Reviewerly/exHarmony huggingface dataset:
Robust and Efficient Ellipse Fitting Using Tangent Chord Distance

Ellipse fitting is a fundamental problem in computer vision which has been extensively studied during the past decades. However, this problem still remains unresolved due to many practical challenges such as occlusion, background clutter, noise and outlier, and so forth. In this paper, we introduce a novel geometric distance, called Tangent Chord Distance (TCD), to formulate the ellipse fitting problem. Under the least squares framework, TCD is used as the measure to quantify the fitting error, based on which a nonlinear objective function is established and minimized via the Gauss-Newton method. Compared to existing geometric distance based methods, a key merit of our approach is that, the very time-consuming iterative procedure of finding the counterparts of the given points has a simple closed-form solution in our TCD-based formulation, which can thereby significantly reduce the computational load without sacrificing the performance. Experimental results on both synthetic data and public image datasets have demonstrated the superiority of our method over other compared methods in terms of robustness and efficiency.
Exploring Internal Representations of Deep Neural Networks

This paper introducesDespraz, Jérémie a method for the generation of images that activate any target neuronTrojanowski, Krzysztof or group of neuronsGomez, Stéphane of a trained convolutional neural network (CNN). These images are created in such a way that they contain attributes of natural images such as color patterns or textures. The main idea of the method is to pre-train a deep generative network on a dataset of natural images and then use this networkSatizábal, Héctor F. to generate images for the target CNN. The analysis of the generated images allows for a better understanding of the CNN internal representations, the detection of otherwise unseen biases, or the creation of explanations through feature localization and description.
Body ownership illusions in humans and other animals

A sense of body ownership is essential in controlling one's own body in external space, which in turn may give rise to a sense of self-awareness. Body ownership in humans is sometimes extended to certain external objects apart from our own bodies, and such plastic changes in body ownership are thought to be the basis of tool use. Specifically, in a rubber hand illusion task, visuotactile stimuli from synchronous brush strokes to both participants' and rubber hands lead to illusory body ownership of the rubber hand and a shift of perception of the tactile stimuli to the rubber hand as a result of multisensory integration. Recent studies suggest that this kind of body ownership illusion occurs not only in humans but also in animals, including monkeys and mice. In addition, other studies have shown that body ownership is linked to empathy. Studying the body ownership illusion in humans and other animals, including models of human psychiatric disorders, will elucidate neural mechanisms underlying the sense of body ownership, which will consequently contribute to addressing the philosophical issue of self-awareness.
DIGITAL EDUCATION POLICIES IN ITALY: A RECOGNITION ON THE ACTIONS REALIZED

This article covers in its first part the digital educational policies elaborated in Italy as part of the plan known as "Piano Nazionale Scuola Digitale" (PNSD).This plan constitutes in fact the orientation document issued by the "Ministero dell'Istruzione, dell'Università e della Ricerca" (MIUR) for the introduction of a comprehensive strategy of innovation for Italian schools, and a new placement of its educational system in the digital age.In the second part of the article, we report the data (extracted by "Osservatorio Tecnologico" of MIUR) concerning the condition of the digital school in Italy, through a survey of the actions undertaken and the criticalities encountered, deepening the theme of digital skills and media education.
A Normalized Rank Based A* Algorithm for Region Based Path Planning on an Image

With the development of many autonomous systems, the need for efficient and robust path planners are increasing every day. Inspired by the intelligence of the heuristic, a normalized rank-based A* algorithm has been proposed in this paper to find the optimal path between a start and destination point on a classified image. The input image is classified and a normalized rank value based on the priority of traversal on each class is associated with each point on the image. Using the modified A* algorithm, the final optimal path is obtained. The obtained results are compared with the traditional method and results are found to be far better than existing method.
Cultivating Rural English Teachers’ Core Competence through Tripartite Guidance Team

In order to cultivate rural English teachers' core competence, with the help of the tripartite interaction community, the tripartite guidance team including college experts, teaching and research staff and excellent teachers tries to offer some help and instructions for the rural English teachers through four ways of concentrated training, centralized guidance, decentralized guidance and online discussion forum.After two-year-guidance, the rural English teachers have a sense of rural affiliation, understand the nature of English and begin to teach English on the basis of thinking and understanding.Furthermore, they began to cultivate students' positive world view in the process of English teaching.
Planning and Practice of a New Group Working Program for Freshmen of Mechanical Engineering Department

