Reviewerly/exHarmony huggingface dataset:
Coarse-to-Fine Supervised Descent Method for Face Alignment

Supervised Descent Method (SDM) is an efficient and accurate approach for facial landmark locating and face alignment. In the training phase, it requires a large amount of training samples to learn the descent directions and get the corresponding regressors. Then in the test phase, it uses the corresponding regressors to estimate the descent directions and locate the facial landmarks. However, when the facial expression or direction changes too much, generally SDM cannot obtain good performance due to the large variation between the initial shape (the initial shape of SDM is the mean shape of the training samples) and the target shape. Therefore, we propose a coarse-to-fine SDM (CFSDM) method to improve the accuracy of the test results. This method predicts the approximate coordinates of the facial landmarks with a simple CNN (Convolutional Neural Network) network (here we introduce the channel-wise attention mechanism, which can predict the coordinates of the landmarks more accurately with a relatively simple structure) in advance, and then SDM will take the coordinates as its initial shape’s coordinates, which reduces the distance between the initial shape and the target shape, thereby solving the problem that SDM cannot achieve good results when the facial expression or direction changes greatly.
Square-Root Cubature Kalman Filters for Training Recurrent Type-2 Fuzzy Neural Networks

This paper proposes a novel learning algorithm benefitting from square-root cubature Kalman filters for training recurrent interval type-2 fuzzy neural networks. The recurrence property in this network is feeding the output of each input to itself. Simulations results show the effectiveness of the proposed network and the proposed learning algorithm.
Smart Survey Tool: A Multi Device Platform for Museum Visitor Tracking and Tracking Data Visualization

This paper describes the Smart Survey Tool, a novel multi-device application for museum visitor tracking and tracking data visualization. The application allows museum staff to capture detailed information describing how visitors move around an exhibition and interact with individual exhibits. They can then visualize the results of tracking either on a single mobile device or with multiple mobile devices connected to a large display. The platform uses orthogonal views of the exhibition space for tracking and visualization, with a chess-piece icon to represent visitors during tracking, and curved semi-transparent lines with animated semi-circles to communicate the path and direction of visitor movement. Our visualization is novel in its use of an orthogonal projection for pedestrian tracking and animation to communicate the flow of visitors around the exhibition space, as well as allowing users to dynamically switch between views representing different groups of visitors. The design of our application was informed through an extensive requirements analysis study conducted with Nanjing Museum and evaluated by conducting expert interviews with museum managers who considered that the application allowed for more effective and efficient recording and analysis of visitor tracking data.
About Fully Homomorphic Encryption Improvement Techniques

Fully homomorphic encryption schemes (FHE) are a type of encryption algorithm dedicated to data security in cloud computing. It allows for performing computations over ciphertext. In addition to this characteristic, a verifiable FHE scheme has the capacity to allow an end user to verify the correctness of the computations done by a cloud server on his encrypted data. Since FHE schemes are known to be greedy in term of processing consumption and slow in terms of runtime execution, it is very useful to look for improvement techniques and tools to improve FHE performance. Parallelizing computations is among the best tools one can use for FHE improvement. Batching is a kind of parallelization of computations when applied to an FHE scheme, it gives it the capacity of encrypting and homomorphically processing a vector of plaintexts as a single ciphertext. This is used in the context of cloud computing to perform a known function on several ciphertexts for multiple clients at the same time. The advantage here is in optimizing resources on the cloud side and improving the quality of services provided by the cloud computing. In this article, the authors will present a detailed survey of different FHE improvement techniques in the literature and apply the batching technique to a promising verifiable FHE (VFHE) recently presented by the authors at the WINCOM17 conference.
A Stain Normalization Method for the White Blood Cell Image

In this paper, a new system is designed to translate dyeing style. The whole system includes blood smears preparation, image acquisition and segmentation, finally, training a dye style transmission network. In the system, we designed a new network based on Deep Learning for the leukocyte dyeing style translation. The network can automatically extract the features of sample images during training and reduces the influence of lighting, dyeing, and taking a photo by different devices. In addition, we also build two datasets, Base dataset and Target dataset.
Fuzzy Cluster Algorithm of Multi-dimensional Dynamic Primitives for Automatic Face Recognition Based on Multi-center Matching

