Reviewerly/exHarmony huggingface dataset:
There’s more to alternations than the main diagonal of a 2×2 confusion matrix: Improvements of MuPDAR and other classificatory alternation studies

Abstract Corpus-based studies of learner language and (especially) English varieties have become more quantitative in nature and increasingly use regression-based methods and classifiers such as classification trees, random forests, etc. One recent development more widely used is the MuPDAR (Multifactorial Prediction and Deviation Analysis using Regressions) approach of Gries and Deshors (2014) and Gries and Adelman (2014). This approach attempts to improve on traditional regression- or tree-based approaches by, firstly, training a model on the reference speakers (often native speakers (NS) in learner corpus studies or British English speakers in variety studies), then, secondly, using this model to predict what such a reference speaker would produce in the situation the target speaker is in (often non-native speakers (NNS) or indigenized-variety speakers). Crucially, the third step then consists of determining whether the target speakers made a canonical choice or not and explore that variability with a second regression model or classifier. Both regression-based modeling in general and MuPDAR in particular have led to many interesting results, but we want to propose two changes in perspective on the results they produce. First, we want to focus attention on the middle ground of the prediction space, i.e. the predictions of a regression/classifier that, essentially, are made non-confidently and translate into a statement such as ‘in this context, both/all alternants would be fine’. Second, we want to make a plug for a greater attention to misclassifications/-predictions and propose a method to identify those as well as discuss what we can learn from studying them. We exemplify our two suggestions based on a brief case study, namely the dative alternation in native and learner corpus data.
Cryptanalysis of a Pixel Permutation Based Image Encryption Technique Using Chaotic Map

Recently, a new image encryption algorithm was proposed by Anwar and Meghana.This encryption scheme uses the Arnold's chaotic cat map to permute the image pixels.The resulting image is then confused with both a secret image provided as part of the secret key and a secret value selected randomly from the permuted image.Using a random image as part of the secret key, gives this algorithm an infinite key space which increases its efficiency against brute-force attacks.In order to help improving the security of modern image encryption schemes, this paper presents a cryptanalysis of the proposed algorithm using a combination of chosen-plaintext and brute-force attacks.First, the infinite key space that the secret image offers is broken using a chosen-plaintext attack.Then, the permutation phase is reversed through a series of chosen-plaintext attacks too.Finally, the secret value chosen randomly from the permuted image is easily brute-forced due to its reduced number of possible values.By applying the above method, it is possible to restore the plain version of any image that was encrypted using the former encryption algorithm.Thus, relying on this algorithm to encrypt real sensitive data is not secure.
Technology "Debate" as a tool for communicative competence development

The task of a modern university is to produce a highly qualified and competent specialist, ready for independent implementation of professional activities. Communicative competence is one of the fundamental in such preparation. The purpose of the article is to review the experience of implementing the Debate technology as an effective means of developing communicative competence of future vocational training teachers. The article focuses on the specifics of their future activities, its features and conditions, as well as their role in the field of professional education. The teacher of vocational training carries out pedagogical, educational-production and organizational-methodological activities in students training in secondary vocational schools. The basis for the implementation of his professional activity is the construction of effective interaction in the classroom between the teacher and students. For this, future teachers of vocational training must master communicative competence which is one of the main components of their future activities success. The article reveals features of the "Debate" and their capabilities in students’ training. The study allowed us to check the level of communicative competence development in dynamics, as well as to identify the level of students' motivation before using the Debate technology and after. Based on the data obtained, we can talk about the effectiveness of technology implementation. "Debate" provides ample opportunities for students to develop interaction skills, conduct constructive dialogue, select arguments, effective interaction strategies, the ability to balance emotional stress during the discussion, the ability to speak in public and other skills that contribute to the development of communicative competence and provide highly qualified specialist training.
A Linked Data-Based Semantic Information Model for Smart Cities

Smart cities typically involve a myriad of inter-connected systems intended to promote better management of urban and natural resources of cities, thereby contributing to the improve the quality of life of citizens. The heterogeneity of domains, systems, data, and relationships among them requires defining a data model able to express information in a flexible, extensible way while promoting interoperability between systems and applications. Furthermore, smart city systems can benefit from georeferenced information to allow for more effective actions over the real-world urban space. Aiming at tackling challenges related to data heterogeneity while considering georeferenced information, this work introduces LGeoSIM, a semantic-based information model for smart cities as means of fostering interoperability and powerful automated reasoning upon unambiguous information. LGeoSIM relies on the recent NGSI-LD Specification, thereby encompassing the principles of Linked Data to allow semantically defining information through ontologies and their interconnection. This paper also presents an implementation of LGeoSIM within Smart Geo Layers, a geographic-layered data middleware platform conceived to integrate data provided by heterogeneous sources in a smart city environment.
SuperCaptioning: Image Captioning Using Two-dimensional Word Embedding