A new group working program was planned and practiced for freshmen of mechanical engineering department with the aim of enhancing and maintaining motivation for specialized education. In this year, a wind car making contest for the introduction of fluid engineering field was carried out. Total of 14 groups was organized for all students of 87 members. Each group was assumed as a virtual company and made an attractive wind car development as a mission. In order to experience an industrial situation, not only the high-speed performance but also the cost of development and the sales presentation were evaluated. The satisfaction level of the lecture was good, and the importance of the lecture closely related to manufacturing was confirmed.
Rotation Face Detection Based on Three-Window Convolutional Neural Networks

Abstract In some family photos or special scene pictures, we can find some rotated faces. Most existing methods are based on increasing the features of rotated faces or changing the directions of pictures to augment the training data. However, these methods have their own limitations and can not detect rotated faces accurately. We propose a method based on three-window convolutional neural networks designed by ourselves. We extract the features of faces and change the feature matrices of faces by clockwise rotation and anticlockwise rotation through three-window convolutional layer in order to increase the face features. We retain parameters of the model after training, and convolutional layer replace fully connected layer. According to the heatmap of the sample, our method can predict face region. We carry out the experiments on FDDB and LFW datasets. The experiments on LFW show that our model achieves AUC of 0.9240, and recall of 0.9367 and the experiments on FDDB show that our model achieves recall of 0.9541.
Detection of Android Applications Homology Based on the Authors’ Programming Style

Abstract As the mobile internet playing an increasingly important role in people’s daily life, the copyright issue of Android applications attaches more and more attentions. Because Android applications are vulnerable to reverse engineering, there are many criminals illegally modify and re-sign the official android for their own interests, which has seriously damaged the user’s rights and disturbed the order of the mobile internet. In this paper, a novel detects method of Android applications similarity based on the authors’ programming style is described. In the proposed method, we firstly extract a series of basic features that can represent the programming style of author. Considering that the features selected manually are highly subjective, we tried ICA, PCA and LDA to optimize the features. Ultimately, an Extreme Learning Machine (ELM) model is trained with sequences of feature vectors. The results of experiments demonstrate that our proposed method has better performance than string comparison and grammar tree analysis when fighting against variable substitution, insert independent statement and data stream confusion.
Fuzzy Logic: Adding Personal Attributes to Estimate Effect of Accident Frequencies

In the field of risk assessment, one of the main aspects is the frequency of accident circumstances. Generally, the risk assessment evaluation is done using generic frequency approach. In case of accident, human factors associated with assigning the impact and cause of the hazard is not considered. Rather more focus is given to generic frequency approach which does not explicitly use the human factors. The main reason behind ignoring this factor primarily lies in the fact that its inclusion makes the entire procedure more complex. However, the proposed fuzzy logic approach helps in achieving the associated complexities in risk computation when we include human factors.
PARADOXICALITY OF SCIENTIFIC KNOWLEDGE AS A RESULT OF SUBJECTIVITY OF INDIVIDUAL PERCEPTION

The article considers the examples of scientific knowledge paradoxicality, which manifests itself in the following ways: perception of science in general, development of scientific knowledge and inconsistency of scientific methods. The authors’ attention is focused on analysing the paradoxicality of certain scientific conceptions and psychological and pedagogical processes. The paper identifies the role of subjectivity in scientific knowledge formation, analyses the influence of individual peculiarities on the process of scientific search and examines the means to reduce subjectivity influence on the development of new spheres of scientific knowledge.
Resource-Aware Allocation and Load-Balancing Algorithms for Cloud Computing

Dynamic cloud computing technique enables resources to be assigned to different clients based on the current demand of each client turning the cloud to a limitless computational platform with limitless storage space which improves the performance of cloud services. To achieve best resource allocation in dynamic hosting frameworks, cloud service providers should provision resources intelligently to all clients. Cloud computing empowers consumers to access online resources using the internet, from anywhere at any time without considering the underlying hardware, technical management, and maintenance problems of the original resources. In this chapter, the authors present a detail study of various resource allocation and other scheduling challenges as well as cloud simulation frameworks tools like CloudSim and ICanCloud.
Load Balancing and Prudential Hypervisor in Cloud Computing

Cloud computing is a service model in internet that provides virtualized resources to its clients. These types of servicing give a lot of benefits to the cloud users where they can pay as per their use. Even though they have benefits, they also face some problems like receiving computing resources, which is guaranteed on time. This time delay may affect the service time and the makespan. Thus, to reduce such problems, it is necessary to schedule the resources and then allocate it to using an optimized hypervisor. Here, the proposed method is used to do the above-mentioned problem. First, the available resources are clustered with respect to their characteristics. Then the resources are scheduled using this method. Finally, with respect to that of the clients request the resources, the resources are allocated. Here, the cost is the fitness of the allocation.
Net Neutrality: Anatomy of a Controversy

