Reviewerly/exHarmony huggingface dataset:
A Distributed Spatial Index on Smart Medical System

Smart medical technologies, combine Internet of Things, cloud computing and artificial intelligence technologies, are redefining the family life. With the advent of the era of big data, traditional medical service systems cannot meet the needs of big data processing in the current medical system because of the limited computing resources, slow operation speed and poorly distributed processing capacity. In this chapter, cloud-based smart medical system applying MapReduce distributed processing technology is proposed to solve these problems. A new distributed k-nearest neighbour (kNN) algorithm that combines the Voronoi-inverted grid (VIG) index and the MapReduce programming framework is developed to improve the efficiency of the data processing. Here, VIG is a spatial index, which uses the grid structure and the inverted index based on Voronoi partitioning technology. The results of extensive experimental evaluations indicate the efficiency and scalability of the proposed approach with real and synthetic data sets.
Efficient Distributed k-clique Mining for Large Networks Using MapReduce

Mining cliques of a network is an important problem that has many applications in different fields like social networks, bioinformatics, and web analysis. In most applications, mining fixed sized cliques, known as k-cliques, is enough. However, mining cliques of a large network is very challenging using current solutions, and it takes a considerable time using a commodity machine. Also, very large networks cannot be efficiently loaded into memory of a single machine. To overcome these limitations, we have proposed a solution named KCminer, which is based on state space search and can be totally fitted into the MapReduce framework. Using the MapReduce framework, it is possible to run KCminer on cloud computing platforms and hence, process very large networks in feasible time. Our experiments which were performed on a cloud computing platform with 100 machines show that KCminer is both fast and scalable. Besides the MapReduce framework, KCminer executes efficiently on parallel shared memory systems. We performed some experiments on a commodity multicore desktop and showed that KCminer can effectively use the power of all cores. The experimental results show that even using a single thread, KCminer is much faster than available serial tools like MACE.
The Future of Education: 2030

Technology affects the future of education in two significant ways. First, it affects the outcomes of education, that is, what kind of qualities are needed for human beings to live a prosperous and fulfilling life in a world affected by technological changes. Second, it affects how education can be organized and delivered to deliver new outcomes. When preparing or planning for the future, two ways of thinking are prominent: forecasting and designing. The first is a noninterventionist approach that makes the forecast based on available evidence and theory of change without taking any actions to affect the course of change. This approach attempts to answer the question “what will education be in the future?” The second way to think about the future education is interventionist. It focuses on what the future should be instead of what it will be. In other words, the futurist here is not an objective forecaster, but a visionary, a designer, an architect, or a creator. He or she is deeply committed to one future over other possible futures. Moreover, he or she is willing to take actions to promote and actualize his or her desired future, in other words, to design the future. This chapter follows the latter approach and presents a designed vision of the future of education. It is not a prediction, but a hope, a vision. It is what education should be and can be with efforts and actions. This chapter presents a new paradigm that is both necessitated and enabled by technology.
Cooperative Path Optimization for Multi-UAVs Based on Dynamic Target Positioning and Tracking

This paper addresses the path planning problem of UAVs in the process of co-locating and tracking moving targets. Based on the particle filter tracking model and considering path planning and target tracking integrated processing, two optimization algorithms, interception and pursuit, are proposed to minimize the flight path with guaranteed positioning accuracy. After simulation experiments, we compare the performance of the two algorithms in path optimization and the impact on positioning accuracy to prove that both algorithms have good performance.
Design of System for Intelligent Search in Industrial Standards

Standards and specifications are widely used in industry to define product and business process requirements, ensure interoperability, safety, and quality, so there is an acute question of how best to apply them in the process of their own production. One of the problems at the moment is the inability to effectively use the existing standards base due to its weak structure. As a solution to this problem, NIST presented the NOVIS system, which provides an interactive visual interface with terminology from standards related to sustainable production. However, this solution has a number of limitations, which are described later in the text, therefore it is necessary to develop our own system of semantic search in industry standards. The aim of this work is system design for intelligent search in industry standards based on ontological description and semantic annotation. It is necessary to consider the existing solution, to determine the requirements for the system, to develop an algorithm for its operation, to compare the tools for management and visualization of ontology to achieve this goal. The practical significance is to define the conceptual framework and build a graphical representation of the system for the further development of an intelligent search system that will allow companies effectively manage, properly understand and comply with industry standards.
An enhanced non local mean method for hole filling in the depth image-based rendering system