Automatic face recognition is an important technology giving a machine the capacity to recognize a person's identity through the computer vision. At present, there is a main problem of automatic face recognition, shown as following: the semantic features extracted by primitives are redundant and the recognition accuracy of faces with sparse characteristics is low. Therefore, this thesis puts forward the fuzzy cluster algorithm of multi-dimensional primitives for automatic face recognition based on multi-center matching. It designs the algorithm aiming at the fuzzy initial center problems of cluster during picture segmentation. By constructing a sparse construction of multi-center parallel clustering, and simultaneously converging various semantic features for many feature semantics, the algorithm improves the time-effect of recognition. Additionally, multi-center cluster reduces the feature quantity of face recognition. Through the simulation verification, the algorithm of the thesis can effectively improve the recognition speed and the accuracy.
PMU Analytics for Power Fault Awareness and Prediction

The analysis of electrical wave measurements on an electricity grid obtained through Phasor Measurement Units (PMUs) offers a unique opportunity to detect power system faults in real time. The goal of this study is to evaluate the use of classification and forecasting models to recognize and predict power system faults in streaming PMU data. The evaluation of these models built with simulated PMU data from a real-world power network demonstrates that both classification and forecasting can be successfully used in the detection and prediction of single line and three phase to ground faults. The results obtained indicate that the same methodology can be applied to other types of power system faults.
Design of Environmental Monitoring System for Sports Stadiums and Gymnasiums of Jiangxi Teachers College in China

Abstract With the application of basketball hall, swimming pool, comprehensive gymnasium and field hall in the new campus of Jiangxi teachers college, the monitoring and construction of the safe environment of sports teaching venues have aroused the common concern of schools, sports bureaus and other relevant departments. Taking the gymnasium of Jiangxi teachers college as an example, this paper proposes a gymnasium environment monitoring system based on ZigBee protocol. The system achieves data transmission and processing, information sending and reminding services through temperature, humidity, smoke, CO2 sensors and hardware devices such as ZigBee gateway. The application of this system in school gymnasiums and other fitness venues can not only ensure the physical and mental health of students to a greater extent, but also make the management of the gymnasium more efficient, safe and convenient, and solve the shortcomings of traditional management methods, so as to provide scientific decision-making basis for the management of university gymnasiums in China.
Network Representation Learning Method Based on Spatial-Temporal Graph in Dynamic Network

Network representation learning, which aims to learn the low-dimensional representations of vertices, has attracted considerable research efforts recently. Most existing network representation learning methods mainly focus on static networks, which extract and condense the network information without temporal information. However, in the real world, networks keep evolving and the evolution process contains a lot of important information. But it is difficult to quantify the evolution of node structure in dynamic networks. To address this problem, we propose a dynamic network representation learning method, call it as RWR-STNE. The proposed framework makes use of structural properties of networks at current and previous timestamps to learn effective node representations. This method first utilizes the historical information obtained from the network snapshots at past timestamps to create a spatial-temporal trajectory graph. Then it uses random walks with restart on the graph at a given timestamp as well as on graphs from past timestamps to capture the spatial-temporal behavior of nodes. The experiments on three real-world temporal network datasets show that the proposed learning method is valid. It also demonstrates the advantages of the method compared with both the state-of-the-art embedding techniques and several existing baseline methods.
Exploring the Change in Nature and Efficacy of Learners’ Questions Through Progressive Interaction with the Stanford Mobile Inquiry-based Learning Environment (SMILE)

Through the use of the Stanford Mobile Inquiry-based Learning Environment (SMILE), students are provided with a digital platform to generate questions, with the purpose of clarifying conceptual doubts, as well as to challenge and learn from each other. This paper seeks to find out whether SMILE has any effect on the nature and efficacy of learners’ questions in Singapore and assess whether SMILE is an effective and reliable tool in helping students achieve better conceptual understanding and accuracy. It also demonstrates how the use of SMILE can be translated as well as sustained in schools, in alignment with the ecological framework which is the thesis of the present book. SMILE lessons were conducted at a secondary school during Physics lessons, with students being encouraged to generate questions related to the subject. Survey data was collected from both teachers and students, and the questions generated by students from three classes were analysed according to content relevance, conceptual accuracy as well as question type, the latter of which is categorised by Bloom’s Taxonomy. There do not seem to be significant changes with regard to the percentage of accurate questions generated, nor the proportion of higher-order thinking questions per student. Nevertheless, both teachers and students are fairly optimistic about the use of SMILE in engaging students in critical thinking. This finding correlates with data indicating an increase in variation of question type over time.
Image Captioning with Emotional Information via Multiple Model

