Reviewerly/exHarmony huggingface dataset:
Implementation of COBIT 4.1 to Define and Maintain Infrastructure of Information Technology at Regional Public Hospital in West Nusa Tenggara

A hospital is an organization that concern with activities related to services, sales and marketing to improve public health, expecially in West Nusa Tenggara (NTB). NTB is one of the 34 provinces in Indonesia. Based on data attached in 2017 NTB Province health profile of 2017, this province has 28 hospitals. Mataram, the capital city, has two regional hospitals (NTB Provincial Hospital and Mataram City Hospital) obtained plenary predicate. The predicate is used as a foundation from the Director-General of Health Development Efforts of the Republic of Indonesia to be a provincial referral hospital. To support all the activities, equipments are needed. Information Technology has been used in NTB Provincial Hospital such as software, hardware and brainware that related and mutually support the use Hospital Management Information System. This work used COBIT 4.1 framework guides the construction of a classification scheme that defines the level of security and protection of the infrastructure technology. Based on the required problems, the NTB Provincial Hospital requires recommendations as a reference in the procurement, maintenance and security of IT infrastructure. The processes used in the COBIT 4.1 framework are PO2 and AI3. This process answers the process that will be carried out in implementing IT governance to improve infrastructure at the NTB Provincial Hospital by measuring the existing maturity level. The results of the recommendations for IT governance expected to help the NTB Provincial Hospital to improve its services.
Unsupervised Pose Anomaly Detection for Dynamic Robotic Environments

The integration of intelligent robotic systems in dynamic environments is one important challenge in the industrial automation. These environments require the robots to adjust the calculated poses while they are conducting their tasks in the gripping and the post gripping phase. This work introduces a three-stage approach, called the Reduced Unsupervised Reconstruction Anomaly Detection (RURAD). RURAD detects occurring pose anomalies, which allow the robot to react to dynamic events by adjusting the placing pose. The introduced anomaly detector has been trained based on a systematic training parameter tuning approach, in order to identify the optimal training setup. RURAD achieves a state-of-the-art performance on the introduced pose anomaly detection data set.
Reinforcement Learning Compensator Robust to the Time Constants of First Order Delay Elements

Reinforcement learning is a learning paradigm in which a control is learned automatically based on rewards through trial and error based on rewards. When reinforcement learning is employed for robot control, the action that is output by reinforcement learning and the input of the actuator are often the same. A robot's actuator has a time constant of a first-order delay element between input and output. Delays result in the deterioration of the reinforcement learning performance because the environments that contain them lack the Markov property. Although there have been studies of such environments, they are problematic in that performance deteriorates when the time constant of a first-order time-delay element greater than the control cycle. The principal contribution of this paper is to propose a compensator for reinforcement learning that is more effective than conventional methods for environments with a time constant of a first-order time-delay element greater than the control cycle. The purpose of the compensator is to minimize the difference between actions in delayed environments and those not in delayed environments. Experiments reveal that the compensator increases rewards within wider ranges than conventional methods.
A Hybrid Approach for Automatic Breast Cancer Detection

Breast cancer is one of the most commonly diagnosed cancers in women worldwide. The purpose of this paper is to propose an automatic diagnosis of breast cell histopathology (BCH) images to help pathologist in breast cancer detection and diagnosis. In this paper, a hybrid approach for an automatic detection of BCH images is proposed. In preprocessing step, color normalization and histogram stretching are used for image enhancement quality. Local adaptive thresholding, K-means, morphological operations and watershed method using fast radial symmetry transform (FRST) are combined to improve segmentation performance. For the classification, 13 morphological features, 9 intensity features and 25 texture features are extracted and relevant features are selected based on ReliefF algorithm. Finally, the tumor classification is performed using support vector machine (SVM) classifier. Our method was tested on 58 BCH images and we achieve a segmentation and a classification accuracies of 86.77% and 86.20% respectively. These results show that the proposed method gives better diagnosis performance compared to other methods.
Raspberry Pi based Sign to Speech Conversion System for Mute Community

Abstract Motion sign based language becomes important roles for mute community for data transmission. Mute people facing very difficult situation to convey information to normal people. We find the solution for dumb community to make their communication simple and smart using micro processor. We proposed a latest sign language system for dumb people; it helps in emergency situation to convey their required information to normal people easily. This proposed system converts their sign signal to voice. In this proposed method we implemented using Raspberry Pi microprocessor to fast conversion of data. In this technique two major roles that sign based data conversion and voice generation system using micro processor. For the implementation of this enhanced sign conversion system we used flex sensors as sign conversion and RPI module for processing and 3.5mm Audio jack for voice enable. This proposes system will be useful to all mute community, physically unable persons as well as older people to share their information in any situation.
Multi-class EEG Signal Classification for Epileptic Seizure Diagnosis

