Reviewerly/exHarmony huggingface dataset:
Preliminary Study of Deep Learning based Speech Recognition Technique for Nursing Shift Handover Context

Nursing shift handover involves conversations between nurses during duty handover. Handover contents have been recorded manually, and it is desirable to have an automatic speech recognition solution that transcribes audio speeches into texts. Even though there are excellent commercial Speech recognition solutions available, they are mainly designed for general contexts, which may not be most efficient for special contexts. This pilot study is to investigate speech recognition for the special contexts of nursing handover. To be more precise, we are to transcribe audios of 500 Chinese sentences conversed during nursing handover at the Taipei Hospital. A deep learning based network, Deep Speech 2 [1] working in conjunction with Beam Search Decoder with Language Model [2], is adopted for the intended automatic speech transcription. Special adaptation is considered for the audios is in Chinese: the recognition unit in DS2 is Chinese characters instead of letters as in English; the N-gram language model is also on Chinese characters rather than words as in English. Performance of the trained model has been conducted on two experiments: One is to transcribe audios made by people other than the people made the training audios. The other is to transcribe audios whose contents deviated from the recorded 500 sentences. For the former, the character error rate (CER) is 2.33%, as compared to that of 7.94% by Google Speech API [3]. For the latter, when the testing sentences are deviated from the training ones by 21% in word counts, the CER by the trained system is 13%. The results show promising foundation for more complicate sentences patterns.
Intent-based Robotic Path-Replanning: When to Adapt New Paths in Dynamic Environments

For goal-based robot navigation in a dynamic environment, human intent includes expectations about what performance objectives are satisfied by a planned path in terms of objectives to be met. If the planned path drifts from the human's intent as a result of environment changes, the path needs to be replanned. This paper presents a replanning framework with three elements: (a) the integration of fast online path-planning algorithms that generate trajectories conforming to the given intent; (b) a mathematical model that says when replanning must happen; and (c) an evaluation of events that trigger replanning. An interactive graphical user interface enables a human to accept or reject replanned paths when a trigger happens. A study of 50 MTurk participants is used to assess what replanning triggers best enable a human-robot collaboration to persistently satisfy intent?
Forensic originality identification of iPhone’s voice memos

Abstract In this paper, we focus on the meta-data analysis methods and digital forensics techniques to solve the problem of forensic originality identification of audio recordings recorded by the Voice Memos App in the iPhone. The Voice Memos App is the built-in application for voice recording and editing in the iPhone. We first introduce the features of the Voice Memos App. Then, the experimental study is carried out to collect and analyze the original and the non-original audio recordings from different iPhones and iOS versions. The file structure pattern, time related file attribute information pattern, application database data pattern of the original audio recordings are analyzed and concluded in this paper, which can be valuable for forensic identification of original audio recordings.
School of Creative Culture: Strategic Planning of Profile Training

Education of creation is the leading direction of modernization of specialized training at the present stage of development of Russian education. Formation of skills of creative innovative work of students is an indicative basis for the actions of teachers of the profi le school. Creative ways of thinking and action can and should become the main personal value of young people in pre-profi le and profi le training.
Multiplanar reconstruction with incomplete data via enhanced fuzzy radial basis function neural networks

Slice images based on a single slicing direction often contain incomplete data and cannot be used by clinicians for diagnosis or observation. It is thus necessary to reconstruct the slices using multiplanar reconstruction technology. In the case of complete data, it is not difficult to obtain a series of clear images from other slicing directions. In the case of incomplete data, interpolation methods are commonly employed on reconstructed images to compensate for the missing information. However, such results are often not ideal. In this study, we propose a new method based on an enhanced fuzzy radial basis function neural network. First, a series of incomplete transverse section images that have been accurately registered are adopted. Then, we superpose the sequence images to obtain the three-dimensional data volume. Thereafter, we can acquire the coronal or sagittal images by reformatting this data volume. For a reconstructed image, the proposed system was applied to compensate for the lost data. We used 15 sets of proposed neural networks to obtain 15 sets of output data, with the final output data acquired via the inverse distance-weighted algorithm. We trained the system via a gravitational search algorithm. Finally, we repaired all the interpolated data. In this experiment, we used two types of datasets, i.e., images obtained via brain magnetic resonance imaging and abdominal computed tomography. Subjective observations and objective evaluations confirm the superiority and effectiveness of the proposed method compared to other state-of-the-art methods.
Prediction of Crime Rate Using Data Clustering Technique