Depth image based rendering (DIBR) is the most widely used technology among synthesis algorithms. Hole filling is a challenge in producing desirable synthesized images. In this paper, we propose an enhanced non local mean based hole filling method. Color, gradient and depth information is combined to select the optimal candidate patches. The missing information from holes is then formed by aggregating multiple candidate patches. Furthermore, an efficient invalid pixel classification method based on their chararcteristics is proposed to divide invalid pixels into three types, and use different methods to fill them, and reduce the computational load of the hole filling unit. The results show that the proposed method has a better robustness and performance for hole filling in DIBR systems than other hole filling based on algorithms.
Applying Transfer Learning into Recommendations in the Case of Data Deficient

Recommendation systems recommend things of interest to users by extracting hidden features in reviews. However, it is difficult for the recommendation system to make correct recommendations for those items with very few reviews. To solve this data deficient problem, we propose a Recommendation method based on Transfer Learning (RBTL). It is based on CNN model and it predict ratings by extracting features. We introduce the idea of transfer learning and minimize the distance between the source domain(with some reviews and ratings) and the target domain(with few reviews and ratings) by using the standard distribution distance metric named Maximum Mean Discrepancy (MMD) so that the model trained in the source domain can predict the rating in the target domain.
Representation Learning in Academic Network Based on Research Interest and Meta-path

Network representation learning can transform nodes into a low-dimensional vector representation, which has been widely used in recent years. As a heterogeneous information network, the academic network contains more information than the homogeneous information network, which attracts many scholars to do research. Most of the existing algorithms cannot make full use of the attribute or text information of nodes, which contains more information than structural features. To solve this problem, we propose a network representation learning algorithm based on research interest and meta-path. Firstly, the author’s research interest are extracted from their published papers, and then the random walk based on meta-path is used. The author node is transformed into several research interest nodes by mapping function, and the skip-gram model is used to train and get the vector representation of the nodes. Experiments show that our model outperforms traditional learning models in several heterogeneous network analysis tasks, such as node classification and similarity search. The obtained research interest representation can help solve the cold start problem of authors in the academic network.
TRINet: Tracking and Re-identification Network for Multiple Targets in Egocentric Videos Using LSTMs

We present a recurrent network based novel framework for tracking and re-identifying multiple targets in first-person perspective. Even though LSTMs can act as a sequence classifier, most of the previous works in multi target tracking use their output with some distance metric for data association. In this work, we employ an LSTM as a classifier and train it over the memory cells output vectors corresponding to different targets obtained from another LSTM. This classifier, based on appearance and motion features, discriminates the targets in two consecutive frames as well as re-identify them in a time interval. We integrate this classifier as an additional block in a detection free tracking architecture which enhances the performance in terms of re-identification of targets and also indicates the absence of targets. We propose a dataset of twenty egocentric videos containing multiple targets to validate our approach.
&lt;i&gt;Reminiscences about Blok&lt;/i&gt; [&lt;i&gt;Vospominaniya o Bloke&lt;/i&gt;] by V. Verigina

The article tells the story of a memoir about A. Blok written by V. Verigina (1882–1974) and includes a commented version of the notes for her public report ‘Blok and Komissarzhevskaya’s Theatre’ [‘Blok i Teatr Komissarzhevskoy’], delivered on 4 December 1924. The notes add details to the reminiscences of the actress who knew Blok and friends of L. Blok and N. Volokhova. The notes also reveal the date when Verigina began working on the memoirs. Unlike her published texts, the speaker notes contain a direct reference to the poet’s traits of a ‘great man’ (according to Carlyle), and put more emphasis on Blok’s relationship with N. Volokhova, Komissarzhevskaya’s Theatre, and V. Meierhold in the years 1906–1908. The manuscript was preserved in the archive of the Soviet literary critic Evgenia Fyodorovna Knipovich (1898–1988), who belonged to Blok’s inner circle in 1918–1921, and later supervised research at the Association of Blok Studies (under the auspices of the State Academy of Artistic Studies, GAKhN) in 1923–1929, at the time when Verigina presented her report to the Association.
Scalable and Parameterizable Processor Array Architecture for Similarity Distance Computation