Language and vision are processed as two different modal in current work for image captioning. However, recent work on Super Characters method shows the effectiveness of two-dimensional word embedding, which converts text classification problem into image classification problem. In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model. The experimental results on Flickr30k data shows the proposed method gives high quality image captions. An interactive demo is ready to show at the workshop.
Two motion models for improving video object tracking performance

Two motion models are proposed to enhance the performance of video object tracking (VOT) algorithms. The first one is a random walk model that captures the randomness of motion patterns. The second one is a data-adaptive vector auto-regressive (VAR) model that exploits more regular motion patterns. The performance of these models is evaluated empirically using real-world datasets. Three real-time publicly available visual object trackers: the normalized cross-correlation (NCC) tracker, the New Scale Adaptive with Multiple Features (NSAMF) tracker, and the correlation filter neural network (CFNet) are modified using each of these two models. The tracking performances are then compared against the original formulation. It is observed that both models of the prior information lead to performance enhancement of all three trackers. This validates the hypothesis that when training videos are available, prior information embodied in the motion models can improve the tracking performance.
Securing Time in Untrusted Operating Systems with TimeSeal

An accurate sense of elapsed time is essential for the safe and correct operation of hardware, software, and networked systems. Unfortunately, an adversary can manipulate the system's time and violate causality, consistency, and scheduling properties of underlying applications. Although cryptographic techniques are used to secure data, they cannot ensure time security as securing a time source is much more challenging, given that the result of inquiring time must be delivered in a timely fashion. In this paper, we first describe general attack vectors that can compromise a system's sense of time. To counter these attacks, we propose a secure time architecture, TIMESEAL that leverages a Trusted Execution Environment (TEE) to secure time-based primitives. While CPU security features of TEEs secure code and data in protected memory, we show that time sources available in TEE are still prone to OS attacks. TIMESEAL puts forward a high-resolution time source that protects against the OS delay and scheduling attacks. Our TIMESEAL prototype is based on Intel SGX and provides sub-millisecond (msec) resolution as compared to 1-second resolution of SGX trusted time. It also securely bounds the relative time accuracy to msec under OS attacks. In essence, TIMESEAL provides the capability of trusted timestamping and trusted scheduling to critical applications in the presence of a strong adversary. It delivers all temporal use cases pertinent to secure sensing, computing, and actuating in networked systems.
A continuously updated predictive analytics model for the timely detection of critically ill patients with a high risk of mortality

ABSTRACTIntensivists routinely encounter seemingly stabilized patients who expire before discharge. Current early warning systems have not proven effective for identifying these patients in sufficient time for clinical intervention. A novel algorithm is described here that is timely and accurate for recognizing patients in an intensive care unit (ICU) that have a substantial mortality risk.Population: 59,400 admissions at 13 adult ICUs from 1/1/2012-9/30/2018.Outcome: Mortality before discharge from the ICU. Overall rate was 6.9%.Methods: All heart rate, respiratory rate and oxygen saturation values were obtained, as well as the start and stop times for those patients receiving mechanical ventilation. Data from the first two hours post-admission were used to find the cut points that maximized variability rates across ranges of vital signs values. These ranges were subsequenty mapped to a letter. A letter was then assigned to the median of each vital sign over consecutive 30-minute periods. Four consecutive letters were concatenated to form a pattern, and these were candidates for triggers (i.e. risk alerts). Using a genetic algorithm that weighted the outcome of mortality, we acquired a set of patterns that increased risk. Those patterns were then validated as triggers for increased risk.Results: Patients with zero or one triggers had a mortality rate of 0%; patients with two to four triggers had a mortality rate of between 2.8% and 5.5%; five or more triggers were seen in patients with a 20.2% to 25.6% mortality rate.Conclusion: Distinctive patterns in vital signs and whether a patient received mechanical ventilation can identify patients that have a high risk of mortality.  This methodology could be prospectively used in ICUs to identify high-risk patients in a timely enough manner to effect remedial treatment.
A Gesture Control System for Drones Used with Special Operations Forces

