Reviewerly/exHarmony huggingface dataset:
Predictor-corrector image interpolation

Abstract A novel image interpolation methodology is proposed in this paper, called the predictor-corrector interpolation (PCI). Given a low-resolution (LR) image, our PCI scheme begins with the prediction stage, aiming to interpolate the LR-sized input image to a high-resolution (HR) image which is of the same size as the final interpolated image. In the subsequent correction stage, those salient pixels (e.g., edge pixels) of the predicted image are identified and then necessary corrections are made to them for further improving the image quality. To demonstrate the effectiveness of this PCI methodology, the sparse mixing estimator (SME) interpolation is selected as the predictor, and a modified version of the contrast-guided interpolation (CGI) is developed and exploited as the corrector. Hence, the proposed PCI algorithm is denoted as PCI( sme , hr-cgi ), which shows a superior performance over a number of comparable state-of-the-art image interpolation algorithms in both objective and subjective image quality assessment.
CLOE: a cross-lingual ontology enrichment using multi-agent architecture

This article proposes across-lingual ontology enrichment (CLOE) approach for enriching ontologies from amultilingual text or other ontologies. Athree-level hierarchical multi-agent model has been built, where each node is modelled as an agent. This has several advantages: ontologies can be used to enrich other ontologies in adifferent language, several ontologies can be enriched at the same time, and domain-independent. A prototype for the proposed approach has been implemented. Evaluation results are promising, showing considerable precision and recall scores. We believe that this approach is the first such attempt to enrich multiple ontologies using adomain-independent approach.
Application of machine learning in BGP anomaly detection

Aiming at the problem of BGP anomalies, a support vector machine-based BGP anomaly detection method (SVM-BGPAD) is proposed. We first employ feature selection algorithm based on Fisher linear analysis and Markov random field technology to select features that can maximize the distance among classes and minimize the distance within the class, and then use grid search and cross-validation methods to optimize the parameters of SVM model. We evaluate the performance of classification model with linear, polynomial, RBF and Sigmoid kernels. The results are compared based on accuracy and F1-Score. Experimental results show that the model based on RBF kernel function can achieve the best classification accuracy of 91.36% and F1-Score of 96.03%.
Nonlocal advantage of quantum coherence in the Heisenberg <i>XY</i> model

Quantum correlations in a state can be quantified from different aspects. The nonlocal advantage of quantum coherence (NAQC) captures quantum correlation stronger than entanglement and Bell nonlocality. We investigate the NAQC-type quantum correlation in the Heisenberg XY model and showed that both the anisotropy of the interaction between two spins and the external transverse magnetic field can serve as efficient parameters for tuning the NAQC-type quantum correlation. In particular, for the relative low temperature case there is a finite parameter region in which the strength of the NAQC-type quantum correlation is of about its maximum 1. We also found there are quantum states for which the NAQC-type quantum correlations defined by the l 1 norm of coherence and the relative entropy of coherence have a different ordering.
Estimating word-level quality of statistical machine translation output using monolingual information alone

Abstract Various studies show that statistical machine translation (SMT) systems suffer from fluency errors, especially in the form of grammatical errors and errors related to idiomatic word choices. In this study, we investigate the effectiveness of using monolingual information contained in the machine-translated text to estimate word-level quality of SMT output. We propose a recurrent neural network architecture which uses morpho-syntactic features and word embeddings as word representations within surface and syntactic n -grams. We test the proposed method on two language pairs and for two tasks, namely detecting fluency errors and predicting overall post-editing effort. Our results show that this method is effective for capturing all types of fluency errors at once. Moreover, on the task of predicting post-editing effort, while solely relying on monolingual information, it achieves on-par results with the state-of-the-art quality estimation systems which use both bilingual and monolingual information.
Knowledge Reused Outlier Detection