Anitha, A.One of the most alarming and predominant aspects in our society is crime. Deterrence against crime is vital for the safety of each citizen of a nation. Analysis of crime in a systematic pattern can enable us to follow the trend of crime occurring and help us to prevent adversity. The main focus of this paper is to analyze the patterns of the data collected over a period of time about the crime against women by applying various clustering algorithms such as K-Means Custering, Agglomerative Custering, and Density-Based Spatial Clustering with Noise (DBSCAN). Clustering techniques are used massively along with the analysis, investigation, and uncovering the patterns for occurrence of different data. The comparative analysis of K-Means, Agglomerative Hierarchical Custering (AHC), and Density-Based Spatial Clustering (DBSCAN) were classified by training and test the real-time crime data against women were collected from the West Bengal, one of the famous state in India. The comparative analysis shows that the accuracy has been achieved as 96.1% of accuracy using DBSCAN clustering technique. This can help the police force to predict crimes which can occur in the future and take steps for accurate prevention.
Model Selection of Bayesian Hierarchical Mixture of Experts based on Variational Inference

We consider the model selection of the hierarchical mixture of experts (HME). The HME is a tree-structured probabilistic model for regression and classification. The HME model has high prediction accuracy and high interpretability, however, the estimation of the parameters tends to overfit due to the complexity of the model. In order to mitigate the overfitting problem, in previous studies, several Bayesian estimation methods for the HME parameters have been proposed. In these studies, the true model that generates data is fixed. In general, however, the true model is unknown. Model selection is one of the most important and difficult problems of regression and classification. For the Bayesian HME, the model is determined by the tree structure, the form of the prior distribution and its parameters, however, only the tree structure is considered as a model parameter in previous studies. In this paper, we consider all of these as model parameters and extend the model selection method. Then, we propose a maximum a posteriori (MAP) estimation method of the Bayesian HME model selection. The approximate posterior probability of each model is calculated by the variational lower bound. We show the effectiveness of the proposed method by numerical experiments and discuss the results applied to actual data sets.
A New Metric for Package Cohesion Measurement Based on Complex Network

With software evolution and code expansion, software structure becomes more and more complex. Refactoring can be used to improve the structure design and decrease the complexity of software. In this paper, we propose a cohesive metric that can be used for package refactoring. It considers not only the dependencies of intra-package and inter-package, but also the backward dependencies of inter-package. After theoretical verification and empirical verification on multiple open source softwares, our metric is proved to effectively measure software structure.
A Critical Review on Federated Cloud Consumer Perspective of Maximum Resource Utilization for Optimal Price Using EM Algorithm

Federated clouds have been a solution to some of the challenges of cloud computing like vendor lock-in and performance-related issues in terms of a wide range of resource utilization and pricing for cloud consumers. This paper provides much insight into the problems faced by cloud consumers while utilizing resources for particular price in relation to SLA violation, QoS awareness and cloud brokerage. A brief review of resource utilization with pricing in perspective of cloud consumers is presented, and a layered agent-based model was proposed for simulating federated cloud. To analyze maximum resource utilization on pricing option a MaxResourceUtility, an expected maximization (EM) algorithm was proposed to consider the influence of missing QoS factors while estimating it for resource utility. The results show that 5–10% increase in maximum resource utility and 10–20% decrease in pricing are observed by considering QoS factor response time while utilizing resources.
Perspectives on Algorithmic Performance through the Lens of Interactive Art

Decades of creating and exhibiting interactive installations have illuminated the way algorithms work to modulate, augment, and constrain performances through real-time feedback. They can be deployed as performers themselves to provide unique insight into their inner workings and their implications for human culture, and to provide an alienating frame within which to wonder about what it means to be human.
The South African ICT Security Awareness Framework for Education (SAISAFE)

The literature review and the investigations that have been conducted show that there is a need for a framework that would integrate ICT security awareness into the South African education system. The state of the current usage of ICT in South Africa was investigated as well as the level of ICT security awareness among South African school learners. A gap analysis between the two spheres of ICT security awareness and ICT in education showed that there was indeed a problem. A vigorous research methodology using among others an extensive questionnaire and themes was used to propose a framework that would attempt to provide a solution to the identified problem. After receiving feedback from the research participants, the proposed framework underwent an analysis phase and it was improved and the responses were incorporated to it. A framework called the South African ICT Security Awareness Framework for Education (SAISAFE) has been proposed in this research. This paper presents the process that was undertaken in order to formulate the proposed framework. The complete proposed framework and a brief description of its components are also presented in this paper.
Tangible Interfaces for the Virtual Reconstructions of Museum Exhibits