Special Operations Forces (SOF) are facing extreme risks when prosecuting crimes in uncharted environments like buildings. Autonomous drones could potentially save officers' lives by assisting in those exploration tasks, but an intuitive and reliable way of communicating with autonomous systems is yet to be established. This paper proposes a set of gestures that are designed to be used by SOF during operation for interaction with autonomous systems.
Producing Standard Rules for Smart Real Estate Property Buying Decisions based on Web Scraping Technology and Machine Learning Techniques

Purchasing of real estate property is a stressful and time-consuming activity, regardless of the individual in question is a buyer or seller.The act is also a major financial decision which can lead to numerous consequences if taken hastily.Therefore, it is encouraged that a person should properly invest their time and money in research relating to price demands, property type and location, etc.It can be a difficult task to assess what real estate property can be considered as the best property to buy.The key idea of the current research study is to create a set of standard rules, which should be embraced to make a smart decision of buying real estate property, based on web scraping technology and machine learning techniques.
Analysis of Performance Cross Validation Method and K-Nearest Neighbor in Classification Data

To produce data classifications that have data accuracy or similarity in proximity of a measurement result to the actual numbers or data, testing can be done based on accuracy with test data parameters and training data specified by Cross Validation. Therefore data accuracy is very influential on the final result of data classification because when data accuracy is inaccurate it will affect the percentage of test data grouping and training data. Whereas in the K-Nearest Neighbor method there is no division of training data and test data. Based on the evaluation results of the Cross Validation algorithm on the effect of the number of K in the K-nearest Neighbor classification data. The data sharing with Cross Validation has better data recognition with a percentage of 100%. The results of the K-NN test results in the classification of data using iris data sets using variation test values 3, 4, 5, 6, 7, 8, 9, have 100% percentage accuracy with 75 true amount of data and 0 incorrect amount of data. Percentage of variation in K K-Nearest Neighbor 3,4,5,6,7,8,9. and variations in the number of K-Fold 1,2,3,4,5,6,7,8,9,10. has a percentage of 100% on K-Fold 4 and 7
Feature partitioning for robust tree ensembles and their certification in adversarial scenarios

Abstract Machine learning algorithms, however effective, are known to be vulnerable in adversarial scenarios where a malicious user may inject manipulated instances. In this work, we focus on evasion attacks, where a model is trained in a safe environment and exposed to attacks at inference time. The attacker aims at finding a perturbation of an instance that changes the model outcome.We propose a model-agnostic strategy that builds a robust ensemble by training its basic models on feature-based partitions of the given dataset. Our algorithm guarantees that the majority of the models in the ensemble cannot be affected by the attacker. We apply the proposed strategy to decision tree ensembles, and we also propose an approximate certification method for tree ensembles that efficiently provides a lower bound of the accuracy of a forest in the presence of attacks on a given dataset avoiding the costly computation of evasion attacks.Experimental evaluation on publicly available datasets shows that the proposed feature partitioning strategy provides a significant accuracy improvement with respect to competitor algorithms and that the proposed certification method allows ones to accurately estimate the effectiveness of a classifier where the brute-force approach would be unfeasible.
A Color Consistency Processing Method for HY-1C Images of Antarctica

The HY-1C satellite, as part of China’s optical satellite constellation for global ocean monitoring, monitors the ocean and coastal environment by the three broad visible bands of the Coastal Zone Imager (CZI) instrument. However, as a result of the sensor instrument noise, the atmospheric environment during imaging, and the shooting angle, the satellite images often show uneven illumination and inconsistent color between neighboring images. In this paper, according to the characteristics of the HY-1C CZI instrument, we propose a color consistency processing framework for coastal zone images of Antarctica. First of all, the high-frequency and low-frequency information of the image is separated by a statistical filter with simple clustering. The uneven lighting is then replaced by artificial lighting, which is globally uniform. Finally, the color difference between images is corrected by a color transfer method. In order to evaluate the color consistency results quantitatively, a new quantitative evaluation method is proposed. The experimental results for the coastal zone images of Antarctica show that the new processing framework can effectively eliminate the unevenness in the lighting and color. The mosaic results show a good performance in consistent lighting and tones, and the lack of visible mosaic lines proves the effectiveness of the proposed method. The quantitative evaluation analysis confirms the superiority of the proposed method over the Wallis method.
Analysis of function-call graphs of open-source software systems using complex network analysis

