Reviewerly/exHarmony huggingface dataset:
RastrOS Project: Natural Language Processing contributions to the development of an eye-tracking corpus with predictability norms for Brazilian Portuguese

This article presents RastrOS, a new eye-tracking corpus of eye movement data from university students during silent reading of paragraphs of texts in Brazilian Portuguese (BP). The article shows the potential of the corpus for natural language processing (NLP) using it to evaluate the sentence complexity prediction task in BP and it also focuses on the description of NLP resources and methods developed to create the corpus. Specifically, we present: (i) the method used to select the corpus paragraphs from large corpora, using linguistic metrics and clustering algorithms; (ii) the platform for collecting the Cloze test, which is also responsible for creating the project datasets, and (iii) the hybrid semantic similarity method, based on word embedding models and contextualised word representations, used to generate semantic predictability norms. RastrOS can be downloaded from the open science framework repository with the computational infrastructure mentioned above. Datasets with predictability norms of 393 participants and eye-tracking data of 37 participants are available in the OSF repository for this work (https://osf.io/9jxg3/).
Utilization prediction-based VM consolidation approach

Reducing energy consumption and optimizing resource usage in large cloud data centers is still an essential target for the current researchers and cloud providers. The state-of-the-art highlights the effectiveness of VM consolidation and live migrations in achieving reasonable solutions. However, most proposals consider only the real-time workload variations to decide whether a host is overloaded or underloaded, or to trigger migration actions. Such approaches may apply frequent and needless VM migrations leading to energy waste, performance degradation, and service-level agreement (SLA) violations. In this paper, we propose a consolidation approach based on the resource utilization prediction to determine the overloaded and underloaded hosts. The prediction method combines a Kalman filter and support vector regression (SVR) to forecast the host's future CPU utilization. Simulations are conducted on Cloudsim using real PlanetLab workloads to verify the performance of our proposal against existing benchmark algorithms. Experimental results demonstrate that our consolidation technique significantly reduces the SLA violation rate, number of VM migrations, and energy consumed in the datacenter.
Hybrid ResNet based on joint basic and attention modules for long-tailed classification

Long-tailed distribution learning is one of the critical research fields of deep learning and has gradually become a research hotspot. Existing re-sampling methods for long-tailed data classification attempt to adjust the number of tail class samples to balance the overall feature space and achieve satisfactory results. However, the methods impair the representative ability of the learned features to a certain extent, which in turn affects the tail class feature space. In this paper, we propose a hybrid ResNet based on joint basic and attention modules to enhance the tail class feature space, which provides rich discriminative and representative features in the tail class feature space. Firstly, we use hybrid ResNet to extract features, where the basic module ResNet and the attention module ResNet extract head and tail class features, respectively. The enhancement of tail class features can reduce the dependence of the classifier on head class features. Secondly, we build a fusion loss function, which considers the tradeoff between head loss and tail loss for long-tailed distribution learning. Experimental results show that the proposed model outperforms several state-of-the-art models in the long-tailed classification. Our model was 2.67% better than the optimal method under the long-tailed Tiny-Imagenet-LT dataset with an imbalanced ratio of 100.
Progressive downsampling and adaptive guidance networks for dynamic scene deblurring

The existing learning-based dynamic scene deblurring methods have made good progress to some extent. However, these methods are usually based on multiscale strategy, which has the following shortcomings: (1) The bilinear downsampling operation will cause some loss of important high-frequency information, e.g., strong edges, which also further affects the network learning a better deblurring mapping. (2) Existing methods only use a single activation function, which limits the ability of the network model to fit data and causes the network performance to be easily saturated. Therefore, we propose an end-to-end progressive downsampling and adaptive guidance network called PDAG-Net for solving above problems. The proposed PDAG-Net can retain more strong edges and other high-frequency information of a blurry image so as to make the network learn a more effective deblurring mapping between the input and label images. In the proposed PDAG-Net, we implement a multiscale blended activation residual block called MSBA-ResBlock for learning the nonlinear characteristics of dynamic scene blur, which can also alleviate the performance saturation problem caused by a single activation function and improve multiscale feature extraction ability. Finally, we propose a multisupervision strategy for obtaining more robust and effective features and making the network possess more stable trainging and faster convergence. Extensive experimental results on a public dataset indicate that the proposed network outperforms the state-of-the-art image deblurring methods.
Real-Time Left- and Right-Hand Detection for Sign Language Recognition

