Reviewerly/exHarmony huggingface dataset:
Application of Internet of Things Based Tracking Device for Vulnerable People

Health Tracking Devices are mostly used in Hospitals and health care sector for monitoring patients. Health is an essential factor for vulnerable people. Various guidelines are given to vulnerable people and data security and privacy are important. Data consisting of administrative, protection and security contributes all of that. Mainly the control devices for health care are installed and operated by qualified personnel. They don’t have excessive rules and restrictions on most of the aspects of electromagnetic resources. One more problem occurs when consumer devices and applications targeting health and fitness factors are again used to monitor vulnerable people who are largely alien to technology interactions. There exists an example, that people who are more easily vulnerable will show less interest in important protective behaviours. The things they lack to perform are getting device updates regularly, modification of old passwords. These people fall to others as victim easily being more vulnerable.
The Quality of Programs Conceived by Object Enhanced Time Petri Nets

Software developers use metrics to estimate the software quality before the development stage to impact early decision making at the requirement stage, development quantification, operation, and software maintenance. These metrics give knowledge of a certain attribute of the software system and thus helps to evaluate it objectively and gives opportunities for software improvement and making plans for modifications. This research aims to find some methods to assist the application developer during the specification and design phases to get higher quality programs. The application development is supposed to be performed using the OETPN based modeling method. The newly proposed metrics are: Structural Complexity (number of arcs and the number of modules), Hierarchical Complexity (if one OETPN module is created by another), Unstructuredness of OETPN Models (which is based on the number of modules, cycles and decision places), Branching Complexity (which is computed according to the reachability graph), Decision Vertex (which is computed based on the decision node of the execution graph), and the Net Complexity is the summation of all the previous complexities. The token complexity consists of the Complexity of guard conditions (using Halsted's method) and the complexity of token classes (using the Chidamber and Kemerer and Lorenz and Kidd metrics suites). All of these metrics are presented with examples. The proposed assessment methods can be used for quality evaluation of implemented applications if the OETPN models are constructed.
Real-Time Suspicious Human Action Recognition from Surveillance Videos for Resource-Constrained Devices

Recent developments in computer vision are seen as a vital advancement in video surveillance. The goal of this research is to build a deep learning model that is capable of analyzing and classifying the video from running CCTV streams to detect criminal actions and identify suspects on the scene. In particular, we focus on the detection of dangerous human behaviors in surveillance videos. This work provides a low cost embedded solution that can be integrated with the existing CCTV cameras. This integration can reduce the cost of transmitting the data to any centralized server, which may have various privacy implications and takes much inference time. We also benchmark our models performance with the existing real-world dataset in terms of accuracy and resource constraints. Using the concept of Multiple Instance Learning on the histogram of the optical flow of the videos combined with the pose estimation of the persons on scene, we provide a lightweight model which has 13 times lesser inference time than the existing very deep models. Focusing on one important thing, this research will expand to which state-of-the-art deep neural networks will “see” violence in photographs and videos, and recognize criminal behavior using characteristics such as gestures, gait, and unethical behavior. This helps enforcement agencies to unravel crime cases faster and also to scale back crimes by identifying the suspects in the surveillance videos.
Orthographic features for emotion classification in Chinese in informal short texts

Informal short texts on the web are rich in emotions as they often reflect unfiltered immediate reactions to breaking news events. The emotion density, however, stands in contrast to its poverty of linguistic contexts and features for emotion classification. This paper tackles that challenge by proposing orthographic features based on orthographic code mixing and code-switching for both non-ML and ML approaches. Our results show that orthographic features routinely outperform grammatical features for emotion classification for short texts in all approaches as expected. Orthographic features were also shown to make more significant contributions, especially in terms of precision and in formal texts when state of the art deep learning algorithms are applied. This result confirms the effectiveness of the orthographic change feature to the task of emotion classification. These results are argued to be applicable to all languages because of the common code-shifting in languages with non-Latin orthographies, and the use of non-letter symbols in all languages.
A Hybrid Model for Container-code Detection

In this paper, we propose a container code detection algorithm that combines PSENet and CRNN in a manner that ensures that the obtained images are greatly affected by the other containers as well as text information in the picture. The proposed algorithm divided into three parts: object detection module, text detection module and text recognition module. At the initial step, the object detection module is used to calculate the position of those areas in container code that needs to be predicted, we then use the text detection module based on pixel segmentation, and finally obtain the container code through the end-to-end text recognition module. This algorithm is able to detect different container code for a vertical and horizontal scaling. We show that, in a complex multi-container scenario, the detection performance is good, and highly stable when trained with multi-angle container, achieving relative improvement of up to 95%.
Image Ranking Relevancy Based on Semantic Web Using Deep Learning Technique