Tremendous efforts have been invested in the unsupervised outlier detection research, which is conducted on unlabeled data set with abnormality assumptions. With abundant related labeled data available as auxiliary information, we consider transferring the knowledge from the labeled source data to facilitate the unsupervised outlier detection on target data set. To fully make use of the source knowledge, the source data and target data are put together for joint clustering and outlier detection using the source data cluster structure as a constraint. To achieve this, the categorical utility function is employed to regularize the partitions of target data to be consistent with source data labels. With an augmented matrix, the problem is completely solved by a K-means - a based method with the rigid mathematical formulation and theoretical convergence guarantee. We have used four real-world data sets and eight outlier detection methods of different kinds for extensive experiments and comparison. The results demonstrate the effectiveness and significant improvements of the proposed methods in terms of outlier detection and cluster validity metrics. Moreover, the parameter analysis is provided as a practical guide, and noisy source label analysis proves that the proposed method can handle real applications where source labels can be noisy.
Automated dictionary generation for political eventcoding

Abstract Event data provide high-resolution and high-volume information about political events and have supported a variety of research efforts across fields within and beyond political science. While these datasets are machine coded from vast amounts of raw text input, the necessary dictionaries require substantial prior knowledge and human effort to produce and update, effectively limiting the application of automated event-coding solutions to those domains for which dictionaries already exist. I introduce a novel method for generating dictionaries appropriate for event coding given only a small sample dictionary. This technique leverages recent advances in natural language processing and machine learning to reduce the prior knowledge and researcher-hours required to go from defining a new domain-of-interest to producing structured event data that describe that domain. I evaluate the method with the production of a novel event dataset on cybersecurity incidents.
Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Summary Side‐channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side‐channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side‐channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short‐term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45‐nm library.
Social stability risk assessment of NIMBY major projects by OWA, matter-element, and cloud model

To solve the problem of Social Stability Risk (SSR) assessment for Not In My Back Yard (NIMBY) major projects, first, we established an SSR assessment index system in terms of legality, rationality, feasibility, and controllability. Then, we used the cloud model to determine the value of SSR assess ment indicator and the Ordered Weighted Averaging (OWA) operator to weight SSR indicators. Finally, the SSR levels of NIMBY major projects were measured by the matter-element model. The case study shows that the SSR indicator system and assessment model developed in this work can assess the SSR of NIMBY major projects and provide a reference for the investment decisions of government departments.
HeteroSense

Occupancy behavior in buildings is challenging to define and quantify because of their stochastic, diverse and complex nature. Indoor environment and energy usage in buildings are significantly influenced by the actions and behavior of occupants. Accurate estimation and prediction of occupancy behaviours such as occupancy trajectories with datasets from deployed sensors can reduce energy consumption and facilitate intelligent building operations. However, identifying affordable sensors for estimating occupancy in buildings is still a challenge. To detect occupancy state in real-time, we propose a smart fusion of not necessarily optimal expensive sensors but accurate enough sensors. Sensor fusion is opted to boost the performance of the framework. In this paper, we propose HeteroSense an occupancy sensing framework that uses a labelbased approach for activity recognition using machine learning classification algorithms. Based on the sensor data collected from heterogeneous sensing modalities, an algorithm is designed for trajectory detection. This paper also discusses the challenges faced during the design phase for the deployment and it summarizes the potential improvements in the field of occupancy sensing for energy efficient buildings.
Late line‐of‐sight check and partially updating for faster any‐angle path planning on grid maps

Path planning is a classic decision problem in computer games and robotics. It is common to discretise the continuous planning space into grids with blocked cells to represent obstacles. Any-angle path planning methods, such as Theta* and Lazy Theta*, use line-of-sight check (LoS-Check) to find the nearly shortest path because the path will not be constrained to grid edges. The authors propose a new method called Late LoS-Check A* (LLA*), which contains two parts: employing the delayed LoS-Check to reduce the collision detection amount (late LoS-Check) and partially updating the path cost of the successor vertexes based on A* (partially updating). The experiment results show that LLA* costs less execution time than Lazy Theta* and retains even shorter path length. Through reducing the LoS-Check amount, the planning will be faster, but the path length will hardly be shorter. Therefore, it is the discriminatory updating strategy that makes both the path length and execution time of LLA* shorter than those of Lazy Theta*.
Effects of frame rate on vection and postural sway