In this paper we propose an approach to create cyber-physical museum exhibits based on the methods and means of scientific visualization, Internet of things, additive technologies and ontology engineering. Cyber-physical exhibit consists of tightly interconnected virtual and real parts. Scientific visualization is used as methodological and technological basis for presenting the virtual part, which can contain relevant digital content and 3D-reconstructions. Internet of things is a core technology to create tangible interfaces for the corresponding visualization software. Additive technologies allow creating virtual reconstructions and precise copies of museum exhibits. Ontology engineering provides adaptive mechanisms for seamless integration of new cyber-physical exhibits into the existing digital infrastructure of museums. The proposed approach is used in practice to create cyber-physical exhibits in State Darwin Museum (Moscow) and Museum of Permian Antiquities (Perm).
Long Short-Term Memory for Bed Position Classification

This paper describes an approach for bed position classification by using 2 stacked layers of Long Short-Term Memory approach. The data is collected from the sensor panel which consists of 2 types of sensors, i.e. piezoelectric and pressure sensors. The raw data has been classified into 5 classes. It also has to go through the min-max scaling normalization on a fixed range between 0 and 1. The data is assembled to fit a one-second interval of the 30Hz sensor sampling rate. The model has been experimented by changing the number of hidden nodes of the model in 128, 80 and 50 nodes. The result is 91.70% of accuracy which is good enough comparing to the previous works.
Decision making on robot with multi-task using deep reinforcement learning for each task

In recent years, a robot is required to perform multi-task autonomously in human living space. It needs to take actions according to situations. We proposed a method which does decision making on a robot with multi-task according to a situation by using an importance of each task. To respond to changes in importance of task, the robot learned each task independently by using reinforcement learning. An action is selected uniquely using action values and importance of each task in this system. In reinforcement learning, a learning space is constructed by Q-table. When the design of states is detailed, the learning times increase sharply in a case of Q-table. Therefore, a state which a robot recognizes is not detailed in reinforcement learning. There are some researches to adapt to detailed states using deep reinforcement learning. However, it is difficult to make decisions when considering multi-task. In this paper, we propose a method which does decision making on a robot with multi-task using deep reinforcement learning for each task. As an approach, the learning method uses Deep Q-Network for each task, and the decision-making uses importance of each task. This allows the robot to recognize a continuous state and select action according to a situation in multi-task. We carried out an experiment which set three tasks to the robot applied proposal method. From experimental results, we confirmed the usefulness of proposed method.
A Preliminary Investigation of Deep Emotion-based Classification from Natural Language Text

In the Social Web age, the role of the web user has evolved from a simple consumer of web content to the main actor who interacts with other users, shares data and cooperates in social networks, online communities, blogs, wikis, feeds, and chats. His opinions, comments, and suggestions have an amazing influence on the online communities and users that can be inadvertently influenced in decision-making activities such as buying a certain product or trusting the recommendations of the blog, etc. Big corporations, as well as scientific communities, study the user behavior trying to capture human feeling and emotions, aimed at guessing the “client” preferences and then attend his expectations. Emotion extraction using natural language is a complex activity that needs to understand the content and capture the sentiment hidden in the written text. To this end, the work proposes a text analysis based on Deep Learning (DL) to capture the emotions that regulate human feeling in the natural language. The work shows the effectiveness of this approach presenting a comparative analysis of emotion-based text classification by DL neural networks methods, with different datasets and feature settings.
Possible Methodological Options for Development of Pattern Recognition Theory

The article provides a fresh approach to some problems of the theory of pattern recognition. In particular, an extension variant of the distance-based models is proposed. The estimation questions of decision quality of recognition problem without training are also considered. The experiments have been conducted that show that neural networks can be considered a new standard in the solution of recognition problems.
SimG: A Semantic Based Graph Similarity Search Engine

