Reviewerly/exHarmony huggingface dataset:
Implementing the NIS Directive, driving cybersecurity improvements for Essential Services

A review by the National Audit Office of the National Cyber Security Programme recommended a more robust performance framework, to understand the impact of the Programme and to focus activities going forward. The Directive on security of network and information systems (the NIS Directive) has placed responsibility for essential aspects of supply chains on Operators of Essential Services (OES). Our dependence on international supply chains also requires a performance framework to assist cybersecurity improvements in this area. The following sections describe work to investigate the implementation of the NIS Directive by Competent Authorities (CA) and OES and proposes a framework to monitor performance across interdependencies. This is to enable development of a more effective set of performance metrics to guide interventions and improvements in cybersecurity for critical infrastructure.
A Single-Shot, Pixel Encoded 3D Measurement Technique for Structure Light

The Structure Light System (SLS) is a general concept and it is one of the cheapest methods for the non-contact-based 3D reconstruction.The existing single-shot SLS which is primarily based on the spatial encoding techniques are not optimal in terms of resolution and digitally encoded patterns.Those schemes are not flexible, controllable, and designed up to the level of the pixel.So, to increase the resolution and to implement a flexible controllable pattern we proposed a novel heuristic method based on the spatial neighborhood.In this paper, we propose a multi-resolution SLS which can be implemented with a set of 25 geometrical shaped distinct symbols or alphabets to use in the projection pattern as shape primitive.The size of each symbol is well defined in pixels which enabled us to have access and control up to the full resolution of the projector.The shape descriptive parameters for each symbol or alphabet are also defined and computed.To spread the alphabets in a controllable manner, a method is defined to generate a robust pseudo-random sequence of any required size with a certain number of alphanumeric bases, to be employed in the projection pattern concerning the measured resolution.This arrangement will enable us to design the projection patterns according to the required surface area and the resolution.A new technique is developed for the decoding of the captured image pattern.The decoding process depends upon the classification of symbols which is based on shape descriptive parameters.The searching in the neighborhood of a symbol is carried out through computing the location information, grid distance, and direction information to find the codewords which are used to establish the correspondence.
Reading the written language environment: Learning orthographic structure from statistical regularities

Statistical regularities in the environment impact cognition across domains. In semantics, distributional approaches posit that similarity between words can be derived from regularities of the contexts in which they appear. Here, we study how regularities in written text impact readers’ knowledge about orthography: Can similarity between characters be learned from the written environment? Adapting methods from distributional semantics, we model the contextual similarity among alphanumeric characters in a large text corpus. We find modest correlations between model-derived similarities with similarity derived from a behavioral experiment. Beyond this result, model-derived similarity from neural embedding models captures key aspects of orthographic knowledge, like case, letter identity and consonant–vowel status. We conclude that the text environment contains regularities that are relevant to readers and that statistical learning is a promising way for this information to be acquired. More broadly, our results imply that statistical regularities are relevant not only at the level of word semantics but also individual written characters.
Improved Training of Spectral Normalization Generative Adversarial Networks

In order to stabilize the training of generative adversarial networks, several recent works advocate spectral normalization in the discriminator. However, the method ignores the influence of the generator, and the quality of the images generated in practice is unstable. We propose L2 norm regularization in the generator based on the spectral normalization, which can solve the above shortcomings. Our method directly makes the generated data close to real data in Euclidean space, and indirectly helps the spectral normalization achieve tighter Lipschitz constraint during the training of generative adversarial networks. Our experiments on CIFAR-10 and STL-10 dataset confirm that our method can not only stable the quality of the images generated by spectral normalization, but also improve the quality of generated images.
You've Got a Friend in Me: Children and Search Agents

Children turn to online search tools to complete classroom-related assignments. Unfortunately, these tools are rarely explicitly tailored to meet children's needs and skills. We seek to design a Search Agent (SA) for children in the context of education; one that can foster natural, effective and rewarding interactions in the educational context. To do so, we build on previous explorations of children's interactions with search tools and study how they imagine a SA that can help with assignments required online quests: What are the pragmatic and hedonistic qualities of such SA? Also, do previous experience and familiarity with search technology affect children's preferences? We involved in our study children ages 10-11 and inquired on factors that define their overall experience with a SA. We outline the steps of our research and the emerging lessons learned so that they can guide the design of a SA.
Using neural-network based paragraph embeddings for the calculation of within and between document similarities