Computer vision and deep learning have significant leverage on the retrieval of image ranking. The impressive advancements of deep learning techniques for computer vision and other applications conducted an excellent performance for semantically image ranking. The great challenge in image ranking task concentrates on extracting the deepest features of the image. This paper investigates a highly scalable and computationally efficient of deep relevance image ranking system for large scale images. The superior deep network model called RetinaNet is utilized as a feature extractor to learn deep semantic feature embedding of the imaging data. Besides, The effective transfer learning scheme is proposed to transfer the RetinaNet learning to deep relevance image ranking system. The experimental results manifest that our deep learning procedure enhancement the retrieval results efficiently and accurately and focuses on inhibit the learning time of a deep, relevant ranking task. As compared with other state-of-the-art object detectors, the RetinaNet detector accomplished more than a 97% mean average precision (MAP). These superior results pretend the effective impact of our proposed procedure learning that drives the more efficient and relevant result of the deep ranking task.
Why mess with users’ autonomy? On ideals and dualism in design

The MRL Studio in Nottingham has a longstanding commitment to designing for the uncomfortable, the thrilling, the unexpected, the unusual and the artistic. In this article, they revisit three of th...
Educating for Sustainable Peace: Neoliberalism and the Pedagogical Potential of Social Media in Creating Conditions for Civic Engagement and Peace

This chapter investigates the capacity of traditional and social media educational spaces, materials, and pedagogy to promote civic engagement and peace. It argues that the “market-based orientation of education” narrows possibilities for civic engagement and peacebuilding, instead promoting different types of violence. The chapter explores the ways in which social media illuminate the failures of traditional educational models to promote civic engagement and peace. However, the author drawing on her research on pre-service teachers’ use of social media to promote civic engagement, also casts doubt on the dominant understandings of the educational and emancipatory potential of Web 2.0. The chapter points to significant gaps in current research on social media, particularly the dearth of analysis of political agenda and mediated nature of social media.
Entropy Repulsion for Semi-supervised Learning Against Class Mismatch

A series of semi-supervised learning (SSL) algorithms have been proposed to alleviate the need for labeled data by leveraging large amounts of unlabeled data. Those algorithms have achieved good performance on standard benchmark datasets, however, their performance can degrade drastically when there exists a class mismatch between the labeled and unlabeled data, which is common in practice. In this work, we propose a new technique, entropy repulsion for mismatch (ERCM), to improve SSL against a class mismatch situation. Specifically, we design an entropy repulsion loss and a batch annealing and reloading mechanism, which work together to prevent potentially mismatched unlabeled data from participating in the early training stages as well as facilitate the minimization of the unsupervised loss term of traditional SSL algorithms. ERCM can be adopted to enhance existing SSL algorithms with minor extra computation cost and no change to their network structures. Our extensive experiments demonstrate that ERCM can significantly improve the performance of state-of-the-art SSL algorithms, namely Mean Teacher, Virtual Adversarial Training (VAT) and Mixmatch in various class-mismatch cases.
A Long Short Term Memory with Peephole Connections and Generative Adversarial Network Based Collaborative Methodology to Identify Outliers in ECG Dataset

Big Data analytics has become an upward field, and it plays a pivotal role in Healthcare and research practices. Big data analytics in healthcare cover vast numbers of dynamic heterogeneous data integration and analysis. Medical records of patients include several data including medical conditions, medications and test findings. One of the major challenges of analytics and prediction in healthcare is data preprocessing. In data preprocessing the outlier identification and correction is the important challenge. Outliers are exciting values that deviates from other values of the attribute; they may simply experimental errors or novelty. Outlier identification is the method of identifying data objects with somewhat different behaviors than expectations. Detecting outliers in time series data is different from normal data. Time series data are the data that are in a series of certain time periods. This kind of data are identified and cleared to bring the quality dataset. In this proposed work a hybrid outlier detection algorithm extended LSTM-GAN is helped to recognize the outliers in time series data. The outcome of the proposed extended algorithm attained better enactment in the time series analysis on ECG dataset processing compared with traditional methodologies.
Movie Genre Prediction Based on Plot Synopsis

Movies have now become one of the main sources of entertainment for people. The extensive use of Internet has increased the creation and sharing of movie related data online. Movie plot summaries generally tell about the movie genres and many people read them before deciding to watch a movie. An automatic system can be applied to predict genres based on summaries. The objective dataset chosen by us consists of 14828 movies taken from Kaggle. We use different approaches such as TFIDF, Char gram, Skip gram etc to get better accuracy scores in predicting movie genre tags.
Sensor-Actuator Software Component Stack for Industrial Internet of Things Applications

