Reviewerly/exHarmony huggingface dataset:
Event Unit Analysis: A Methodology for Anticipating Processing Demands of Complex Animated Diagrams

The addition of explicit, faithfully represented dynamics to diagrams that depict complex behaviours may negatively affect viewers’ information processing and prejudice their comprehension of the referent subject matter. This presentation introduces a methodological tool for identifying and characterizing likely sources of the negative consequences that can arise from an animated diagram’s dynamics. Event Unit Analysis offers a systematic way to document these sources so that they can be minimized by implementing changes in how animations are designed. This analytical methodology underlies the development of a novel animation design approach that significantly improves viewer comprehension over that obtained using conventionally designed animations. The origins of event unit analysis in the theoretical framework of the Animation Processing Model and its development as a tool for analyzing increasingly sophisticated dynamics are described. Its potential breadth of its application and opportunities for further elaboration are illustrated through two contrasting types of content.
Conjugated mathematical model for optimal location of industrial objects

Abstract The urgent problem related to solving the problems of monitoring and predicting the ecological state of the air basin in industrial regions is considered in the paper. It provides a brief review of scientific publications devoted to these problems. A mathematical model has been developed to study, predict and monitor the concentration of harmful substances in the atmosphere. The model takes into account weather and climate conditions, soil erosion, physicomechanical properties of aerosol particles, and other factors affecting the process under consideration as a whole; it is designed for optimal location of newly constructed production facilities.
Adaptive Structure Evolution Convolutional Neural Network for Image Recognition

A new method based on adaptive evolution strategy to automatically search for the optimal feature maps connections between the sub-sampling layer and the convolutional layer of convolutional neural networks without experience is proposed. The purpose of this method is to remove redundant connections in discrete spaces, reduce the complexity of CNN models, and improve the accuracy of image recognition to a certain extent, where the adaptive algorithm can adjust the crossover probability and mutation probability according to the evolution process. The optimal CNN by this method compare with CNN in originally connecting structures and existing methods in the Fashion-MNIST database and MIAS medical image database, this method can effectively optimize the connecting structure, accelerate convergence and improve recognition accuracy.
Transfer Learning for Recognizing Face in Disguise

Face recognition is a method in Machine Learning to recognize objects in the picture or video. Humans have a memory to recognize other people and recognize some objects like animals, plants, living objects, and non-living objects. However, how the computer does that although it has memory? Machine Learning is the technique or method in Computer Vision that can be used, so computers can understand one person's face to another person contained in the image or video. In this paper, the author proposes about testing some popular Convolutional Neural Network (CNN) Model Architecture to see which one is better to recognize the person face dataset in disguised. The author uses the “Recognizing Disguised Faces” dataset to distinguish 75 classes of faces, and then try to train and test how accurate it can be recognized by the machine, where it will be useful to anyone who needs to explore and develop an Architecture of Deep Learning. This paper is expected to contribute to the field Machine Learning related algorithm that is used to solve the problem in image classification. The experimental results show significant improvement using transfer learning in VGG Models. We then conclude that ImageNet weight best used for face-recognizing using VGG Models.
It's so Real: Psychophysiological Reaction towards Virtual Reality Exposure

Virtual reality (VR) technology has been utilized in various types of scientific fields. In psychology, VR is used as an alternative intervention particularly exposure therapy, in which therapists often have difficulties in maintaining client confidentiality and security when confronting them with feared situations, especially in public places. VR allows therapist to present a three-dimensional stimulus virtually, so that the risks of safety can be minimized. This study aimed to explore the effect of height stimulus presented with VR technology on physiological and psychological reactions. The research method used was a non-random pre-test and post-test experiment. Physiological reactions were measured using the ProComp5 Infiniti biofeedback system with three sensors: heart rate, respiratory rate, and skin conductance. Participants were confronted with a height environment simulated through a Head-mounted display (HMD) Oculus Rift Development Kit 2. The results indicated that there were changes in their psychological and physiological reactions. It could be concluded that exposure through VR, which is very close to brain's information processor accompanied by various sensory stimuli, is able to create conditions where individuals can really feel as if being at a real height. Changes in psychological and physiological reactions are discussed in this paper.
A Novel Method for Curved Scene Text Detection

Scene text detection in the wild has gained momentum due to the rapid development of deep learning. Previous methods have excellent performances on detection horizontal and multi-oriented text, thereby the research focus gradually shift into curved text detection. To resolve this problem, we propose a novel curved text detection method by combining segmentation with regression in this paper. For each curved text instance, boundary points, text central region is predicted through a Fully Convolution Network with multi-layer feature fusion to locate text. Experiment results on two public benchmarks for curved scene text detection demonstrate that our method obtains accurate description of text boundary and achieves state-of-art performance.
Synthetic Speech Evaluation by 2D GMM Classification in Pleasure-Arousal Scale