The quality of stereoscopic 3D cinematic content is a major determinant for user experience in immersive cinema in both traditional theatres and cinematic virtual reality. One of the most important parameters is the frame rate of the content which has historically been 24 frames per second for movies, but higher frame rates are being considered for cinema and are standard for virtual reality. A typical behavioural response to immersive stereoscopic 3D content is vection, the visually-induced perception of self-motion elicited by moving scenes. In this work we investigated how participants’ vection varied with simulated virtual camera speed, frame rate, and motion blur produced by the virtual camera’s exposure, while viewing depictions of movement through a realistic virtual environment. We also investigated how their postural sway varied with these parameters and how sway covaried with levels of perceived self-motion. Results show that while average perceived vection significantly increased with 3D content frame rate and motion speed, motion blur had no significant effect on perceived vection. We also found that levels of postural sway induced by vection correlated positively with subjective ratings.
Image‐based CAPTCHAs based on neural style transfer

Over the last few years, completely automated public turing test to tell computers and humans apart (CAPTCHA) has been used as an effective method to prevent websites from malicious attacks, however, CAPTCHA designers failed to reach a balance between good usability and high security. In this study, the authors apply neural style transfer to enhance the security for CAPTCHA design. Two image-based CAPTCHAs, Grid-CAPTCHA and Font-CAPTCHA, based on neural style transfer are proposed. Grid-CAPTCHA offers nine stylized images to users and requires users to select all corresponding images according to a short description, and Font-CAPTCHA asks users to click Chinese characters presented in the image in sequence according to the description. To evaluate the effectiveness of this techniques on enhancing CAPTCHA security, they conducted a comprehensive field study and compared them to similar mechanisms. The comparison results demonstrated that the neural style transfer decreased the success rate of automated attacks. Human beings have achieved a successful solving rate of 75.04 and 84.49% on the Grid-CAPTCHA and Font-CAPTCHA schemes, respectively, indicating good usability. The results prove deep learning can have a positive effect on enhancing CAPTCHA security and provides a promising direction for future CAPTCHA study.
New Scheduling Strategy Based on Multi-Criteria Decision Algorithm

This paper presents a new scheduling strategy proposed to optimize the scheduling of several containers submitted online by users in a private infrastructure of nodes i.e. a cloud platform. In the literature, several scheduling frameworks and studies are proposed. The majority of these works use a scheduling strategy based on one criterion, such as Spread and Bin Packing strategies. The Spread strategy consists to select the node having the least number of executed containers to balance the containers load between all nodes of the infrastructure. The Bin Packing strategy consists to select the most compacted node in terms of resources to reduce the number of used nodes of the infrastructure. However, the submitted containers are defined according to a multi-criteria, such as the number of used CPUs and the used memory size. The state of each node is also defined according to a multi-criteria, such as the number of executed containers, the number of available CPUs and the size of available memory. The novelty of our scheduling strategy is to choose the node that executes a container by combining the Spread and the Bin Packing principles using the Technique for the Order of Prioritisation by Similarity to Ideal Solution (TOPSIS) algorithm. TOPSIS is a multi-criteria decision analysis algorithm. Our proposed strategy is implemented in Docker Swarm. Docker swarm is an important container scheduler framework developed by Docker. Experiments demonstrate the potential of our strategy under different scenarios.
Finding model through latent semantic approach to reveal the topic of discussion in discussion forum

There are lots of information and knowledge can be extracted from a discussion forum. Despite a discussion is opened by submitting a thread as the topic of discussion, however, the discussion may open out to different topics. This paper aims to present a model to find out a topic of discussion through latent semantic approach, named Topics Finding Model (TFM). The model proposes a complete step to reveal the topic of discussion from a thread in a discussion forum, consisting of the pre-processing text document, corpus classification and finding a topic. The model can be applied in various discussion forums and various languages with a few adjustments, such as stop-word removal list and stemming algorithm. The data were obtained from discussion forum in a learning management system. The data consist of 1050 posts divided into three different course subjects: information systems, management, and character building. The reason for using several course subjects is to observe consistency of the model. F-measure was used to measure the effectiveness of the model, and the results showed that the TFM was consistent and effective to reveal the topic of discussion, with a good precision. However, the recall can still be increased in a further study.
Extractive document summarization using an adaptive, knowledge based cognitive model