Processor array architecture is a popular approach to improve computation of similarity distance matrices; however, most of the proposed architectures are designed in an ad hoc manner, some have not even considered dimensionality and size of the datasets. We believe a systematic approach is necessary to explore the design space. In this work, we present a technique for designing scalable processor array architecture for the similarity distance matrix computation. Implementation results of the proposed architecture show improved compromise between area and speed. Moreover, it scales better for large and high-dimensional datasets since the architecture is fully parameterized and only has to deal with one data dimension in each time step.
When everyone became a god. The literature of the early aeon

It has become mainstream in the 21st c. for literature to shift its focus from ideology to the way of life and the person, towards a symbiotic coexistence of fiction and memoirs. The article considers several variations of the contemporary ‘literature.doc’: from autopsychological prose, with E. Limonov as its likely founder, to in-between genres originating in Facebook posts and sketches of real life. In her comparison of different invariants of modern documentary and semi-documentary literature, the author finds that, from the artistic viewpoint, autopsychological prose has a structure unlike that of a classical autobiography or classical prose. The new genre aims to establish a direct contact, through perceptions and suggestion; having said that, ‘literature.doc’ is very much devoid of any social or moral agenda. It describes objects within the author’s field of vision, but lacks in generalization and typology and misses out on alternative viewpoints differing from that of the author which enrich the work’s dimensions and dialogue quality. Development prospects of ‘literature.doc’ are concerned with the aesthetic and philosophical realization of the category of ‘the other’.
Threshold cryptosystem based on multiple cryptographic assumptions

A cryptosystem is a cryptographic scheme that allows a sender to encrypt and send a confidential message or document by using public key, while the receiver can decrypt the encrypted message into its original form by using secret key. Most of the existing cryptosystems are constructed based on single hard number theoretical problems. Furthermore, the concept of group-oriented cryptosystem, known as threshold cryptosystem gives an important role in developing a cryptosystem. In this research, a threshold cryptosystem based on two hard number theoretical problems is presented. The strength of this new threshold cryptosystem is the difficulty to solve both factoring and discrete logarithm problems simultaneously. The security analysis of the scheme shows that it is secure against common security attacks.
A calculation method for the development trend of subject keywords

SIGNIFICANCE: The complex network structure information such as citation network and co-authored network provides rich structural information for subject trend research, thus research on deep mining of existing static structure information to obtain keyword trend receives less attention. Both dynam ic method for the extraction of domain-specific common word set and the method for calculating keywords dynamic characteristic (DC value) proposed in this paper can be used as basic methods and parameters for subject trend research. METHOD: Employed mature and widely used TF-IDF algorithm as the word frequency calculation tool, this paper use stable static structural information such as publishing date and journal name to propose a method of calculating the dynamic characteristic of subject keywords based on stable structure such as time, journal name and domain-specific common word set. RESULT: The experimental results show that the keywords’ DC value is positively correlated to the change trend of keywords based on time.
Translating classes to first-order logic

Through an example about linked lists we show how a sequential program with classes can be translated to first-order logic independent of loop invariants.
Improvement Design of Smart Toilet Interactions Based on Peak-End Rule

Technology is recognized as a vital measure to promote the transformation and the upgrading of medical industry when health management has entered the intelligent era. Smart toilets, considered to have great potential for monitoring health information at home, emerge in our daily life. This paper is conducted to ameliorate the key interactions between human and smart toilet and based on peak-end rule, effectively improving user experience. A new design approach is applied to finds the peak and end moments in using process, and finally identifies four key interactions through the method combining user interview and Kano model. This paper details how the application of the new approach improves the interactions between human and smart toilet, contributing to a smoother product operation process which creates a better user experience.
A novel fusion strategy for probabilistic sparse representation classifier guided by support vector machines

In recent object recognition research, the Sparse Representation based Classifier (SRC) and Collaborative Representation based Classification (CRC) have been widely used, achieving promising performances and robustness. However, both of these two algorithms are seldomly fused in classification based on the theory of probability. In this paper, we propose a novel image classification algorithm named Probabilistic Sparse-Collaborative Representation based Classifier (PSCRC), by fusing SRC and CRC. To boost the recognition performance and maintain the robustness of SRC, we introduce the theory of probability to offer different weights for each element in the coefficient vectors of SRC and CRC, respectively. We generate the probabilities of each sample in the training set by using Support Vector Machines (SVMs) which are fused with the coefficients of SRC and CRC. The proposed method is verified on five popular real word image datasets while being compared with other classifiers. The numerical results in the experiments show that the proposed classifier using our fusion strategy outperforms others.
An Algorithm Integrating and Updating Rights Management Information on Public Domain Images