There is no neutrality when it comes to net neutrality. Set up in the trenches between digital infrastructure and new media publics, net neutrality has become one of the defining controversies of internet governance, concerning fundamental questions regarding access, digital civil rights and the net’s affordances.
CMCAP

We present CMCAP (context-mapped capabilities), a decentralized mechanism for specifying and enforcing adaptive access control policies for resource-centric security. Policies in CMCAP express runtime constraints defined as containment domains with context-mapped capabilities, and ephemeral sandboxes for dynamically enforcing desired information flow properties while preserving functional correctness for the sandboxed programs. CMCAP is designed to remediate DAC's weakness and address the inflexibility that makes current MAC frameworks impractical to the common user. We use a Linux-based implementation of CMCAP to demonstrate how a program's dynamic profile is used for access control and intrusion prevention.
Cognitive processes in the artistic and educational environment

The article discusses the features of the cognitive processes of students, including the process of cognition in context with the artistic and educational environment that ensures the reflection of the phenomena of the objective world in solving the upcoming life tasks. The impact of an insufficiently objective attitude to the discipline «Visual arts» in school teaching practice, which influences the development of cognitive-visual (visual-cognitive) thinking of students, is shown. It is in the artistic and educational environment that the skills of visual perception of various information are formed using the pictorial language of images, clarity, sign and symbolic systems that promote the conscious use of concepts and inference in any academic discipline. As a result, it is shown that the development of cognitive processes includes the formation of internal motivational activity of children, methods of perception of visual information associated with information exchange, inspired by cognitive metaphor and cognitive load, as a result, children's intellectual development, including cognition, explanation and understanding of educational information.
Load Balancing in Heterogeneous Cluster Cloud Computing

Cloud computing is a modern exemplar to provide services through the internet. The development of cloud computing has eliminated the need of manpower, which is mainly used for the management of resources. During the cloud computing process, the term cloud balancing is a vital one. It deals with distribution of workloads and computing resources. The load balancing allows the company to balance the load according to the demands by the allocation of the resources to multiple servers or networks. The quality of service (QoS) metrics, including cost, response time, performance, throughput, and resource utilization are improved by means of load balancing. In this chapter, the authors study the literature on the load-balancing algorithms in heterogeneous cluster cloud environment with some of its classification. Additionally, they provide a review in each of these categories. Also, they provide discernment into the identification of open issues and guidance for future research work.
International Education Cooperation at the FIT Graduate School of Engineering and the Development of an International Engineering Curriculum through Cooperation

This report introduces an international education and research cooperation carried out between the FIT (Fukuoka Inst. of Tech.) graduate school of engineering and its foreign agreement universities against the backdrop of the regional characteristics of FIT. The results of consideration to a difficult problem with an internationalized education in the graduate school of engineering and the higher education for the next-generation information-based society are shown. In the report, we explain about an acceptance situation of overseas students admitted to the FIT graduate school of engineering from its agreement universities and internationalized curriculum for the engineering graduate students of FIT formed through international education cooperation with agreement universities. Finally, problems with an internationalization of engineering education and a human resource training for globalization are considered.
Emotion Teaching Interfaces Using Emoticons and Emojis for Finger Braille Emotion Teaching System

Some deafblind persons utilize Finger Braille, a communication medium using tactile sense, to communicate each other. Deafblind persons can communicate words, if they are trained in Finger Braille, and also communicate varied emotions by Finger Braille. In this work, the emojis and emoticons were applied to the emotion teaching interfaces in order to develop Finger Braille emotion teaching system. Two novel emojis for joy, anger and sadness were selected, respectively. The best depicted emoticons for joy, anger and sadness were also selected, respectively. Then we designed six emotion teaching interfaces with the emojis and three ones with the emoticons. We conducted the evaluation experiment which evaluate the associable emotion teaching interfaces for joy, anger and sadness, respectively. As a result, the emojis Anger D and Sadness D were associated the best with anger and sadness, respectively; the emoticon Joy 1 was associated the best with joy.
Pixel-Classification-Based Reticulocyte Detection in Blood-Smear Microscopy Images

