Reviewerly/exHarmony huggingface dataset:
The early prediction of neonates mortality in Intensive Care Unit

Predicting mortality in Intensive Care Units (ICU) is an important issue because resources are very expensive and limited compared to the needs. Besides, decision-making is critical in the part of hospitals. Many scoring systems and machine learning models have emerged in previous years in this context. But the combination of an efficient prediction in the earliest hours of admission to the ICU remains a great and open challenge. Neonates are patients aged under 28 days. Neonatal ICU mortality prediction after the two first hours of admission using the machine learning tools is the main goal of this work. Data used includes information such as laboratory test results, demographic data and vital sign measurements. The proposed solution is composed of three steps: (1) The first step, is feature selection based on feature importance and recursive feature elimination. (2)The second step is patients' classification into mortal and alive using an ensemble of algorithms in order to keep the best performing ones. Tested algorithms were Classification and Regression Trees (CART), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM). LDA was the best performing one with an accuracy = 0.947 and AUROC = 0.97.(3)The last step is mortality time prediction using the Galaxy-Random Forest method (f-score=0.871).
DLB: Deep Learning Based Load Balancing

In this paper, we introduce DLB, a Deep Learning based load Balancing mechanism, to effectively address the data skew problem. The key idea of DLB is to replace hash functions in the load balancing mechanisms with deep learning models, which are trained to be able to map different distributions of workloads and data to the servers in a uniformed manner. We implemented DLB and deployed it on a practical Cloud environment using CloudSim. Experimental results using both synthetic and real-world data sets show that compared with traditional hash function based load balancing methods, DLB is able to achieve more balanced mappings, especially when the workload is highly skewed.
Similarity-based non-singleton general type-2 fuzzy logic controller with applications to mobile two-wheeled robots

Since the non-singleton fuzzy logic controllers (NFLCs) can effectively reflect the uncertainty brought by the inputs, they are used for balance control and position control of the mobile two-wheeled self-balancing robot (MTWSBR) in this paper. The similarity between the inputs and the antecedent fuzzy sets as the firing strength, that is, the similarity-based non-singleton fuzzy logic controller (Sim-NFLC), is proposed to deal with the problem of information loss caused by the standard non-singleton fuzzy logic controller (Sta-NFLC) in terms of the interaction of the inputs and antecedents. A comparative study among singleton fuzzy logic controllers (SFLCs), Sta-NFLCs and Sim-NFLCs, and interval type-2 fuzzy logic controllers (IT2FLCs) and general type-2 fuzzy logic controllers (GT2FLCs) are also shown. The simulation results show that the performance of Sim-NFLCs is better than that of SFLCs and Sta-NFLCs. The similarity-based general type-2 fuzzy logic controller (Sim-NGT2FLC) gets the best performance in handling the input uncertainty.
Museums’ Tales: Visualizing Instagram Users’ Experience

Social networks have renewed the ways audiences experience art and its spaces. The phenomenon concerns visitors, communicating their art experience through social media, and the artistic institutions, communicating their spaces and events. Sharing contents is a practice of fruition that allows the experience to be textualized. Our research focuses on how Igers represent themselves and their experience at museums, through a qualitative and quantitative description of the data collected. Our approach can support art institutions’ communication strategies.
Deep Learning as a Tool for Early Cinema Analysis

Visual Cultural Heritage has extensively been explored using multimedia methods, but has so far been limited to still images. In particular, Early Cinema has hardly been explored. We analyze the Desmet collection, a recently digitized collection of early cinema (1907-1916), in the context of intertitles. Intertitles played an important role in silent movies in order to convey the main narratives, and split the film into semantically meaningful segments. We first build several classifiers to detect these intertitles, and evaluate it on a gold standard collection annotated by an expert. We illustrate the usefulness of using Deep Learning methods to extract semantic features to analyze the role of intertitles in early cinema. Furthermore, we attempt to structure and map the narrative progression of a film with respect to the locations at which shots were filmed.
Combining Visual and Contextual Information for Fraudulent Online Store Classification

Following the rise of e-commerce there has been a dramatic increase in online criminal activities targeting online shoppers. Considering that the number of online stores has risen dramatically, manually checking these stores has become intractable. An automated process is therefore required. We approached this problem by applying machine learning techniques to extract and detect instances of fraudulent online stores. Two sources of information were used to determine the legitimacy of an online store. First, contextual features extracted from the HTML and meta information were used to train various machine learning algorithms. Second, visual information, like the presence of social media logos, was added to make improvements on this baseline model. Results show a positive effect for adding visual information, increasing the F1-score from 0.93 to 0.98 over the baseline model. Finally, this research shows that visual information can improve recall during web crawling.
End-to-End Model Based on Bidirectional LSTM and CTC for Segmentation-free Traditional Mongolian Recognition

