wire or wireless way. The IoT devices are capable of sensing the environment and has the ability to transmit that information to the next level. The application area of IoT is Smart city, Smart transportation, Healthcare sector, Agriculture, Monitoring environment. Each of these applications, lots of information are share or transmit among different devices. In the information sharing system among devices, lots of security and privacy challenges exist like data leakage, data modification, device identity. In this paper, authors firstly identify the communication protocols used in IoT application and given their working principle. Secondly, challenges exist in IoT and corresponding Blockchain solution approach are explained, Lastly, the authors proposed a secure architecture based on open Blockchain which can solve some of the challenges in IoT applications.
Bengali abstractive text summarization using sequence to sequence RNNs

Text summarization is one of the leading problem of natural language processing and deep learning in recent years. Text summarization contains a condensed short note on a large text document. Our purpose is to create an efficient and effective abstractive Bengali text summarizer what can generate an understandable and meaningful summary from a given Bengali text document. To do this we have collected various texts such as newspaper articles, Facebook posts etc. and to generate summary from those text we will be using our model. Our model works with bi-directional RNNs with LSTM in encoding layer and attention model at decoding layer. Our model works as sequence to sequence model to generate summary. There are some challenges we have faced while building this model such as text pre-processing, vocabulary counting, missing words counting, word embedding, unknown words find out and so on. In this model, our main goal was to make an abstractive summarizer and reduce the train loss of that. During our research experiment, we have successfully reduced the train loss to 0.008 and able to generate a fluent short summary note from a given text.
Autonomous Exploration and Map Construction of a Mobile Robot Based on the TGHM Algorithm

An a priori map is often unavailable for a mobile robot in a new environment. In a large-scale environment, relying on manual guidance to construct an environment map will result in a huge workload. Hence, an autonomous exploration algorithm is necessary for the mobile robot to complete the exploration actively. This study proposes an autonomous exploration and mapping method based on an incremental caching topology–grid hybrid map (TGHM). Such an algorithm can accomplish the exploration task with high efficiency and high coverage of the established map. The TGHM is a fusion of a topology map, containing the information gain and motion cost for exploration, and a grid map, representing the established map for navigation and localization. At the beginning of one exploration round, the method of candidate target point generation based on geometry rules are applied to extract the candidates quickly. Then, a TGHM is established, and the information gain is evaluated for each candidate topology node on it. Finally, the node with the best evaluation value is selected as the next target point and the topology map is updated after each motion towards it as the end of this round. Simulations and experiments were performed to benchmark the proposed algorithm in robot autonomous exploration and map construction.
The practice of self-citations: a longitudinal study

In this article, we discuss the outcomes of an experiment where we analysed whether and to what extent the introduction, in 2012, of the new research assessment exercise in Italy (a.k.a. Italian Scientific Habilitation) affected self-citation behaviours in the Italian research community. The Italian Scientific Habilitation attests to the scientific maturity of researchers and in Italy, as in many other countries, is a requirement for accessing to a professorship. To this end, we obtained from ScienceDirect 35,673 articles published from 1957 and 2016 by the participants to the 2012 Italian Scientific Habilitation, that resulted in the extraction of 1,379,050 citations retrieved through Semantic Publishing technologies. Our analysis showed an overall increment in author self-citations (i.e. where the citing article and the cited article share at least one author) in several of the 24 academic disciplines considered. However, we depicted a stronger causal relation between such increment and the rules introduced by the 2012 Italian Scientific Habilitation in 10 out of 24 disciplines analysed.
Multitask-based association rule mining