Science mapping using document networks comes often with the implicit assumption that scientific papers are indivisible units with unique links to neighbour documents. Research on proximity in co-citation analysis and the study of lexical properties of sections and citation contexts indicate that this assumption doesn't always hold. Moreover, the meaning of words and co-words depends on the context in which they appear. This study proposes the use of a neural network architecture for word and paragraph embeddings (Doc2Vec) for the measurement of similarity among those smaller units of analysis. It is shown that paragraphs in the "Introduction" and the "Discussion" Section are more similar to the abstract, that the similarity among paragraphs is related to -but not linearly- the distance between the paragraphs. The "Methodology" Section is least similar to the other sections. Abstracts of citing-cited documents are more similar than random pairs and the context in which a reference appears is most similar to the abstract of the cited document. This novel approach with higher granularity can be used for bibliometric aided retrieval and to assist in measuring interdisciplinarity through the application of network-based centrality measures.
A distributed networked system for secure publicly verifiable self-tallying online voting

For successful electronic voting (e-voting), it is critically important to ensure the security, privacy and verifiability of all steps. This paper uses Intel Software Guard Extensions (SGX) to achieve these goals and provide protection against malicious adversaries with administrative access. We introduce a Distributed SGX Networked System (DSGXNS). It connects one or several SGXs and the bulletin board of the election. We propose and investigate a DSGXNS-based secure, publicly verifiable and self-tallying online voting protocol. Each cast vote is uploaded to the DSGXNS via a secure communication channel built after a remote attestation. The validity of submissions is verified inside the SGX enclaves and legitimate votes are encrypted there. For end-to-end voter verification, a new encryption mechanism is proposed. It prevents confidentiality breaches and enables everyone to verify the validity of submissions and the outcome, which can be self-tallied using a homomorphic property. It maintains confidentiality because even the system administrators or malicious adversaries gaining administrative access cannot read data in the SGX enclaves. Every voter can verify all steps of the election and tally the cast ballots. Our theoretical analysis and experiments show that the protocol is secure and efficient.
Towards robust BOS measurements for axisymmetric flows

Abstract Accurately reconstructing a radial refractive index ( n ) field is challenging in axisymmetric background oriented Schlieren (BOS) measurement. In this study, we systematically investigated several widely adopted inversion algorithms in BOS applications. To quantitatively assess the performance of each algorithm, a synthetic experiment mimicking a helium jet discharged into ambient air was established to provide the reference. Relying on the necessity to solve a Poisson equation for a line-of-sight projected variable, tested algorithms were categorized into two groups: direct and indirect. In the direct approach, the algorithm is applied directly to the light deflection angle ( $$\varepsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ε</mml:mi> </mml:math> ) to reconstruct the radial $$\delta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>δ</mml:mi> </mml:math> field, defined as $$\delta =(n-n_{0})/n_{0}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>δ</mml:mi> <mml:mo>=</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> <mml:msub> <mml:mi>n</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>/</mml:mo> <mml:msub> <mml:mi>n</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:mrow> </mml:math> where $$n_{0}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>n</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> is the reference refractive index. In the indirect group, the Poisson equation is solved first. Then, an inversion algorithm is subsequently applied to the projected $${{\overline{\delta }}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mover> <mml:mi>δ</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> </mml:math> to obtain $$\delta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>δ</mml:mi> </mml:math> in the radial plane. The two approaches were compared with the synthetic experiment both using the adaptive Fourier–Hankel methods (AFH). The comparison showed that at the cost of introducing the additional step of solving the Poisson equation, the indirect approach performed more accurately when noises were present in the $$\varepsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ε</mml:mi> </mml:math> measurements. To identify the proper inversion algorithm suitable for the indirect approach, we further compared four types of algorithms in the synthetic experiments including AFH, onion peeling (OP), three-point Abel (TPA), and filtered back projection tomography (FBPT). The results showed that TPA had the best performance in terms of the reconstruction accuracy with noisy $$\varepsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ε</mml:mi> </mml:math> data. Finally, experiments on axisymmetric helium jets were conducted to confirm the effectiveness of the proposed TPA algorithm in the indirect approach. Graphic Abstract
The Effect of the Degree of Anxiety of Learners during the Use of VR on the Flow and Learning Effect