ÖzSoftware systems are usually designed in a modular and hierarchical fashion, where functional responsibility of a system is decomposed into multiple functional software elements optimally such as subsystems, modules, packages, classes, methods, and functions.These elements are coupled with each other with some kind of dependency relationships to some degree, and their interactions naturally form a graph or network structure.In this study, we generated the static function-call graphs of several open-source software systems, where functions were the most basic type of interacting elements calling each other.Then, we analyzed the call graphs both visually and topologically using the techniques of complex network analysis.We found the call graphs to reveal scale-free and small-world network properties similar to the findings of the previous studies.In addition, we identified the most central and important functions in each call-graph using several centrality measures.We also performed community analysis and found that the call graphs exhibited a tendency to form communities.Finally, we showed that analysis of static function-call graphs of software systems through complex network analysis has the potential to reveal useful information about them.Yazılım sistemleri genellikle sistemin işlevsel sorumluluğunun optimal bir şekilde altsistemler, modüller, paketler, sınıflar, metodlar, ve fonksiyonlar gibi çok sayıda işlevsel yazılım elemanına ayrıştırıldığı, modüler ve hiyerarşik bir biçimde tasarlanırlar.Bu elemanlar birbirleriyle çeşitli ilişki türleri ile bağlıdırlar ve bunların etkileşimleri doğal olarak bir graf veya ağ yapısı oluşturur.Bu çalışmada, etkileşim halindeki en temel eleman türü olarak birbirini çağıran fonksiyonları dikkate alarak,
Spatial-Temporal Feature Aggregation Network For Video Object Detection

Video object detection is a challenging problem in computer vision. In this paper, we propose a novel spatial-temporal feature aggregation network to deal with this issue. Specifically, we present a novel instance-level feature aggregation module as complementary to traditional pixel-level feature aggregation, in which we build a new movement estimation module to learn instance movements across frames. Then the Graph Convolutional Networks (GCNs) is applied to obtain temporal relation among instances over frames to implement instance-level feature aggregation. At last, we combine pixel-level and instance-level features by learnable soft weights to make use of their complementary information. Our framework is simple to implement and enables end-to-end training, which achieves state-of-art performance on the ImageNet VID dataset by extensive experiments.
Deep Multi-Scale Gabor Wavelet Network for Image Restoration

Due to the limitations of the imaging processors and complex weather conditions, image degradation is often inevitable. Existing deep learning-based image restoration methods often rely on the powerful feature representation capacity of deep networks and pay less attention to the inherent properties of the degradation signal, e.g. variations in spatial scale and orientations across the image, which makes them ineffective for the image restoration tasks. In this paper, we propose a Multiscale Gabor Wavelet Network (MsGWN) for image restoration. We apply the multi-scale architecture to extract the contaminated feature from input at different spatial scales, and thus the contaminated feature can be effectively restored in a corse- to-fine manner. However, using multi-scale architecture alone cannot remove the degradations with different orientations. To overcome this problem, we introduce a Gabor Wavelet Module (GWM) to further extract the contaminated features from four orientations. By decomposing the features into four multi-orientation components, the restoration process can be facilitated by avoiding learning the mixed degradations all-in- one. We evaluate the proposed method on image demoirding, image deraining, and image dehazing. Experiments on these applications demonstrate that the proposed method can achieve favorable results against the state-of-the-art approaches.
Symmetric Cryptography With Shift 2<sup>n-1</sup>, Hash Transformation, Optimization-Based Controller for Medical Image Infosecurity: Case Study in Mammographic Image

Among imaging examinations, mammography noninvasively aids in the early detection and diagnosis of diseases inside the body, such as lung cancer or breast tumor, for first-line examination and helps physicians to treat medical conditions. As digital medical images can be transmitted by computer or telecommunication network, ensuring image infosecurity in public spaces is difficult. In infosecurity considerations, digital signature and encryption of individual anamnesis plain images should prevent data from being stolen, tampered, or peeked by unauthorized people. Hence, infosecurity has become an important issue in the digital era. This study proposes right / left shift 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-1</sup> pixel values, hash transformation with multi-secret gold keys, and an optimization-based controller to integrate two symmetric cryptography processes for medical image encryption and decryption with the same secret gold keys. Then, swarm optimization-based controllers, which are based on particle swarm optimization (PSO) and firefly algorithm (FA), are implemented to search the decryption key parameters. For a case study on mammographic images consisting of medical X-ray images of 50 benign tumors and 50 malignant tumors, the peak signal-to-noise ratio is used to evaluate the relation between the original plain images and decrypted images. Conclusively, the FA-based controller performed better than the PSO-based controller in terms of recovery reliability and speeds up the computation of the desired optimal solution.