Ghotkar, Archana Gokhale, Atharva Malpure, DurveshSign language recognition is one of the most important application areas under computational linguistics where artificial intelligence-based utilities are being involved increasingly. As the signs are made using hands, there is a need to detect them in a given video or image. Some signs make use of both the hands, with each hand performing a different gesture, thus requiring a distinct identification of both the hands for complete recognition of such signs. Detection of the presence of left and right hands separately can make the sign language recognition systems more robust towards recognizing a greater number of signs, mainly due to the inclusion of signs that make use of both the hands. Also, with a large number of object detection models available, the selection of the most efficient and accurate model for the said purpose is important. For this study, eight standard object detection models from the You Only Look Once (YOLO) family were trained and tested on a robust dataset of images containing a variety of hand shapes for the task of detection of left and right hands. A detailed analysis of their performances has been presented in this paper.
Security Maturity Self-Assessment Framework for Software Development Lifecycle

Vulnerable software often originates from insufficient attention to security in the software development lifecycle. However, current maturity models provide limited support for the teams to assess the security maturity of their software development practices.
Fraudulent Activities in the Cyber Realm: DEFRAUDify Project

The increase in the number of Internet users has also led to an increase in activities leading to a cyber threat and fraud intelligence. These activities include the use of the Dark Web for coordination and virtual currencies for funding. This article will present the main methods of cyber-attacks and crimes used nowadays, and how they can be prevented by using tools specialized in monitoring transactions with virtual currencies and detecting web pages that pose a threat to users. The tools that will be described in this article are Graphsense used to analyze virtual currency activities and SpiderFoot used to identify Cyber Threat, Attack Surfaces, Security Assessments and Asset Discovery.
Precise Analysis of Purpose Limitation in Data Flow Diagrams

Data Flow Diagrams (DFDs) are primarily used for modelling functional properties of a system. In recent work, it was shown that DFDs can be used to also model non-functional properties, such as security and privacy properties, if they are annotated with appropriate security- and privacy-related information. An important privacy principle one may wish to model in this way is purpose limitation. But previous work on privacy-aware DFDs (PA-DFDs) considers purpose limitation only superficially, without explaining how the purpose of DFD activators and flows ought to be specified, checked or inferred. In this paper, we define a rigorous formal framework for (1) annotating DFDs with purpose labels and privacy signatures, (2) checking the consistency of labels and signatures, and (3) inferring labels from signatures. We implement our theoretical framework in a proof-of concept tool consisting of a domain-specific language (DSL) for specifying privacy signatures and algorithms for checking and inferring purpose labels from such signatures. Finally, we evaluate our framework and tool through a case study based on a DFD from the privacy literature.
The generalized microscopic image reconstruction problem

This paper presents and studies a generalization of the microscopic image reconstruction problem (MIR) introduced by Frosini and Nivat (2007) and Nivat (2002). Consider a specimen for inspection, represented as a collection of points typically organized on a grid in the plane. Assume each point x has an associated physical value ℓx, which we would like to determine. However, it might be that obtaining these values precisely (by what we call a surgical probe ) is difficult, risky, or impossible. The alternative is to employ aggregate measuring techniques (such as EM, CT, US or MRI), whereby each measurement is taken over a larger window, and the exact values at each point are subsequently extracted by computational methods. In this paper, we extend the MIR framework in a number of ways. First, we consider a generalized setting where the inspected object is represented by an arbitrary graph G, and the vector ℓ∈Rn assigns a value ℓv to each node v. A probe centred at a node v will capture a window encompassing its entire neighbourhood N[v], i.e., the outcome of a probe centred at v is Pv=∑w∈N[v]ℓw. We give a criterion for the graphs for which the extended MIR problem can be solved by extracting the vector ℓ from the collection of probes, P={Pv∣v∈V}. We then consider cases where such reconstruction is impossible (namely, graphs G for which the probe vector P is inconclusive, in the sense that there may be more than one vector ℓ yielding P). Assume that surgical probes are technically available, yet are expensive or risky, and must be used sparingly. We show that in such cases, it may still be possible to achieve reconstruction based on a combination of a collection of ordinary (aggregate) probes together with a suitable set of surgical probes. We aim at identifying the minimum number of surgical probes necessary for a unique reconstruction, depending on the graph topology. This is referred to as the Minimum Surgical Probing problem (MSP). Besides providing a solution for the above problems for arbitrary graphs, we also explore the range of possible behaviours of the Minimum Surgical Probing problem by determining the number of surgical probes necessary in certain specific graph families, such as perfect k-ary trees, paths, cycles, grids, tori, tubes and hypercubes.
Polarization Fingerprint: A Novel Physical-Layer Authentication in Wireless IoT