A large number of Mongolian books and documents need to be stored and processed by computer. Optical Character Recognition (OCR) can convert a scanned document into text. However, the existing Mongolian OCR systems are realized by a glyph segmentation scheme. However, the glyph segmentation is much more difficult for several fonts. In this study, a segmentation-free approach based on end-to-end model is proposed for traditional Mongolian word recognition. In particular, the proposed model can extract features directly from the input word images and generate the corresponding recognition results (i.e. a sequence of letters). Experimental results demonstrate that the proposed end-to-end model outperforms the segmentation-based method. Moreover, the proposed model can solve the problem of out-of-vocabulary as well.
Image Restoration Using Optimized Weiner Filtering Based on Modified Tikhonov Regularization

In this paper, the optimization technique to estimate the regularization term of the Weiner filter is proposed. Normally, the noise term must be known prior to the calculation. However, in our approach this prior knowledge of noise term is unnecessary. The modified Tikhonov regularization is applied to control the regularization term of the Weiner filter for the best-restored image. This parameter will be changed following the degraded image although the regularization parameter of modified Tikhonov regularization is a fixed value. In our experiments, several sizes of Gaussian blur and noise are used to produce the degraded image. The Peak Signal to Noise Ratio (PSNR) is considered to measure the restored image quality. The results show that the noise regularization parameter of the Weiner filter can be adapted, and comparison results are superior to the traditional Lucy- Richardson algorithm and the regularized filter except the degraded images which have small blurring. Moreover, this method has also tolerance from the non-exact blur kernel.
Distributional Semantic Model Based on Convolutional Neural Network for Arabic Textual Similarity

The problem addressed is to develop a model that can reliably identify whether a previously unseen document pair is paraphrased or not. Its detection in Arabic documents is a challenge because of its variability in features and the lack of publicly available corpora. Faced with these problems, the authors propose a semantic approach. At the feature extraction level, the authors use global vectors representation combining global co-occurrence counting and a contextual skip gram model. At the paraphrase identification level, the authors apply a convolutional neural network model to learn more contextual and semantic information between documents. For experiments, the authors use Open Source Arabic Corpora as a source corpus. Then the authors collect different datasets to create a vocabulary model. For the paraphrased corpus construction, the authors replace each word from the source corpus by its most similar one which has the same grammatical class applying the word2vec algorithm and the part-of-speech annotation. Experiments show that the model achieves promising results in terms of precision and recall compared to existing approaches in the literature.
Calibration method for binocular vision with large FOV based on normalized 1D homography

Calibration method for binocular vision with large field of view (FOV) is the key technology in visual measurement system. The method based on 1D targets has been widely studied due to its high flexibility and convenience. In this paper, a calibration method based on normalized 1D homography matrix is proposed for robust camera internal parameters calculation with 1D target. Furthermore, based on combined one-dimensional targets, the calibration method for external parameter of binocular vision system is proposed. The numerical simulation is carried out to verify our method and to analyze the noise sensitivity, and the real data experiments are also developed. The results show that the internal parameter based on the normalized homography matrix is better than 0.1mm when the noise level is 2, and the precision of the measurement system is about 0.1mm in the depth direction of 3m. The method based on 1D target in this paper is effective and practical in the calibration of large FOV measurement system.
Research for Energy Optimized Resource Scheduling Algorithm in Cloud Computing Base on Task Endurance Value

In order to reduce the energy consumption of cloud computing systems, the situation of task execution in cloud systems was studied in this paper. When there were large-scale random tasks in the cloud computing system, the concepts of the task endurance value and the relaxation time were introduced, and the task waiting time was reduced by adjusting the task allocation resources. The task endurance value was calculated according to parameters such as the current network importance level of the tasks and the execution time of the tasks in the cloud system. Based on this, a resource scheduling algorithm based on task endurance value was proposed. The experimental results showed that the proposed algorithm could optimize the energy consumption of the cloud computing system.
Variable elimination strategies and construction of nonlinear polynomial invariant attacks on T-310

One of the major open problems in symmetric cryptanalysis is to discover new specific types of invariant properties for block ciphers. In this article, we study nonlinear polynomial invariant attacks. The number of such attacks grows as 22n and systematic exploration is not possible. The main question is HOW do we find such attacks? We have developed a constructive algebraic approach that is about making sure that a certain combination of polynomial equations is zero. We work by progressive elimination of specific variables in polynomial spaces and we show that one can totally eliminate big chunks of the cipher circuit. As an application, we present several new attacks on the historical T-310 block cipher that has particularly large hardware complexity and a very large number of rounds compared with modern ciphers, e.g., AES. However, all this complexity is not that useful if we are able to construct new types of polynomial invariant attacks that work for any number of rounds.
An approach for design optimization of 3R manipulator using Adaptive Cuckoo Search algorithm