Recently, there has been a growing interest in association rule mining (ARM) in various fields. However, standard ARM algorithms fail to discover rules for multitask problems as they do not consider task-oriented investigation and, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to jointly discover rules by considering multiple tasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of the proposed approach, highly frequent local rules (single-task rules) are explored for each task separately and then these local rules are combined to produce the global result (multitask rules) using a majority voting mechanism. Experiments were conducted on four different real-world multitask learning datasets. The experimental results indicated that the proposed MTARM approach discovers more information than that of traditional ARM algorithms by jointly considering the relationships among multiple tasks.
A Suitable AST Node Granularity and Multi-Kernel Transfer Convolutional Neural Network for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a feasible way to perform software defect prediction (SDP) when lacking historical data. Recent CPDP approaches have employed deep learning techniques to better exploit the information from the program's abstract syntax trees (ASTs). However, the granularity of the AST nodes and the data distribution difference between projects may have negative impacts on the prediction performance, which many CPDP studies didn't take into consideration. To handle these issues, this paper explores a better AST node granularity and proposes a CPDP framework based on multi-kernel transfer convolutional neural networks. Specifically, for AST node granularity, we explore the difference of three AST node granularities and then compare the prediction performance of each granularity on several prediction models. For the CPDP framework, we first parse the program source code into ASTs and then encode the AST nodes into numerical vectors using the embedding technique. Secondly, to mine transferable semantic features, the encoded ASTs are fed into a convolutional neural network, in which a multi-kernel matching layer is added to minimize the data distribution divergence between the source and target project. Finally, to make use of the information from the handcrafted features, the semantic features mined from the AST are joint with handcrafted features to form the joint features for CPDP. We evaluate our approach on 110 CPDP tasks formed by 11 open-source projects and results show that the proposed CPDP method outperforms most deep learning-based approaches.
Pseudo Random Number Generation: a Reinforcement Learning approach

Pseudo-Random Numbers Generators (PRNGs) are algorithms produced to generate long sequences of statistically uncorrelated numbers, i.e. Pseudo-Random Numbers (PRNs). These numbers are widely employed in mid-level cryptography and in software applications. Test suites are used to evaluate PRNGs quality by checking statistical properties of the generated sequences. Machine learning techniques are often used to break these generators, i.e. approximating a certain generator or a certain sequence using a neural network. But what about using machine learning to generate PRNs generators? This paper proposes a Reinforcement Learning (RL) approach to the task of generating PRNGs from scratch by learning a policy to solve an N-dimensional navigation problem. In this context, N is the length of the period of the sequence to generate and the policy is iteratively improved using the average score of an appropriate test suite run over that period. Aim of this work is to demonstrate the feasibility of the proposed approach, to compare it with classical methods, and to lay the foundation of a research path which combines RL and PRNGs.
IOTA-Based Access Control Framework for the Internet of Things

With the rapid dissemination of the Internet of Things (IoT), the number of resources deployed in IoT systems such as devices and data is growing explosively. Since IoT systems often handle private information, it is essential to enforce appropriate access control to prevent unauthorized access. However, conventional access control schemes in which access rights are stored in a centralized server are prone to load concentration and a single point of failure. Although distributed access control schemes leveraging blockchain technologies have been proposed to deal with such problems, they inherit the drawbacks of the blockchain technologies, such as high transaction fee and low throughput. This paper proposes a novel access control framework based on IOTA, an emerging distributed ledger technology which enables free micro transactions with high throughput. This framework provides scalable and fine-grained access control by encrypting access rights using the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) technology and storing them on the distributed ledger of IOTA, called Tangle.
Nucleus Segmentation of Cervical Cytology Images Based on Depth Information

In cervical cancer screening, accurate segmentation of cervical nucleus is a key part in the early diagnosis of cervical cancer. However, the cervical nucleus segmentation faces many great challenges owing to the overlapping cervical cells, uneven staining and poor contrast of cervical cytology smear images. In this paper, a tree domain structure and screening algorithm based on depth-first searching strategy are proposed to obtain candidate nucleus regions according to the annular clustering characteristics of nucleus depth information in cervical cytology images. Then, the candidate nucleus regions are finely segmented with an iterative level set algorithm based on adaptive radius morphological dilation. Experimental results are evaluated on the ISBI2015 public dataset. The performance of the proposed nucleus segmentation algorithm is higher than that of the state-of-the-art methods in terms of positive predictive value, negative predictive value, precision, recall of the cervical nucleus segmentation.
A perceptual model of motion quality for rendering with adaptive refresh-rate and resolution