Document summarization involves identifying the salient text in a document and creating a representative summary. The event-index cognitive model describes the human cognitive processes involved in generating situational models from a given text to comprehend that text. This paper proposes an adaptive, knowledge based event-index cognitive model that creates an extractive summary for a given document. The proposed cognitive model uses the hierarchical human memory model and emotion to create the extractive summary. The performance of the proposed cognitive model was compared with the existing state-of-art methods using the DUC (Document Understanding Conference) 2001 dataset and the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) summary evaluation tool. Under ROUGE-N, ROUGE-L, ROUGE-S and ROUGE-SU evaluations, the proposed cognitive model for document summarization achieved an improvement of 25%, 12%, 24% and 18% in mean F-measure values. The results are statistically verified as significantly better than existing state-of-art methods using one way ANOVA with post-hoc Tukey-HSD test. In addition, this paper discusses the possible future research directions in document summarization based on the proposed cognitive model.
Visual Stabilization of Balance in Virtual Reality Using the HTC Vive

Vision in real environments stabilizes balance compared to an eyes-closed condition. For virtual reality to be safe and fully effective in applications such as physical rehabilitation, vision in virtual reality should stabilize balance as much as vision in the real world. Older virtual reality technology was previously found to stabilize balance but by less than half as much as real-world vision. Recent advancements in display technology might allow for vision in virtual reality to be as stabilizing as vision in the real world. This study evaluated whether viewing a virtual environment through the HTC Vive—a new consumer-grade head-mounted display—stabilizes balance, and whether visual stabilization is similar to that provided by real-world vision. Participants viewed the real laboratory or a virtual replica of the laboratory and attempted to maintain an unstable stance with eyes open or closed while standing at one of two viewing distances. Vision was significantly stabilizing in all conditions, but the virtual environment provided less visual stabilization than did the real environment. Regardless of the environment, near viewing led to greater visual stabilization than did far viewing. The smaller stabilizing influence of viewing a virtual compared to real environment might lead to greater risk of falls in virtual reality and smaller gains in physical rehabilitation using virtual reality.
Convolutional Feature Fusion for Multi-Language Text Detection in Natural Scene Images

In this paper, we propose a feature fusion of convolutional layers in a VGG-16 network to detect multi-lingual text in natural scenes. A Fast-RCNN network is used to generate the text/non-text proposals, which are then passed to the recurrent neural network to connect them sequentially. The recurrent neural network is implemented within the convolutional network. The proposed network is trained on a combined ICDAR 2017 multi-lingual natural scene text dataset and a custom manually labeled Urdu text dataset. The text in the Urdu natural images usually has variations in font sizes, patterns, alignment and writing styles. Urdu text is formed by joining two or more characters and the words or sentences generally overlap with other text. Therefore, accurate detection of Arabic and Urdu text in natural scenes is complex, and challenging. The proposed method achieves favorable results for Arabic and Urdu text detection in natural scene images.
Real-Time Facial Feature Extraction Scheme Using Cascaded Networks

Facial landmarks such as eyes, nose, and mouth are the most prominent feature points on the face. So far, many works have been done for efficiently extracting such landmarks from facial images. Utilizing more feature points for landmark extraction usually requires more processing time, which has been an obstacle to real-time processing or video processing. On the contrary, utilizing a too small number of feature points cannot represent diverse landmark properties such as shape accurately. In this paper, we propose a deep learning based method for extracting popular 68 feature points for facial landmarks quickly and accurately. To do that, we first detect all the faces in the image by using a cascaded structure composed of relatively light Convolution Neural Networks(CNN). Then, we perform facial landmark extraction for each face, which reduces the processing time a lot. We performed several experiments to evaluate the performance of our method. We report some of the results.