Radio frequency (RF) fingerprinting is a low-cost, high-efficiency, and high-security authentication technique for wireless IoT devices with limited resources, but RF fingerprinting faces problems such as small fingerprint differences, low finger-print stability, and high implementation difficulty. In order to solve these problems, we propose a novel concept of polarization fingerprinting. Polarization fingerprint (PF) is manifested as the correlation between polarization state and frequency. The properties of PF include group feature, individual feature and directionality. Group feature characterizes the antenna structure, and individual feature characterizes the antenna hardware imperfections. The directionality comes from the vector property of polarization and contains the relative position information of the communicating devices. The directionality solves the problem of similar fingerprints that may occur when the number of devices increases, which cannot be solved by RF fingerprinting. Compared to RF fingerprint, PF can exist stably and continuously, which not only solves the problem of low fingerprint stability, but also makes polarization fingerprinting based authentication easier to be implemented. The stability and continuity of PF allow more samples to be obtained during authentication. We also proved that increasing the sample amount can reduce the false alarm rate of authentication. Finally, we conducted experiments based on wireless IoT devices. Experiment results show that polarization fingerprinting based authentication has better performance than RF fingerprinting based under the same conditions.
Super-Resolution Based on Curvelet Transform and Sparse Representation

Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s).In this paper, we proposed a single image super-resolution algorithm.It uses the nonlocal mean filter as a prior step to produce a denoised image.The proposed algorithm is based on curvelet transform.It converts the denoised image into low and high frequencies (sub-bands).Then we applied a multi-dimensional interpolation called Lancozos interpolation over both sub-bands.In parallel, we applied sparse representation with over complete dictionary for the denoised image.The proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher resolution.The experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced edges.The comparison study shows that the proposed super-resolution algorithm outperforms the state-of-the-art.The mean absolute error is 0.021 ± 0.008 and the structural similarity index measure is 0.89 ± 0.08.
Ghost Hunting in the Broken Archives: Re-Historicizing Digital Education in an Institutional Context

Abstract Digital education is often presented as breaking from tradition. A failure to account for how digital education emerges from historical institutional activity is problematic insofar as this activity continues to circulate through the present and future, appearing and disappearing in often unexpected ways. Using Derrida’s hauntology as a theoretical lens, this paper traces how a digital education initiative at the University of Edinburgh in 2003 carried through to the creation of a course to train teachers to teach online in 2019, which in turn informed the university’s response to the pandemic in 2020. Working in a broadly autoethnographic way alongside archival document analysis, several findings emerged. First, hauntology provides a mechanism for institutions to trace their own histories and to note how these histories, often hidden in archives or carried forward into the present by hosts, inform their present and future trajectories. Second, broken archives, those that have ceased to function as active repositories but are disconnected from institutional domains and ontologies, shut due to absent gatekeepers, or merely forgotten, contribute​ to the sudden and often unexpected emergence of hauntings in present and future trajectories. Third, curation of the archive is an act of reinterpretation, one that troubles historical narratives and introduces new hauntings. All these findings assert a re-historicizing of digital education by emphasising the hauntings from the past that inform its emergent present and contested future, countering many of the ahistorical imaginaries of digital education.
Using cognitive models to design dynamic task allocation systems

Many operations in Intelligence, Surveillance, and Reconnaissance (ISR) involve balancing multiple simultaneous interdependent tasks and coordinating between multiple teammates. Recently, autonomous managers (AMs) were proposed as a method for optimizing performance in team-based workflows using dynamic reallocation in response to changes in workload and performance. We demonstrate how cognitive models can be used in the design and evaluation of AMs. Specifically, cognitive models can be used to inform an AM’s decision policy, and to stress test an AM under a wide variety of conditions. Simulation 1 tested the robustness of numerous AMs across a wide range of cognitive agents. We found that a simpler cognitive model in the AM’s decision system was more robust than more complex models. In the second simulation study, we compared dynamic task reallocation and corrective feedback to improve performance of cognitive agents based on the ACT-R cognitive architecture. Our results indicate that both interventions have the potential to improve performance, and that the most robust AM from simulation 1 can improve the performance of a model with realistic learning dynamics. Our simulations demonstrate that cognitive models are useful for designing and evaluating AMs for multiple military applications, including ISR.
Interstitial lung disease detection using template matching combined sparse coding and blended multi class support vector machine