The public domain sites provide various types of public domain works and displays Rights Management Information (RMI) for them. Users may use public domain works in accordance with RMI as specified by the author. However, users who do not understand RMI for a public domain work can be disputed by copyright infringement. To solve this problem, it is necessary to provide users with the accurate and most updated information of RMI on public domain works. To solve these problems, in this paper, we propose an algorithm that extracts and analyzes the information needed to update the latest RMI and correct the wrong or duplicate RMI information in various public domain sites. The proposed algorithm has been verified using 10,000 data for the accuracy of extraction and consistency of updates. The experiment shows that the automatic updating and integration was accomplished for 99.96% of data and only 0.04% of data required to be verified manually.
Unsupervised Effectiveness Estimation Through Intersection of Ranking References

Estimating the effectiveness of retrieval systems in unsupervised scenarios consists in a task of crucial relevance. By exploiting estimations which dot not require supervision, the retrieval results of many applications as rank aggregation and relevance feedback can be improved. In this paper, a novel approach for unsupervised effectiveness estimation is proposed based the intersection of ranking references at top-k positions of ranked lists. An experimental evaluation was conducted considering public datasets and different image features. The linear correlation between the proposed measure and the effectiveness evaluation measures was assessed, achieving high scores. In addition, the proposed measure was also evaluated jointly with rank aggregation methods, by assigning weights to ranked lists according to the effectiveness estimation of each feature.
Elastic Modulus Estimation Using a Scaled State Parameter in the Extended Kalman Filter

The Elastic Modulus of soil is an important property both from a strength and settlement perspective. Back analysis using observation data in an EKF could prove troublesome considering the large difference in magnitude of the covariances of the observation noise and the state vector noise. A simple scaled formulation is developed wherein the Kalman Filtering is done with respect to a new scaled state parameter. The domain is subdivided into blocks and only a simple two-block case is considered. The Elastic Modulus is considered constant inside a block. The EKF using the scaled state parameter is successful in estimating the state of only one of the blocks.
A Pseudo-likelihood Approach for Geo-localization of Events from Crowd-sourced Sensor-Metadata

Events such as live concerts, protest marches, and exhibitions are often video recorded by many people at the same time, typically using smartphone devices. In this work, we address the problem of geo-localizing such events from crowd-generated data. Traditional approaches for solving such a problem using multiple video sequences of the event would require highly complex computer vision (CV) methods, which are computation intensive and are not robust under the environment where visual data are collected through crowd-sourced medium. In the present work, we approach the problem in a probabilistic framework using only the sensor metadata obtained from smartphones. We model the event location and camera locations and orientations (camera parameters) as the hidden states in a Hidden Markov Model. The sensor metadata from GPS and the digital compass from user smartphones are used as the observations associated with the hidden states of the model. We have used a suitable potential function to capture the complex interaction between the hidden states (i.e., event location and camera parameters). The non-Gaussian densities involved in the model, such as the potential function involving hidden states, make the maximum-likelihood estimation intractable. We propose a pseudo-likelihood-based approach to maximize the approximate-likelihood, which provides a tractable solution to the problem. The experimental results on the simulated as well as real data show correct event geo-localization using the proposed method. When compared with several baselines the proposed method shows a superior performance. The overall computation time required is much smaller, since only the sensor metadata are used instead of visual data.
UFANS: U-Shaped Fully-Parallel Acoustic Neural Structure for Statistical Parametric Speech Synthesis

Neural networks with auto-regressive structures, such as Recurrent Neural Network (RNN), have become the most appealing structures for acoustic model in parametric text-to-speech synthesis (TTS). Despite the prominent ability to generate high quality results, high inference cost of RNN model prevents its usage in industry TTS applications and services. In this paper, we propose a fully-parallel convolutional neural network based acoustic model, U-shaped Fully-parallel Acoustic Neural Structure (UFANS). Experiments show our model achieves 20 times speed up in inference and is 300 times faster than real time with comparable speech quality, which is suitable for industrial level TTS services.