The paper is focused on a description of a system for automatic evaluation of synthetic speech quality based on two-dimensional detection in the Pleasure-Arousal (P-A) scale. The original speech material of a speaker used for synthesis is compared with the synthesized one to find similarities/differences between them. For continual P-A detection, the Gaussian mixture model (GMM) classifier is used. The GMM models of the P-A classes are created and trained using the sound/speech material from the database labelled directly in the P-A scale without any relation with the used original speech or the tested sentences. The basic experiments confirm the principal functionality of the developed system. Additional analysis shows the great importance of the proper selection of the number of mixtures, and the used type of the sound/speech database for GMM models building. The obtained objective evaluation results are highly correlated with the subjective ratings of human evaluators.
Named Entity Recognition in Semi Structured Documents Using Neural Tensor Networks

Information Extraction and Named Entity Recognition algorithms derive major applications related to many practical document analysis system. Semi structured documents pose several challenges when it comes to extract relevant information from these documents. The state-of-the-art methods heavily rely on feature engineering to perform layout-specific extraction of information and therefore do not generalize well. Extracting information without taking the document layout into consideration is required as a first step to develop a general solution to this problem. To address this challenge, we propose a deep learning based pipeline to extract information from documents. For this purpose, we define ‘information’ to be a set of entities that have a label and a corresponding value, e.g., application_number: ADNF8932NF and submission_date: 15FEB19. We form relational triplets by connecting one entity to another via a relationship, such as (max_temperature, is, 100 degrees) and train a neural tensor network that is well-suited for this kind of data to predict high confidence scores for true triplets. Up to 96% test accuracy on real world documents from publicly available GHEGA dataset demonstrate the effectiveness of our approach.
Implementation of the Montgomery Modular based RSA Algorithm on FPGA

Lifting and modulus operations are the main operations on the RSA algorithm which require long computation time. Implementation of the operation of the lift and modulus on hardware devices requiring more resources than other arithmetic operations. Montgomery modular multiplication, the method which can be used to simplify the operation of lift and modulus, is implemented on FPGA, to speed up the computing process. The implementation results in this study, which is done using VHDL on the Xilinx Artix 7 series FPGA, were able to work at a maximum frequency of 133.76 MHz, requiring 17.66% LUT (11,195 of 63,400) and 7.14% of IOBs (15 of 210).
Time-optimal Path Planning for Autonomous Vehicles Based on Bi-Level Programming

In this paper, a path planner is proposed for autonomous vehicles to find the time-optimal path that visits a given set of nodes in a directed graph, which is widely regarded as a NP-hard problem. Toward this end, a bi-level programming model is introduced to formulate and solve the underlying problem. In particular, an improved Dijkstra algorithm named directed-Dijkstra, considering turning time of vehicles, is proposed to obtain the time-optimal paths between all given nodes. Then, Branch and Bound-LKH hybrid algorithm is presented to allocate the given set of nodes in a reasonable sequence. Finally, the global time-optimal path is obtained by concatenating the corresponding sub-paths. Simulation results verified the effectiveness of the proposed scheme.
Practical Multisource Transfer Regression With Source–Target Similarity Captures

A key challenge in many applications of multisource transfer learning is to explicitly capture the diverse source-target similarities. In this article, we are concerned with stretching the set of practical approaches based on Gaussian process (GP) models to solve multisource transfer regression problems. Precisely, we first investigate the feasibility and performance of a family of transfer covariance functions that represent the pairwise similarity of each source and the target domain. We theoretically show that using such a transfer covariance function for general GP modeling can only capture the same similarity coefficient for all the sources, and thus may result in unsatisfactory transfer performance. This outcome, together with the scalability issues of a single GP based approach, leads us to propose TC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MS</sub> Stack, an integrated framework incorporating a separate transfer covariance function for each source and stacking. Contrary to typical stacking approaches, TC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MS</sub> Stack learns the source-target similarity in each base GP model by considering the dependencies of the other sources along the process. We introduce two instances of the proposed TC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MS</sub> Stack. Extensive experiments on one synthetic and two real-world data sets, with learning settings up to 11 sources for the latter, demonstrate the effectiveness of our approach.
Semantic Segmentation Method Based on Residual and Multi-Scale Feature Fusion