Abstract Methods for reticulocyte identification and counting based on digital image processing technology are rare. In this paper, we proposed a pixel-based reticulocyte identification method for blood micrographs. This approach not only addresses slowness of manual methods but also largely alleviates the susceptibility of flow-based methods to nucleic acid. The key is to extract pixel-level features. One color feature is extracted from three color spaces, namely RGB, HSI, and LUV. Three texture features called Gabor features, the gray-level co-occurrence matrix, and local contrast pattern are extracted from the Cr channel of the YCbCr color space, forming six features. Subsequently, the recognition effects of each combination of color and texture were compared, and the combination of Gabor texture features and LUV color features was selected. Then, a support vector machine (SVM) classifier was used to classify the pixel-level features, and the RNA-staining area was detected. Based on the location, number, and area of the region, whether the target cell is reticulocyte can be determined. The precision of this method for reticulocytes was 98.4%, recall was 98.0%, and the F1 measure was 0.982, indicating its usefulness for automation equipment.
22‐2: HDR Dynamic Tone Mapping with Enhanced Rendering Control

High dynamic range (HDR) video standards have been evolving from static tone mapping (e.g. HDR10) to dynamic tone mapping. This enables tone mapping customized for each scene or frame. This paper introduces an enhanced tone mapping curve formulation which helps improve rendering precision for shadow details and addresses the display's low‐end luminance characteristics.
A Privacy-Aware Framework for Friend Recommendations in Online Social Networks

Online Social Networks (OSN), such as Facebook, Twitter and Tumblr, have revolutionized the way how people share information and stay connected with family and friends. Along this direction, user's privacy has been a significant concern to almost every user in the social network. In this paper, we propose a privacy-aware framework that allows users to outsource their encrypted profile data to a cloud environment. In order to achieve better security and efficiency, our framework utilizes a hybrid encryption approach that consists of Paillier's encryption scheme and AES. Also, we construct a privacy-aware friend recommendation protocol that recommends new friends to a given set of users without compromising their privacy. The proposed protocol adopts a collaborative analysis between the online social network provider and the Cloud in a privacy-preserving manner. Our proposed protocol utilizes the common neighbors approach and additive homomorphic encryption to achieve a reasonable trade-off between accuracy and security. Furthermore, we compared the performance of our protocol with existing work and show that our solution offers better functionality and security. Finally, we present experimental results evaluating the performance of our protocol and demonstrate the practical applicability of our solution.
Surveillance Video Portrait Recognition Preprocessing Technology for Police Actual Combat

With the continuous development of technology, face recognition technology has played an important role in police work.In order to get a clear face image, image preprocessing technology is needed.This paper mainly proposes illumination compensation technology and reconstruction technology based on symmetry.
Action Keyframe Connection Network for Temporal Action Proposal Generation

Abstract Temporal action detection is an important research topic in computer vision, of which Temporal Action Proposal (TAP) generation is a key step for finding candidate action segments. Our paper provides an action proposal generation network for temporally untrimmed videos in which a new effective and efficient deep architecture named action keyframe connection network for temporal action proposal Generation. Firstly, a two-stream network is adopted to extract frame-level features which inclued appearance feature and optical flow feature. The temporal information helps the subsequent network to determine whether a frame is the beginning or the ending of the action. Secondly, a position discrimination network is designed to infer the probability of each frame being starting frame or ending frame. The network outputs a starting probability sequence and an ending probability sequence which indicates the start of the action and the end of the action respectively. Finally, our network generates a proposal by a specific threshold rule combining the points in the starting probability sequence and the ending probability sequence. We carry out experiments on ActivityNet dataset to compare our proposed method with the state-of-the-art methods. Experiment results show that our method achieves superior performance over other methods.
Carsten Nicolai: Unitape

In the autumn of 2015 I traveled to my home country of Germany to visit the latest work by audiovisual installation artist Carsten Nicolai, which according to the press release “reflects on communi...
Multi-Document Extractive Summarization as a Non-linear Combinatorial Optimization Problem

Multi-document summarization deals with finding the core theme presented in multiple documents. This can be done by selecting the important information from the text in the multiple documents. Extractive summarization selects and extracts such sentences which represent the gist of the documents. In this paper, we have surveyed how research in multi-document summarization has evolved from simple sentence-based techniques like sentence position to complex neural network based supervised learning techniques. In recent years, more and more supervised learning methods are proposed to tackle this problem along with some unsupervised approaches described in LSA (Deerwester et al. J Am Soc Inf Sci 41(6): 391–407, 1990) and TextRank (Mihalcea et al. Textrank: Bringing order into text. In: Proceedings of the 2004 conference on empirical methods in natural language processing, 2004). In this chapter, we have proposed an alternative unsupervised method where the problem of multi-document summarization can be viewed as a non-linear combinatorial optimization problem. We have formulated the problem and discussed possible solution to this problem.