The area of image captioning has been significantly promotion on the quality of generated sentence in recent years. However, machine generated sentences often lack emotional or sentimental dimension and lose a lot of high-level sentiment information in CNN stage, which may be a clue understanding the state of the objects. To improve that, we propose a new captioning architecture (e-LSTM), which designed to integrates the experience learned from multiple models trained on different datasets. In order to explore the influence of emotional features on target recognition, we designed 4 variants of models. Experiments on COCO image captioning dataset prove that the sentences generated by our model are more vivid and the evaluation of VAEDR quantifies this advantage. Most remarkably, the promotion on classic evaluation matrix proves our method can use the additional input as supplementary information to capture the state of objects in the image more accurately.
Teaching Materials of Rocket Stoves for Environmental Education and Investigation of Educational Effect

Conventional environmental education is mainly of nature experience type, and many require time and place. A rocket stove is an efficient and hot burning portable stove using wood fuel. Especially since the Great East Japan Earthquake, there are many reports made as part of environmental activities. However, rocket stoves are not well known generally in Japan. In this study, we made a small rocket stove as a teaching material for environmental education. Furthermore, environmental education using small rocket stove was conducted at elementary and junior high schools and its educational effect was investigated. As a result, in environmental education, not only promotion of the interests of the environment and science but also the promotion of interest in disaster prevention was great effect. Small rocket stove had a small entrance and could not supply air, making stable combustion difficult. However, by improving the combustion section and reviewing the fuel, stable combustion became possible.
A 3D-Cellular Automata-Based Publicly-Verifiable Threshold Secret Sharing

In secret sharing, a secret is distributed between various participants in a manner that an authorized group of participants in the appropriate access structures can recover this secret. However, a dealer might get corrupted by adversaries and may influence this secret sharing or the reconstruction process. Verifiable secret sharing (VSS) overcomes this issue by adding a verifiability protocol to the original secret sharing scheme. This chapter discusses a computationally secure publicly verifiable secret sharing scheme constructed using the three-dimensional cellular automata (3D CA). The initial configuration of the 3D CA is the secret. The following configurations are devised to be the shares distributed among the participants. Update mechanisms and various rules make it hard for an adversary to corrupt or duplicate a share. To make it even more efficient, the authors added a verifiability layer such that a dealer posts a public share and a private share to each shareholder. The NIST test suite has been used to calculate the randomness of the shares.
Text Spotting in the Wild with Embedded Device

Abstract Detecting and recognizing text in natural scenes (e.g. streets, restaurants, shops, etc.) could be a part of an artificial intelligence system, especially with regard to the speech synthesis system. Properly detected text is passed to a recognition stage and then to the speech synthesis system, which translates text to speech. Research is carried out for the ‘Toucan Eye’ project — embedded device with artificial intelligence system able to help people with impaired sight. Due to constrained resources and abilities of embedded devices, criteria for text spotting must be met. First criterion is quality of detected and recognized regions with text and the second is time spent on both operations. Particular stages of the system and chosen methods of text spotting under aforementioned constraints are presented.
Legislation on the New Types of Cybercrime and Its Application

The new types of cybercrime have different features from traditional “cumulative crimes.” The application of the relevant provisions of the Criminal Law has run into difficulties, and the relevant theoretical explanations of substantive preparatory crime and being an accomplice in crime find it hard to achieve self-consistency. Legislation on the new types of cybercrime is a legitimate response to recent changes in this area. Utilizing the particular techniques of cybercrime and the circumstantial requirement of limiting the amount of criminal harm is the key to satisfying the conditions for offences warranting criminal punishment. The harmfulness of the new types of cybercrime is not great; the law’s circumstantial requirements are flexible, and the criteria for judicial application are unclear, so the law’s provisions are seldom invoked and the sphere of criminal activity is unduly expanded. In order to give full play to the effectiveness of this legislation, we should formulate reasonable judicial rules that accord with the constitution of cybercrimes, narrow down the constituent elements of such crimes in a reasonable way, and categorize and limit the interpretation of circumstantial requirements.
Frequent Subtree-Based Event Language Expression Pattern Discovery