Limited GPU performance budgets and transmission bandwidths mean that real-time rendering often has to compromise on the spatial resolution or temporal resolution (refresh rate). A common practice is to keep either the resolution or the refresh rate constant and dynamically control the other variable. But this strategy is non-optimal when the velocity of displayed content varies. To find the best trade-off between the spatial resolution and refresh rate, we propose a perceptual visual model that predicts the quality of motion given an object velocity and predictability of motion. The model considers two motion artifacts to establish an overall quality score: non-smooth (juddery) motion, and blur. Blur is modeled as a combined effect of eye motion, finite refresh rate and display resolution. To fit the free parameters of the proposed visual model, we measured eye movement for predictable and unpredictable motion, and conducted psychophysical experiments to measure the quality of motion from 50 Hz to 165 Hz. We demonstrate the utility of the model with our on-the-fly motion-adaptive rendering algorithm that adjusts the refresh rate of a G-Sync-capable monitor based on a given rendering budget and observed object motion. Our psychophysical validation experiments demonstrate that the proposed algorithm performs better than constant-refresh-rate solutions, showing that motion-adaptive rendering is an attractive technique for driving variable-refresh-rate displays.
How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context

Recently semantic parsing in context has received a considerable attention, which is challenging since there are complex contextual phenomena. Previous works verified their proposed methods in limited scenarios, which motivates us to conduct an exploratory study on context modeling methods under real-world semantic parsing in context. We present a grammar-based decoding semantic parser and adapt typical context modeling methods on top of it. We evaluate 13 context modeling methods on two large complex cross-domain datasets, and our best model achieves state-of-the-art performances on both datasets with significant improvements. Furthermore, we summarize the most frequent contextual phenomena, with a fine-grained analysis on representative models, which may shed light on potential research directions. Our code is available at https://github.com/microsoft/ContextualSP.
Quantum phase transition and spontaneous symmetry breaking in a nonlinear quantum Rabi model

The experimental advance on light–matter interaction into strong couplings has invalidated the Jaynes–Cummings model and brought the quantum Rabi model (QRM) to more relevance. The QRM only involves linear coupling via a single-photon process (SPP), while the nonlinear two-photon process (TPP) is weaker and conventionally neglected. However, we find a contrary trend that enhancing the linear coupling might not suppress the nonlinear effect more but backfire to trigger some collapse of linear characters. Indeed, in strong SPP couplings a tiny strength of the TPP may dramatically change properties of the system, like a symmetry spontaneous breaking. By extracting the ground-state phase diagram including both a SPP and TPP, we find the TPP in low frequency limit induces a quantum phase transition with continuity–discontinuity double faces, which split into two distinct transitions at finite frequencies and yields a triple point. Our analysis unveils a subtle SPP–TPP entanglement.
Cautious Reinforcement Learning with Logical Constraints

This paper presents the concept of an adaptive safe padding that forces Reinforcement Learning (RL) to synthesise optimal control policies while ensuring safety during the learning process. Policies are synthesised to satisfy a goal, expressed as a temporal logic formula, with maximal probability. Enforcing the RL agent to stay safe during learning might limit the exploration, however we show that the proposed architecture is able to automatically handle the trade-off between efficient progress in exploration (towards goal satisfaction) and ensuring safety. Theoretical guarantees are available on the optimality of the synthesised policies and on the convergence of the learning algorithm. Experimental results are provided to showcase the performance of the proposed method.
Social and health care educators' perceptions of competence in digital pedagogy: A qualitative descriptive study

Digitalisation has made digital competence a necessity for those working in social and healthcare. A high degree of competence in digital pedagogy is required of educators to meet the challenge of educating future professionals who are themselves highly digitally competent. The aim of this study was to describe the perceptions of competence in digital pedagogy that educators in social and healthcare have. A qualitative descriptive study. The participants were Finnish-speaking social and healthcare educators (n = 37) working at six Finnish universities of applied sciences (UAS). Group interviews (n = 12) were conducted during spring 2018. Each group consisted of 2–5 educators, with a total of 37 educators. The data was analysed using an inductive content analysis. According to the interviewed educators, competence in digital pedagogy involved pedagogical, digital, and ethical skills and awareness. The educators were aware of the possibilities afforded by digital technology and had a positive view on how the technology could be utilised in education. However, the educators were concerned that technology might solely be utilised for the sake of digitalisation instead of being pedagogically preferable. In the future, the results of this study can be utilised while developing an instrument to evaluate the level of competence in digital pedagogy. Examining the perceptions of the educators will allows us to better understand the phenomena from the educators' point of view.
Single-pixel compressive optical image hiding based on conditional generative adversarial network