Recent developments in software, hardware, and communication technologies resulted in a sudden increase of things connected to the Internet. Not only did the number of devices connected to the Internet increase exponentially, so did their kind - devices previously unthought-of are now participants of the Internet of Things/Everything ecosystem. Subsequently, the reusability of existing IoT solutions or solution components is crucial to face the high demand for new types of things to be connected to the network. A well-defined, architectural approach that implements reusability is crucial. In this paper, IoT Systems are viewed as a whole and abstracted to an immense spread-out device that is sensing everything and acting to everything in the environment. Consequently, a concept which views the Internet of Things as an Internet of Sensor-Actuator is proposed. This concept assumes that interaction with the environment is provided by well-defined Sensor-Actuator pattern components that provide services to the applications. By combining practices from the automotive layered architecture approaches, as well as the Sensor-Actuator Pattern defined for the system scaling and reuse purposes, the similarity of the signal conditioning for acquiring and acting is pointed out. Furthermore, the diagnosis and protection procedures are discussed for both sensors and actuators, and their auxiliary functions are considered. The proposed architecture is analyzed on an industrial robotic arm application.
A Chaotic-Jaya Optimized OSELM Model for Cancer Classification

In the rapidly defiled environment, cancer has emerged out as the most threatening disease to human species. So, the diagnosis and prognosis of cancer should be easier and affordable. To help the medical practitioner, machine learning researchers aim to design a robust classification model which can able to classify cancer data with high accuracy and less computational complexity. Here, Chaotic-Jaya (CJaya) algorithm optimized online sequential extreme learning machine (CJaya-OSELM) is proposed for classifying cancer data. To estimate the efficacy of the proposed model, two standard cancer datasets, breast cancer and lung cancer, are used. The result of this model is compared with CJaya optimized multilayer perceptron (CJaya-MLP), CJaya optimized ELM (CJaya-ELM), and OSELM. Sensitivity, Gmean, Fscore, specificity, precision, and accuracy are taken as performance evaluation measure for these models. The experimental outcome reveals that CJaya-OSELM is significantly better than other models.
Delegate and Verify the Update Keys of Revocable Identity-Based Encryption

Revocable identity-based encryption (RIBE) is an extension of identity-based encryption (IBE) and it supports efficient revocation of private keys. In the past, many efficient RIBE schemes have been proposed, but research on efficiently delegating the generation of update keys to a cloud server is somewhat insufficient. In this paper, we newly introduce the concept of delegated RIBE (DRIBE) that can delegate the generation of update keys to the semi-trusted cloud server and define the security models of DRIBE. Next, we propose a DRIBE scheme by generically combining a hierarchical IBE (HIBE) scheme, an identity-based broadcast encryption (IBBE) scheme, and a collision-resistant hash function. In addition, we propose a DRIBE-INC scheme that generates an occasional base update key and a periodic incremental update key to reduce the size of update keys in our DRIBE scheme.
How to Encode Dynamic Gaussian Bayesian Networks as Gaussian Processes?

One dimensional versions of the Markov chain and the hidden Markov model have been generalized as Gaussian processes. Currently these approaches support only a single dimension which is limiting their usability. In this paper we encode the more general dynamic Gaussian Bayesian network as a Gaussian process and thus allow arbitrary number of dimensions and arbitrary connections between time steps. Our developed Gaussian process based formalism has the advantage of supporting a direct inference from any time point to the other without propagation of evidence throughout the whole network, flexibility to combine the covariance function with others if needed and keeping all properties of the dynamic Gaussian Bayesian network.
Virtual reality learning environments for vocational education: a comparative study with conventional instructional media on two-stroke engine

Abstract Virtual reality technology has been adopted as a modern learning media in vocational education. Nowadays, the practical learning needs complete equipment and tools to achieve the goal study. However, the conventional media requirement is a challenge for academician for learning process, specifically in automotive and machine areas. Virtual reality provides a solution for this problem, but still needs proper analysis on the impact of usability between conventional and VR media to which each learning objective is achieved. The case study used in this research is a two-stroke engine, which is currently a rare item in the real world. We replicated this two-stroke engine in the developed VR system using Unity game engine and Oculus quest. A comparison on the usability effectiveness on conventional and virtual reality methods was conducted using cognitive walkthrough, which follows the four steps of learning objective training. The result shows that three out of four learning objectives are similar with conventional objective training. One of the process on preparation for disassembly machine has slight difference with conventional training; faulty engine due from environment elements (liquid, rusty iron) was not included.
Preliminary Performance Analysis of Distributed DNN Training with Relaxed Synchronization