In this study, the workspace volume of a 3R manipulator has been maximized using a biologically inspired optimization algorithm, namely Adaptive Cuckoo Search (ACS) algorithm. The proposed algorithm is tested on four diverse cases involving different constraints and without imposing any constraint. The outcomes of this study are compared with the standard results of different heuristics, such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Bacteria Foraging Algorithm (BFA), and Cuckoo Search (CS) algorithm. Statistical tests were performed to test the superiority of the proposed algorithm. Further, the cross-sectional area of the voids in the workspace has been estimated. The convergence study along with the coefficient of variation analysis identifies the critical kinematic parameters. A constraint conformation study has been performed to investigate the relative importance of the constraints. In addition, to ensure the applicability of the proposed algorithm in practice it is tested by using the kinematic parameters of two existing industrial robot manipulators (KUKA KR-30 and Mitsubishi MRP-700A). It has been found that the predicted results from the proposed algorithm for the two robot manipulators are in line with the actual values.
Group-level Cohesion Prediction using Deep Learning Models with A Multi-stream Hybrid Network

In this paper, we propose a hybrid deep learning network for predicting group cohesion in images. It is a kind of regression problem and its objective is to predict the Group Cohesion Score (GCS), which is in the range of [0,3]. In order to solve this issue, we exploit four types of visual cues, such as scene, skeleton, UV coordinates and face image, along with state-of-the-art convolutional neural networks (CNNs). We use not only fusion but also ensemble methods to combine these approaches. Our proposed hybrid network achieves 0.517 and 0.416 mean square errors (MSEs) on validation and testing sets, respectively. We finally achieved the first place on the Group-level Cohesion Sub-challenge (GC) in the EmotiW 2019.
Investigating the Validity of Ground Truth in Code Reviewer Recommendation Studies

Background: Selecting the ideal code reviewer in modern code review is a crucial first step to perform effective code reviews. There are several algorithms proposed in the literature for recommending the ideal code reviewer for a given pull request. The success of these code reviewer recommendation algorithms is measured by comparing the recommended reviewers with the ground truth that is the assigned reviewers selected in real life. However, in practice, the assigned reviewer may not be the ideal reviewer for a given pull request.Aims: In this study, we investigate the validity of ground truth data in code reviewer recommendation studies.Method: By conducting an informal literature review, we compared the reviewer selection heuristics in real life and the algorithms used in recommendation models. We further support our claims by using empirical data from code reviewer recommendation studies.Results: By literature review, and accompanying empirical data, we show that ground truth data used in code reviewer recommendation studies is potentially problematic. This reduces the validity of the code reviewer datasets and the reviewer recommendation studies. Conclusion: We demonstrated the cases where the ground truth in code reviewer recommendation studies are invalid and discussed the potential solutions to address this issue.
Multi-Image Hiding Blind Robust RGB Steganography in Transform Domain

Steganography is a widely-used technique for digital data hiding. Image steganography is the most popular among all other kinds of steganography. In this article, a novel key-based blind method for RGB image steganography where multiple images can be hidden simultaneously is described. The proposed method is based on Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT) which provides enhanced security as well as improve the quality of the stego. Here, the cover image has been taken as RGB although the method can be implemented on grayscale images as well. The fundamental concept of visual cryptography has been utilized here in order to increase the capacity to a great extent. To make the method more robust and imperceptible, pseudo-random number sequence and a correlation coefficient have been used for embedding and the extraction of the secrets, respectively. The robustness of the method is tested against steganalysis attacks such as crop, rotate, resize, noise addition, and histogram equalization. The method has been applied on multiple sets of images and the quality of the resultant images have been analyzed through various matrices namely ‘Peak Signal to Noise Ratio,' ‘Structural Similarity index,' ‘Structural Content,' and ‘Maximum Difference.' The results obtained are very promising and have been compared with existing methods to prove its efficiency.
Multi-Agent Coverage Search in Unknown Environments with Obstacles: A Survey