We present a deep learning (DL) framework based on a conditional generative adversarial network (CGAN) to perform compressive optical image hiding (COIH) with a single-pixel detector. An end-to-end compressive sensing generative adversarial network (eCSGAN) is developed, achieving the approximate equivalent model of an inverse system of a nonlinear COIH model, to reconstruct two-dimensional secret images directly from real acquired one-dimensional compressive sampling signals without the need of any security keys of the COIH system such as the sequence of illumination patterns, the host image, etc. Moreover, detailed comparisons between the image reconstructed using eCSGAN and compressive sensing (CS) shows that the proposed method can remarkably increase the quality in image reconstruction with a lower sampling rate. The feasibility and security of the proposed method are demonstrated by the numerical simulations and optical experiment results.
The top programming languages: Our latest rankings put Python on top-again - [Careers]

It would be an understatement to say it's been a turbulent year since the last time IEEE Spectrum broke out the digital measuring tools to probe the relative popularity of programming languages. Yet one thing remains constant: the dominance of Python.
Private Mean Estimation of Heavy-Tailed Distributions

We give new upper and lower bounds on the minimax sample complexity of differentially private mean estimation of distributions with bounded $k$-th moments. Roughly speaking, in the univariate case, we show that $$n = \Theta\left(\frac{1}{\alpha^2} + \frac{1}{\alpha^{\frac{k}{k-1}}\varepsilon}\right)$$ samples are necessary and sufficient to estimate the mean to $\alpha$-accuracy under $\varepsilon$-differential privacy, or any of its common relaxations. This result demonstrates a qualitatively different behavior compared to estimation absent privacy constraints, for which the sample complexity is identical for all $k \geq 2$. We also give algorithms for the multivariate setting whose sample complexity is a factor of $O(d)$ larger than the univariate case.
Feedback-driven semi-supervised synthesis of program transformations

While editing code, it is common for developers to make multiple related repeated edits that are all instances of a more general program transformation. Since this process can be tedious and error-prone, we study the problem of automatically learning program transformations from past edits, which can then be used to predict future edits. We take a novel view of the problem as a semi-supervised learning problem: apart from the concrete edits that are instances of the general transformation, the learning procedure also exploits access to additional inputs (program subtrees) that are marked as positive or negative depending on whether the transformation applies on those inputs. We present a procedure to solve the semi-supervised transformation learning problem using anti-unification and programming-by-example synthesis technology. To eliminate reliance on access to marked additional inputs, we generalize the semi-supervised learning procedure to a feedback-driven procedure that also generates the marked additional inputs in an iterative loop. We apply these ideas to build and evaluate three applications that use different mechanisms for generating feedback. Compared to existing tools that learn program transformations from edits, our feedback-driven semi-supervised approach is vastly more effective in successfully predicting edits with significantly lesser amounts of past edit data.
PrivColl: Practical Privacy-Preserving Collaborative Machine Learning

Collaborative learning enables two or more participants, each with their own training dataset, to collaboratively learn a joint model. It is desirable that the collaboration should not cause the disclosure of either the raw datasets of each individual owner or the local model parameters trained on them. This privacy-preservation requirement has been approached through differential privacy mechanisms, homomorphic encryption (HE) and secure multiparty computation (MPC), but existing attempts may either introduce the loss of model accuracy or imply significant computational and/or communicational overhead. In this work, we address this problem with the lightweight additive secret sharing technique. We propose PrivColl, a framework for protecting local data and local models while ensuring the correctness of training processes. PrivColl employs secret sharing technique for securely evaluating addition operations in a multiparty computation environment, and achieves practicability by employing only the homomorphic addition operations. We formally prove that it guarantees privacy preservation even though the majority ( $$n-2$$ out of n) of participants are corrupted. With experiments on real-world datasets, we further demonstrate that PrivColl retains high efficiency. It achieves a speedup of more than 45X over the state-of-the-art MPC-/HE-based schemes for training linear/logistic regression, and 216X faster for training neural network.