Event language expression plays an important role in the event ontology. Event language expression patterns are the part of the event language expression, which indicate the customary habits when human describe the events in natural language. They are implicit in texts and can be represented by frequent subtrees. For the sentences that describe the same event class in CEC 2.0, firstly, we did semantic dependency parsing for each sentence and got the semantic dependency graphs by LTP, then transform them to semantic dependency trees, and mined frequent subtrees from these semantic dependency trees by the PETreeMiner algorithm. We verified the validity of the mined frequent subtrees through detecting event elements in texts with them. The value of F1 is 76.4%.
Image of Jurisprudence Reconstructed to Enhance Innovation: Liability Allocation for Improved Predictability

Law is generally imaged to be stickler to human rights, often taking its stance to be against technological developments which possibly infringe “humane territory”. In this chapter, however, the author tries to show the positive effect of law to enhance innovation, by designing proper allocation of responsibility through legislation. In the case of advancing artificial intelligence (AI) technology, for instance, the “trolley problem” is often used to indicate the difficulty of choice between two alternatives, and thus to show the danger to leave such decision to non-human beings as AIs. But considering that the system of “legal bodies” as companies could be seen as an artificial reality to establish something to hold rights and duties to be liable to the results of their action, we could notice the function of law to establish social institutions apart from the natural order, to sustain or enhance welfare and happiness of the society. Taking example from the problem of traffic accident committed by level 4 auto-driving car, the author tries to discuss the alternatives on the allocation of responsibilities and liabilities, and to show that some of them will surely suppress the further technological innovation, concluding that law may work as a measure for the society to avoid falling into such pitfalls.
Visibility Attribute Extraction and Anomaly Detection for Chinese Diagnostic Report Based on Cascade Networks

In the positron emission tomography/computed tomography (PET/CT) image diagnosis report, the semantic analysis of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">image findings</i> section is an important part of the automatic diagnosis of medical image, which is an essential step for extracting keywords and abnormal sentences in the diagnostic report. To this end, this paper combines visibility attribute extraction network (VAE-Net) and bi-directional gated recurrent unit (BiGRU) into cascade networks to solve the tasks of attribute extraction and anomaly detection. First, a visibility attribute (VA) is defined to summary the vocabulary into 12 patterns based on the language characteristics in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">image findings</i> . Second, a visibility attribute extraction network (VAE-Net) is developed to automatically extract VA from word embeddings, which is composed of residual convolutional neural network (residual CNN), BiGRU, and conditional random field (CRF). Finally, word embeddings and the corresponding VA are input into BiGRU and softmax to perform sentence-level anomaly detections. We evaluate the proposed method on a proprietary Chinese PET/CT diagnostic report dataset with an F1-score of 94.35% in the attribute extraction, an F1-score of 96.40% in sentence-level anomaly detection, and an F1-score of 96.77% in case-level anomaly detection. Besides, a publicity English national center for biotechnology information (NCBI) disease corpus dataset is used for externed validation with an F1-score of 95.81% in disease detection. The experimental results demonstrate the advantage of the proposed cascade networks as compared to other related methods.
Inference of finite mixture models and the effect of binning

Abstract Finite mixture models are widely used in the life sciences for data analysis. Yet, the calibration of these models to data is still challenging as the optimization problems are often ill-posed. This holds for censored and uncensored data, and is caused by symmetries and other types of non-identifiabilities. Here, we discuss the problem of parameter estimation and model selection for finite mixture models from a theoretical perspective. We provide a review of the existing literature and illustrate the ill-posedness of the calibration problem for mixtures of uniform distributions and mixtures of normal distributions. Furthermore, we assess the effect of interval censoring on this estimation problem. Interestingly, we find that a proper treatment of censoring can facilitate the estimation of the number of mixture components compared to inference from uncensored data, which is an at first glance surprising result. The aim of the manuscript is to raise awareness of challenges in the calibration of finite mixture models and to provide an overview about available techniques.
A Progressively Clearing Framework Perceivability Location under Hazy Environment

Fog is originate on the field due to water molecules in the air. It is also a complex climate sensation. Recognition in foggy climate is essential due to poor perceptible. Camera based strategies are utilize to recognize the nearness of the haze level. Sometimes in fog climate, people want to take a picture but unfortunately the picture is not clear due to existence of fog. So here, by using real time system and image processing techniques clearance of framework under hazy environment can easily be possible. In this research work, we recommended segmentation procedure, extraction methods, clearance technique so that the foggy picture can be cleared. This whole phenomenon is applied on picture that is clicked by the mobile camera. Here, all work is going to be done in soft real time system which will be shown further in this research paper.