Coverage search problem is a hot research topic in computational geometry, robotics, and artificial intelligence. It not only involves basic theoretical problems such as environment modeling, optimal agent deployment, search path planning, algorithm design and analysis, but also has a wide range of applications such as the evacuation of dangerous areas, target search, and exploration of unknown environments. In view of this kind of problem, many excellent results have been achieved in recent years. In this paper, we provide a comprehensive survey on coverage search in unknown environments especially in obstacle-rich environments. The relevant works on environment modeling, agent deployment, search algorithm, etc. will be discussed and summarized. Finally, the challenges will be identified and some suggested future work will be highlighted.
Passive browser identification with multi-scale Convolutional Neural Networks

Browser identification is the act of recognizing web traffic through surveillance despite the use of encryption or anonymizing software. Although previous work has reported some promising results, browser fingerprinting is still an emerging technique and has not reached an acceptable level of performance. This paper presents a novel approach by using deep-convolutional-neural-network-based (deep CNN) learning model to extract the complete shape of traffic I/O graph signal in obtaining stable traffic characteristics, employing nonlinear multi-class classification algorithms to perform the task of browser identification. The approach is evaluated on a new dataset collected across a large number of websites. Extensive experimental results show that traffic characteristics which are learned from I/O graph by deep CNN are much more stable and discriminative than the metrics those are obtained from the early studies, and the approach achieves a practically useful level of performance with significant precision and recall. Additional experiments on the depth of deep CNN are provided to further examine the applicability of our approach. Our dataset is publicly available to facilitate future research.
Generative Model With Dynamic Linear Flow

Flow-based generative models are a family of exact log-likelihood models with tractable sampling and latent-variable inference, hence conceptually attractive for modeling complex distributions. However, flow-based models are limited by density estimation performance issues as compared to state-of-the-art autoregressive models. Autoregressive models, which also belong to the family of likelihood-based methods, however suffer from limited parallelizability. In this paper, we propose Dynamic Linear Flow (DLF), a new family of invertible transformations with partially autoregressive structure. Our method benefits from the efficient computation of flow-based methods and high density estimation performance of autoregressive methods. We demonstrate that the proposed DLF yields state-of-the-art performance on ImageNet 32 × 32 and 64 × 64 out of all flow-based methods. Additionally, DLF converges significantly faster than previous flow-based methods such as Glow.
Electric Vehicles Privacy Preserving Using Blockchain in Smart Community

During the process of charging, electric vehicle’s location is usually revealed when making payment. This brings about the potential risk to privacy of electric vehicle. We observe that the trade information recorded on blockchain may raise privacy concern and therefore, we propose a blockchain oriented approach to resolve the privacy issue without restricting trading activities through $$(\epsilon , \delta )$$ -differential privacy. The proposed scheme does not only preserve the electric vehicle’s location; however, prevents semantic, linking and data mining based attacks. Simulation results show that as the privacy level increases, the risk revealing decreases as well.
Separation of Multicomponent Chirp Signals Using Morphological Component Analysis and Fractional Fourier Transform

A chirp signal is a large-bandwidth signal which is widely used in engineering. In many applications, it is necessary to decompose a mixed chirp signal into its components. However, the traditional Fourier transform method cannot process a mixed chirp signal when its components intersect in the joint time-frequency domain. Combining the advantages of the morphological component analysis (MCA) with multicomponent signal processing and the fractional Fourier transform (FrFT) in chirp signal processing, this letter proposes the MCA-FrFT method to decompose a multicomponent chirp signal. First, the cost function is defined using the FrFT and optimized by the split augmented Lagrangian shrinkage algorithm (SALSA), and then, all the morphological components are obtained. The proposed method is verified by simulations, and simulation results show that the proposed method has good performance in separating the multicomponent chirp signals into components. Besides, the proposed method is evaluated experimentally in the sea target detection, and the experimental results confirm that the proposed method can not only extract the low observable targets from a heavy sea-clutter environment but also separate them from each other.
An Interactive Mobile Technology to Improve the Usability of Exam Application for Disabled Student

Mobile devices have become a commonly used device as information technology develops. Mobile devices are also equipped with sensors that can improve device functionality. In the field of education, it can be used as an alternative control method and provides accessibility for users when used in human computer interface solutions. With mobile devices which has internal inertial sensors like accelerometer, gyroscope, and magnetometer, a head movement control method has been developed to determine the movements that can be used to navigate the application. This method changes the function of hand-based input into head-based. The combination of Head-Mounted Displays (HMDs) with mobile devices, provide an innovation of new low cost of human-computer interaction. It can be used with six type of head poses movement; move to the left, move to the right, head looking down, head looking up, tilt head toward left shoulder, and tilt head toward right shoulder. In this study, we concern to design head poses movements that are appropriate to the process of answering the exams and testing the level of user usability. Usability testing is used by disabled people to answer the test, with the usability score is 81,75 from 100 and classified as "excellent" category