Scalability of distributed DNN training can be limited by slowdown of specific processes due to unexpected hardware failures. We propose a dynamic process exclusion technique so that training throughput is maximized. Our evaluation using 32 processes with ResNet-50 shows that our proposed technique reduces slowdown by 12.5% to 50% without accuracy loss through excluding the slow processes.
A Study on the Assesment of Leukocyte with Hybrid Imaging Technique

In recent years, a considerable number of bio-image and assisted disease detection systems are proposed and implemented in medical clinics to identify the diseases. The proposed work aims to implement an imaging approach to segment the Leukocyte (White-Blood-Cell) section from the thin blood smear image. The Leukocyte segmentation and evaluation is an initial process and it is related with the blood-sample analysis. The proposed technique implements the integration of RGB image threshold and segmentation technique to extract the White-Blood-Cell (WBC) section with better accuracy. In this work, the Bat-Algorithm (BA) and Shannon's entropy assisted thresolding is initially executed to enhance the WBC by eliminating the other sections and later the Watershed-Algorithm (WA) is executed to extract the WBC. Finally, a relative assessment with the extracted WBC and the ground-truth is implemented and based on the accuracy; the performance of the proposed technique is authenticated.
Pedagogical essence and content of inclusive educational process in children’s institutions of sanatorium type

The article reveals the essence and content of the inclusive educational process of schoolchildren with disabilities during their rehabilitation treatment in children’s sanatorium-type institutions. The article also considers the specifics of psychological and pedagogical effects and interactions in the process of supporting education for this category of students.
Analysis of gaze points when looking at paintings and saliency map to improve the accuracy of ROI (region of interest)

As a preliminary step to improve the accuracy of ROI (Region of Interest) based on human visual characteristics, we measured and analyzed the eye movements when looking at paintings. We assumed that there was regularity in viewers' eye movement when looking at a world-renowned painting. This time, we introduce our experimental method, and compare for gaze points distributions and saliency map.
Sumcheck-Based Delegation of Quantum Computing to Rational Server

Recently, a new model of delegated quantum computing has been proposed, namely, rational delegated quantum computing. In this model, after a client delegates quantum computing to a server, the client pays a reward to the server. In this paper, we propose novel one-round rational delegated quantum computing protocols. The construction of the previous rational protocols depends on gate sets, while our sumcheck technique can be easily realized with any local gate set. We also show that a constant reward gap can be achieved if two non-communicating but entangled rational servers are allowed. Furthermore, we show, under a certain condition, the equivalence between rational and ordinary delegated quantum computing protocols.
On the relationship between middles and passives

Abstract Previous studies using diachronic data from the Sejong Historical Corpus have traced the semantic extension of voice marker - eci from middle to passive uses (e.g. Ahn &amp; Yap 2017 ). In this study, based on data from the Sejong Contemporary Spoken Corpus, we further examine the relationship between middle and passive uses of - eci constructions, with special attention to the neutralization of adversative readings that give rise to generalized (in addition to adversative) middle and passive - eci constructions. Our analysis reveals that judgments about adversative readings in Contemporary Korean are not emergent solely from the semantics of the verb or adjective preceding - eci but additionally are emergent and grounded in the interaction between discourse participants. The distributional characteristics of - eci also show a strong interaction between voice and tense-aspect-mood (TAM). There is also some interaction effects from register and text type/genre, particularly in the usage frequency distribution of spontaneous and passive - eci constructions. In addition, contrary to the traditional notion that - eci is essentially a passive marker, in real usage, - eci is still far more frequently used as a middle marker than a passive marker.
Data-Based Technique for Extracting Knowledge from Data Generated in Experiments

Fuzzy sets are used in different fields and determination of the membership functions is one of the most important issues in the design of fuzzy systems. The paper presents an approach to that problem to provide solutions in specific cases. In context, a technique for extracting knowledge from measurements data sets was developed that allows to retrieve human expertise and the construction of algorithms for decision-making systems. Initially, the method was developed to be used in data-based fuzzy modeling for the micro-wire casting plant control.
Lateral Movement Method of Inverted Two-wheeled Vehicles for Narrow Roads Using Pivot Turn

In recent years, the declining birthrate and an aging population have become a serious problem in Japan. For this reason, the number of elderly people who have difficulty going outside is expected to increase. In addition, elderly people have difficulty dealing with modes of transportation such as cars and bicycles. To solve these problems, a small and easy-to-handle mobile robot, such as an inverted two-wheel vehicle is expected to be a useful device. However, the vehicle robot has difficulty moving along a path with a narrower width than that of the vehicle body. Therefore, in this paper, a trajectory planning method is proposed that makes it possible for vehicles to move laterally on narrow roads using a pivot turns in places where it is otherwise difficult to turn. The validity of the proposed method was evaluated experimentally.