Virtual reality (VR) learning content that provides negative experiences makes learners anxious. Thus, experimental research was conducted to determine how anxiety felt by learners using VR impacts learning. To measure the learning effects, flow, a leading element of learning effects, was measured. Flow has a positive effect on learning as a scale of how immersed an individual is in the work he or she is currently performing. The evaluation method used the empirical recognition scale by Kwon (2020) and the six-item short-form State-Trait Anxiety Inventory (STAI) from Marteau and Becker (1992), which were used in the preceding study. The difference in flow between high- and low-anxiety groups was explored by measuring the degree the study participants felt using an Fire Safety Education Game based on VR that allows learners to feel the heat and wind of the fire site with their skin. As a result of the experiment, no difference in flow was found between the high- and low-anxiety groups that played the same VR game with cutaneous sensation. However, the high-anxiety group who played the VR game with cutaneous sensation showed a higher flow than the group that played the basic fire safety education VR game. Based on these results, the following conclusions were drawn: the closer to reality the VR learning and training system for negative situations is reproduced, the more realistically the learner feels the anxiety. In other words, the closer to reality the virtual environment is reproduced, the more realistically the learner feels the feelings in the virtual space. In turn, through this realistic experience, the learner becomes immersed in the flow more deeply. In addition, considering that flow is a prerequisite for the learning effect, the anxiety that learners feel in the virtual environment will also have a positive effect on the learning effect. As a result, it can be assumed that the more realistically VR is reproduced, the more effective experiential learning using VR can be.
Learning to Accelerate Decomposition for Multi-Directional 3D Printing

Multi-directional 3D printing has the capability of decreasing or eliminating the need for support structures. Recent work proposed a beam-guided search algorithm to find an optimized sequence of plane-clipping, which gives volume decomposition of a given 3D model. Different printing directions are employed in different regions to fabricate a model with tremendously less support (or even no support in many cases). To obtain optimized decomposition, a large beam width needs to be used in the search algorithm, leading to a very time-consuming computation. In this letter, we propose a learning framework that can accelerate the beam-guided search by using a smaller number of the original beam width to obtain results with similar quality. Specifically, we use the results of beam-guided search with large beam width to train a scoring function for candidate clipping planes based on six newly proposed feature metrics. With the help of these feature metrics, both the current and the sequence-dependent information are captured by the neural network to score candidates of clipping. As a result, we can achieve around 3× computational speed. We test and demonstrate our accelerated decomposition on a large dataset of models for 3D printing.
MINE: A framework for dynamic regressor selection

Dynamic Regressor Selection (DRS) techniques aim to select the most competent regressors from an ensemble per test pattern. So, for each test pattern, only a subset of the most competent regressors are used to estimate its target value. Hence, the central issue in DRS techniques is how to define the competence of the regressors which is usually defined using a single measure, such as the performance of the regressor in the local region of the feature space around the test pattern, called the region of competence. However, no single measure is the best for any task. In this work, we present the Meta INtEgration (MINE) framework that selects and combines the most competent regressors from an ensemble during the evaluation of a given test pattern. MINE uses different measures extracted from the region of competence as a criterion for the selection and combination of the regressors. In contrast to traditional combination schemes where all the regressors are weighted to produce the final answer, MINE selects the best regressors per pattern on-the-fly and combines them to predict the value of the test pattern. Comprehensive experiments on 20 regression datasets show that MINE compares favorably to literature techniques.
Nonlinear Optics with Near-Infrared Excitation Enable Real-Time Quantitative Diagnosis of Human Cervical Cancers

Abstract Histopathologic analysis through biopsy has been one of the most useful methods for the assessment of malignant neoplasms. However, some aspects of the analysis such as invasiveness, evaluation range, and turnaround time from biopsy to report could be improved. Here, we report a novel method for visualizing human cervical tissue three-dimensionally, without biopsy, fixation, or staining, and with sufficient quality for histologic diagnosis. Near-infrared excitation and nonlinear optics were employed to visualize unstained human epithelial tissues of the cervix uteri by constructing images with third-harmonic generation (THG) and second-harmonic generation (SHG). THG images enabled evaluation of nuclear morphology in a quantitative manner with six parameters after image analysis using deep learning. It was also possible to quantitatively assess intraepithelial fibrotic changes based on SHG images and another deep learning analysis. Using each analytical procedure alone, normal and cancerous tissue were classified quantitatively with an AUC ≥0.92. Moreover, a combinatory analysis of THG and SHG images with a machine learning algorithm allowed accurate classification of three-dimensional image files of normal tissue, intraepithelial neoplasia, and invasive carcinoma with a weighted kappa coefficient of 0.86. Our method enables real-time noninvasive diagnosis of cervical lesions, thus constituting a potential tool to dramatically change early detection. Significance: This study proposes a novel method for diagnosing cancer using nonlinear optics, which enables visualization of histologic features of living tissues without the need for any biopsy or staining dye.
Toward fault-tolerant and secure frequent itemset mining outsourcing in hybrid cloud environment