Interstitial lung disease (ILD), representing a collection of disorders, is considered to be the deadliest one, which increases the mortality rate of humans. In this paper, an automated scheme for detection and classification of ILD patterns is presented, which eliminates low inter-class feature variation and high intra-class feature variation in patterns, caused by translation and illumination effects. A novel and efficient feature extraction method named Template-Matching Combined Sparse Coding (TMCSC) is proposed, which extracts features invariant to translation and illumination effects, from defined regions of interest (ROI) within lung parenchyma. The translated image patch is compared with all possible templates of the image using template matching process. The corresponding sparse matrix for the set of translated image patches and their nearest template is obtained by minimizing the objective function of the similarity matrix of translated image patch and the template. A novel Blended-Multi Class Support Vector Machine (B-MCSVM) is designed for tackling high-intra class feature variation problems, which provides improved classification accuracy. Region of interests (ROIs) of five lung tissue patterns (healthy, emphysema, ground glass, micronodule, and fibrosis) selected from an internal multimedia database that contains high-resolution computed tomography (HRCT) image series are identified and utilized in this work. Performance of the proposed scheme outperforms most of the state-of-art multi-class classification algorithms.
Data-Driven Winter Landscape Design and Pleasant Factor Analysis of Elderly Friendly Parks in Severe Cold Cities in Northeast China under the Background of Artificial Intelligence

Urban parks not only combine the greening of the city with the natural landscape but also are places for people’s daily fitness, leisure, entertainment, and activities to meet their psychological needs. At present, the design of our country’s northeast severe cold city park is not perfect, which cannot fully highlight the regional characteristics and show the local winter natural landscape. Under the background of the lagging development of the northeast severe cold city park, we use artificial intelligence technology and data-driven algorithm to design a new northeast severe cold city elderly friendly park. The experimental results are as follows: (1) we analyze the development trend of population aging in our country. As the main demanders of urban parks, the elderly determined the research direction of the experiment, investigated the needs of different people for the park, and designed the function of the park according to the needs of the park. (2) On the basis of retaining the traditional garden design, show artificial intelligence technology in the design of urban parks and transforming the urban park design with intelligent construction can not only let tourists have better entertainment and exercise but also enable residents to better invest in the park surroundings. Intelligent park design can not only show the unique park landscape but also relieve the pressure of tourists.
Estimation of dual‐mode nonlinear stochastic systems with unknown parameters

Abstract This article proposes the problem of state estimation for dual‐mode nonlinear stochastic systems with unknown parameters, that is, the possible state evolution is described by unknown switch between normal and abnormal modes, and the mismatch term of abnormal mode deviating from the normal one is parameterized as a linear stochastic transformation of state with unknown transformation matrix and modeling error covariance to generalize various abnormal state evolution. For this complex estimation problem involving unknown states, modes, and parameters (i.e., transformation matrix and covariance), a Bayesian learning based filter is first derived to update the joint posterior of state and unknown parameters in abnormal mode by exploiting sequential Monte Carlo, conjugate prior and marginalization. Then a switching output strategy of normal/abnormal mode filters based on likelihood‐ratio test is designed to reduce the conservatism of abnormal mode filter to normal mode state estimation. Finally, simulation study on a benchmark of maneuvering target tracking shows the superiority of the proposed method compared with existing ones.
D3VN: Decentralized ABE-Based Distributed Data Validation Network

The Internet of things (IoT) network resources generate enormous data. The generated real-time data can be used in various fields to improve the quality of the provided services and make smart decisions. One of the main concerns in such systems is validating the received data. Most data validations techniques rely and run on a centralized entity, which affects the system’s security, efficiency, and scalability. The recently proposed decentralized data validation network assumed that the logical classification of validators could be checked. In this paper, we provide a concrete model of a decentralized data validation network that utilizes attribute-based encryption (ABE) to ensure that the validators without required attributes cannot be involved in the process of data validation. We defined and proved the completeness and unforgeability security properties of our model.