In order to solve the problem of insufficient use of context information and lack of detail information in image semantic segmentation, this paper proposes an RMFU network model based on residual module and multi-scale feature fusion. RMFU network uses cross entropy and mean intersection over union as loss function. RMFU network obtains the fine semantic segmentation results by using fully connected Conditional Random Field to optimize the initial segmentation results. The test results on Pascal VOC 2012 dataset show that RMFU network is better than FCN network in SSIM and COSIN. RMFU network effectively uses multi-scale feature information. It improves the precision of image semantic segmentation and optimizes the image segmentation effect.
Clustering collaborative filtering recommendation algorithm of users based on time factor

Collaborative filtering recommendation algorithm is the most widely used recommendation algorithm in recommendation systems. Based on the collaborative filtering recommendation algorithm, a time-based user clustering collaborative filtering recommendation algorithm is proposed. In this novel algorithm, all users and their interest changes are considered. There are three mainly improved steps in this novel algorithm. Firstly, the user rating time is added to increase the similarity between users when users are clustered. Secondly, in order to reduce the search space of similar users, only the nearest neighbor with a high similarity to the target user can be found in the cluster by adding the rating time. Finally, the traditional collaborative filtering recommendation algorithm is used to recommend the target users in the clustered data set. By using the MovieLens100K dataset, some experiments show the validity of the algorithm.
Observational Advantages and Occurrence Referentiality

Logical diagrams are known to have certain advantages over sentential notations for particular reasoning tasks: using a diagram may make logical consequences directly evident, when these consequences are “hidden” in an equivalently expressive sentential language. This phenomenon is known as a “free ride” or “observational advantage”. Where does this advantage come from, and why? We answer this question by distinguishing two general kinds of logical languages: occurrence-referential languages, in which sameness of reference (for sentential and predicate variables) is determined by the sameness of variable occurrence, and type-referential languages, in which sameness of variable type determines sameness of reference. We explain that it is the occurrence-referential nature of some languages that explains for their observational advantage over equivalently expressive type-referential languages.
A Click Fraud Detection Scheme Based on Cost-Sensitive CNN and Feature Matrix

Around 30% revenue has been wasted due to different types of advertising fraud. In view of the continuous emergence of fraud and the improvement of fraudulent means, a click fraud detection of cost-sensitive convolutional neural network (CSCNN) based on feature matrix is proposed. In order to capture the pattern of click fraud, priori probability is introduced and a new feature set is derived from the knowledge of entropy. Then, using cost-based sampling method and threshold-moving cost sensitive learning algorithm to solve unbalanced data training problems. Features are transformed into feature matrix by different time windows to fit the input of a convolutional neural network. Finally, considering the convolution kernel size, the number of convolution kernels and the pooling size of different network structures in this work, effective CSCNN structure was chosen. Experiments on real-word data show that this method can complete click fraud detection effectively, and can complete multi classification without increasing the complexity of the model. A new effective method was provided on click fraud detection in mobile advertisement.
Correlation Gaussian Particle Filter for Robust Visual Tracking

Visual tracking plays a significant role in computer vision. Correlation filter (CF) based visual tracking algorithms have made great progress owing to their high efficiency. However, the traditional CF based tracking algorithm uses the fixed-size template, when the target changes in size, the tracker cannot track the object accurately. To address the issues, A novel accurate position and scale estimation algorithm that combination kernel correlation filter (KCF) with Gaussian particle filter (CGPF) for visual tracking has been proposed. The algorithm estimates the position of the target by KCF, and then corrects the position by a Gaussian particle filter algorithm. About the problem of scale variation information in the KCF, A scale correlation filter is constructed, and it is used to identify the scale with the maximum response to select the target size. Finally, The comprehensive experiments on OTB-50 visual tracking benchmark datasets are performed. The experiments results show that the proposed approach outperforms the KCF algorithm. And the results also prove availability of our method.
Computationally Efficient Fine-Grain Cube CP-ABE Scheme with Partially Hidden Access Structure