Due to the rising costs of maintaining IT infrastructures for large-scale data mining, it is becoming a trend for data owners to outsource data mining tasks together with storage to cloud service providers, however, which also arouses security concerns on unauthorized breaches of data confidentiality and result integrity. Existing solutions yet seldom protect data privacy whilst guaranteeing result integrity. To address these issues, this paper proposes a series of privacy-preserving building blocks by employing Shamir’s secret sharing scheme. Based on those subprotocols, an efficient frequent itemset mining protocol is designed under hybrid cloud setting, in which the public unreliable cloud and semi-trusted cloud cooperate to mine frequent patterns over the encrypted database. Our scheme not only protects the privacy of datasets from frequency analysis attack, but also verifies the integrity of mining results. Theoretical analysis demonstrates that the scheme ensures security as well as fault tolerance under our threat model. Experimental evaluations show that our proposed protocol outperforms the similar solution regarding efficiency while it can detect and correct cloud servers’ errors effectively.
Alleviating the Burden of Labeling: Sentence Generation by Attention Branch Encoder–Decoder Network

Domestic service robots (DSRs) are a promising solution to the shortage of home care workers. However, one of the main limitations of DSRs is their inability to interact naturally through language. Recently, data-driven approaches have been shown to be effective for tackling this limitation; however, they often require large-scale datasets, which is costly. Based on this background, we aim to perform automatic sentence generation of fetching instructions: for example, “Bring me a green tea bottle on the table.” This is particularly challenging because appropriate expressions depend on the target object, as well as its surroundings. In this letter, we propose the attention branch encoder-decoder network (ABEN), to generate sentences from visual inputs. Unlike other approaches, the ABEN has multimodal attention branches that use subword-level attention and generate sentences based on subword embeddings. In experiments, we compared the ABEN with a baseline method using four standard metrics in image captioning. Results show that the ABEN outperformed the baseline in terms of these metrics.
Fraud Detection for Credit Card Transactions Using Random Forest Algorithm

In these days, credit card fraud detection is a major concern in the society. The use of credit cards in e-commerce sites and various banking sites has been increased rapidly in recent times. As modernization will have both positive and negative impacts, the use of credit cards in online transactions has made it simple; likewise, it also led to the increase of the number of fraud transactions. As part of the activities happening, it is always advised for the e-commerce sites and the banks to have automatic fraud detection system. Credit card fraud might result in huge financial losses. While look for the solutions for credit card frauds that are happening, machine learning techniques provide favorable solutions. The proposed system uses a random forest application in solving the problem and to attain more accuracy when compared to the other algorithms used till now. All the basic classifiers have the same weight but random forest algorithm has relatively high and others have relatively low weights because of the randomization of bootstrap sampling of a making decision and selection of attributes cannot guarantee that all of them have the same stability in decision making.
DEAL: Deep Evidential Active Learning for Image Classification

Hemmer, Patrick Kühl, Niklas Schöffer, Jakob Convolutional Neural Networks (CNNs) have proven to be state-of-the-art models for supervised computer vision tasks, such as image classification. However, large labeled datasets are generally needed for the training and validation of such models. In many domains, unlabeled data is available but labeling is expensive, for instance, when specific expert knowledge is required. Active Learning (AL) is one approach to mitigate the problem of limited labeled data. Through selecting the most informative and representative data instances for labeling, AL can contribute to more efficient learning of a model. Recent AL methods for CNNs propose different solutions for the selection of to-be-labeled instances. However, they do not perform consistently well and are often computationally expensive. In this paper, we propose a novel AL algorithm that efficiently learns from unlabeled data by capturing high prediction uncertainty. By replacing the softmax standard output of a CNN with the parameters of a Dirichlet density, the model learns to identify data instances that contribute efficiently to improving model performance during training. We demonstrate in several experiments with publicly available data that our method consistently outperforms other state-of-the-art AL approaches. It can be easily implemented and does not require extensive computational resources for training. Additionally, we are able to show the benefits of the approach on the real-world medical use case of pneumonia detection in chest X-ray images.