RDF knowledge graphs have received more attention in recent years. Many graph similarity search approaches are proposed to help users get the desired knowledge. However, few of them consider storage architecture and semantic similarity search together. We believe that a complete query engine requires not only an efficient search approach, but also a reliable storage architecture to support. In this paper, we design a semantic-based graph similarity search engine SimG with carefully designed architecture and data structures over an RDF Knowledge graph, which can quickly get the best k answers even given a simplified queries (e.g., Basic Graph Pattern (BGP)) by users. The outstanding features of SimG are as follows: (1) To improve RDF data management efficiency and reduce storage overhead, we first divide the RDF knowledge graph into multiple topic graphs based on the type similarity model. Then we store these topic graphs in a distributed manner(e.g., all topic-graphs are managed and maintained independently). (2) In order to manage topic graphs efficiently, we utilize the adjacency list as the fundamental data structure to store each topic graph. Moreover, we design a skip list based index to accelerate the data accessing. On the top of this topic graph storage, we implement several basic APIs such as access, add, delete and update to support the following semantic query approach. (3) The semantic similarity query module is deployed on the topic graph storage, which returns topk answers by considering the semantic feature based on the APIs implemented above. Finally, extensive experiments on our query algorithm and storage architecture confirm the effectiveness and efficiency of SimG.
Video Testing at the FirstNet Innovation and Test Lab Using a Public Safety Dataset

Video applications are projected as a stressing and significant service of the Nationwide Public Safety Broadband Network. There is an need to empirically experiment and evaluate services across the actual network, rather than purely theoretical or simulation-based assessments. Our contribution to addressing this need, is being one of the first to demonstrate, using open source technology, how to conduct video experiments at the FirstNet Innovation and Test Lab. We transmitted video from the dataset across public safety and commercial broadband networks and under different network conditions. We demonstrated that data preemption and priority were dependent upon a variety of factors.
Person Re-identification Using Group Constraint

The group refers to several pedestrians gathering together with a high motion collectiveness for a sustained period of time. The existing person re-identification (re-id) approaches focus on extracting individual appearance cues, but ignores the correlations of different persons in a group. In this paper, we propose a group-guided re-id method named group retrieval correlation (GRC) to address the above problem, which pays more attention to the correlations of surrounding pedestrians in the same group and reliefs the interference caused by the dependence of appearance cues. Compared with traditional re-id methods which compute naive similarity merely by the appearance characteristics, the GRC based re-id method proposes a novel and optimal person similarity by considering the relationships among groups. Therefore, the proposed approach provides sufficient hints of group relationships, which are supplementary to the appearance features and contributes to constructing a more reliable re-id system. Experimental results demonstrate that the group information promotes person re-id by a large margin on the proposed Group-reID dataset in terms of both hand-crafted descriptors and deep features.
BILL VIOLA WITH GILBERT SIMONDON

This essay undertakes a joint exploration of Gilbert Simondon’s philosophy of individuation and Bill Viola’s video art to propose an ontogenetic model of ecology and a corresponding political praxis, or eco-politics, predicated on the affects of collective individuation. I examine several of Viola’s works that are most explicitly engaged with ecological thinking. These video works enact collective subjectivities related to the emergence of existential territories (The Path), flooding events (The Deluge; The Raft), or simply a confluence of bodies and water devoid of catastrophic implications (The Reflecting Pool). The collective subjectivities enacted in these videos prioritize the ecological event or existential territory over the individual subject, as well as over a social model based on commercial exchanges and interindividual relations. Instead, collective individuation and the discovery of the transindividual serve as catalysts of an affective attunement with the forces of Nature. Under these conditions, the ecological crisis has the potential to become the exceptional event that can serve as the point of inflection for a new immanent relation with Nature.
Assessing password practices of mobile apps

Mobile devices have become an essential part of human life and their increasing use has given rise to a number of innovative applications (e.g. finance manager). As far as we know, no prior work has examined the password practices on mobile apps. Therefore, we focus on the password implementation of mobile apps with the primary objective of assessing their authentication practices. Specifically, we analyze 50 leading mobile apps, which belong to 10 different categories of innovative mobile applications by dividing them into 2 groups based on the sensitivity of the information they hold. Our work can be considered as a follow-up to the study conducted by Bonneau and Preibusch for evaluating password practices of leading websites, which was published at WEIS 2010. We address the following four research questions: (a) How does the user experience vary from app to app? (b) How do the password policies of mobile apps compare with those of websites? (c) Are sensitive group of apps designed with stronger security policies as compared to the non-sensitive group of apps? and (d) What security weaknesses exist in the password implementation of mobile apps? Finally, we propose design recommendations to increase the overall security of password-protected accounts.