In ciphertext policy attribute based encryption (CP-ABE), the private key is associated with the number of attributes and the data is encrypted with access policy. Most existing CP-ABE schemes suffer from the issue of having long ciphertext size. The ciphertext depends on the number of attributes in the access policy. Also, Most CP-ABE does not consider the recipients’ anonymity, the encryptor-specified access structure is not hidden in the ciphertexts. Zhou et al. [1] proposed a construction of privacy preserving CP-ABE with constant ciphertext. However, [1] does not consider the importance of the attributes. On the other hand, existing weighted CP-ABE [2] consider the importance of the attributes but does not protect the recipients’ anonymity and the efficiency, it requires increasing the ciphertext size with increasing number of attributes. In this paper, we provide an affirmative answer to the above issue; we proposed a new construction of CP-ABE named as Computationally efficient Fine-Grain Cube CP-ABE with Partially Hidden Access Structure(denoted as Cube CP-ABE). CUBE CP-ABE reduces the ciphertext to constant size irrespective of the number of attribute in the access policy. Second, it protects the recipients’’ privacy. Third, it applies the idea of the weighted value. Forth, it is more granular than [2]. As far as we know, CUBE CP-ABE is the first construction with such properties. Our scheme is CPA-secure under Bilinear Diffie Hellman Exponent Assumption.
A Research on Rebalance Algorithm for Imbalanced Data Based on Probability Graph

In imbalanced data, the results of classification are often biased toward the majority class, which reduces the classified accuracy of the classifier. Based on this, this paper proposes a probability graph rebalancing algorithm based on Gaussian Mixture Model-EM (GMM-EM) algorithm for imbalanced data. The probability density functions of the minority class and majority class are obtained by the GMM-EM algorithm. Then according to the probability graph (μ – σ graph) of the dataset, the security concept of the minority class is put out, which determines the different generating weights of minority instances. After that, oversampling is performed using the new algorithm above . The proposed method not only considers the generating direction of minority class, but also ensures the consistency of the probability distribution of the data before and after the balance. Finally, the balanced dataset is classified using the decision tree classifier. Experimental results show that the new algorithm is more efficient than other existing methods.
Comparison Several Hue Preserving Image Enhancement Methods from Under Exposure Image

An image with poor contrast, improper brightness, and low saturation generates a lot of missing information. This problem can be solved with enhancing the image saturation without changing the hue and range values of the image. In this paper, we compared three different hue preserving methods: Hue Preserving Image Enhancement without gamut problem, Hue and Range Preserving RGB Image Enhancement and Modified Hue Preserving Image Enhancement without gamut problem. The results were evaluated using Peak Signal Noise to Ratio as one of the references evaluation score and Blind Referenceless Image Spatial Quality Evaluator as the natural image evaluation score. From three different methods, Hue and Range Preserving RGB Image Enhancement give the best result compared the other two.
Collision Free Path Planning based on Local 2D Point-Clouds for MAV Navigation

The usage of Micro Aerial Vehicles (MAVs) in different applications is gaining attention, however one of the main challenges is to provide collision free paths, despite the uncertainties in localization, mapping, or path planning. This article proposes a novel collision-free path planner for MAVs navigation in confined environments, while not being dependent on the information of the localization, only relying on 2D local point-cloud data. The proposed backup path planner generates velocity commands for a trajectory-following controller, while guaranteeing a safety distance from all points in the local-point-cloud. The proposed method considers the kinematics of the MAV and can be extended to any robotics application, such as ground vehicles. The proposed method is evaluated in a Gazebo simulation environment and successfully provides a collision-free navigation.
Compromising Emanations From VGA Interface

The laboratory stand configuration in anechoic chambers for testing compromising emanations has been shown in the article. To evaluate the possibility of information leakage from VGA graphical interface the article has focused on revealing the possibility of eavesdropping of radiated compromising emanation during image transmission through VGA graphical interface. The influence of the intermediate frequency filter bandwidth of the infiltration receiver and the process of averaging of compromising emanations signal on the quality of the reproduced image generated by the tested VGA interface is presented.
Imaging in Breast Cancer: Use of Magnetic Resonance Spectroscopy

Magnetic resonance spectroscopy (MRS) is used nowadays with increased specificity to distinguish between malignant and benign breast lesions. Objective: To determine the diagnostic accuracy of MRS in malignant breast lesions. Methodology: Newly diagnosed patients (n=158) having breast lesions diagnosed on ultrasound and mammography were enrolled to conduct the present study at Bahawal Victoria Hospital, Bahawalpur, Pakistan for six months. Enrolled patients were informed and consent was taken. Every patient underwent proton MRS using a 1.5 Tesla MR system. Fast scans in various planes were obtained. Mean ± standard deviation (SD) was given for age, size of the lump, and duration of the disease whereas frequency and percentage were given for benign and malignant breast lesions by SPSS version 26. A significant p-value was ≤0.05. Results: The mean age of patients was 41.27 ± 5.48 years. The diagnosis of malignant breast lesions in 80 (50.63%) patients was shown by MRS whereas histopathology showed malignancy in 83 (52.53%) cases. Conclusion: MRS is an accurate diagnostic modality for malignant breast lesions.