EEG signal recordings are increasingly replacing the old methods of diagnosis in medical field of many neurological disorders. Our contribution in this article is the study and development of EEG signal classification algorithms for epilepsy diagnosis using one rhythm; for classification, an optimum classifier is proposed with only when used one rhythm so that both execution time and number of features are reduced. We used wavelet packet decomposition (WPD) to extract the five rhythms of brain activity from the public Epilepsy-EEG recordings in order to represent each signal with features vector; then we applied on it the well-known classification methods. A statistical study is done to validate the different algorithms.
Occupation Time for Classical and Quantum Walks

This is a personal tribute to Lance Littlejohn on the occasion of his 70th birthday. It is meant as a present to him for many years of friendship. It is not written in the “Satz-Beweis” style of Edmund Landau or even in the format of a standard mathematics paper. It is rather an invitation to a fairly new, largely unexplored, topic in the hope that Lance will read it some afternoon and enjoy it. If he cares about complete proofs he will have to wait a bit longer; we almost have them but not in time for this volume. We hope that the figures will convince him and other readers that the phenomena displayed here are interesting enough.
HydrogenAR: Interactive Data-Driven Presentation of Dispenser Reliability

When delivering presentations to a co-located audience, we typically use slides with text and 2D graphics to complement the spoken narrative. Though presentations have largely been explored on 2D media, augmented reality (AR) allows presentation designers to add data and augmentations to existing physical infrastructure on display. This coupling could provide a more engaging experience to the audience and support comprehension. With HydrogenAR, we present a novel application that leverages the benefits of data-driven storytelling with those of AR to explain the unique challenges of hydrogen dispenser reliability. Utilizing physical props, situated data, and virtual augmentations and animations, HydrogenAR serves as a unique presentation tool, particularly critical for stakeholders, tour groups, and VIPs. HydrogenAR is a product of multiple collaborative design iterations with a local Hydrogen Fuel research team and is evaluated through interviews with team members and a user study with end-users to evaluate the usability and quality of the interactive AR experience. Through this work, we provide design considerations for AR data-driven presentations and discuss how AR could be used for innovative content delivery beyond traditional slide-based presentations.
Digital Technology and Usability and Ergonomics of Medical Devices

Abstract Trying to identify what digital medical technologies are today is a practically unresolvable task. Over the last few years, we have seen a radical change in these technologies; they have become not only extremely sophisticated and complex but also capable of maintaining evolving relationships with their users.
Trajectory Tracking by the Interacting Multiple Model Algorithm: Genetic Approach to Improve the Performance

In the paper, we consider an application of the genetic approach to improve the performance of the trajectory tracking procedure. Our aim is to reduce the horizontal position estimation errors. The procedure is based on the well known Interacting Multiple Model algorithm and has many parameters to be adjusted, e.g., elements of the transition probability matrix. A genetic tuning algorithm is elaborated and some numerical experiments on simulated aircraft trajectories are made.
Multi-edition approach for Median String Problem

Perturbation-based heuristics for the median string problem performs successive refinements to an incumbent solution. Perturbations can be applied (i) one by one or (ii) multiple at a time. Algorithms in (i) seem to converge to noticeable best quality solutions but are much slower than those in (ii).In this paper, we proposed an algorithm performing several edit operations at a time with a better trade-off between quality and execution time. A profuse experimental evaluation suggests our approach can converge to much better solutions than former algorithms in (ii), even comparable with (i) in some cases. In counterpart, it is much faster than those in (i) but slower than those in (ii).
Performing and Premiering in spite of a Global Crisis: Gerd Kühr’s Corona Meditation and its Usage of Online Platforms

Through the challenges that can arise in connection with a global crisis, in which live performances are prohibited, musicians and music institutions, such as concert houses, have to find solutions in order to give the recipients the opportunity to convey music through a different medium. Gerd Kühr’s Corona Meditation for any number of pianos was especially composed to adjust to the recently changing situation concerning the typical structures of concert life. The concept offers a direct connection between technology – here in the form of using a conference programme and a livestream platform – and music itself, as the composer was aware of difficulties that can arise through using an online medium. By examining the mentioned composition, this paper focuses on the aspect of relocating music to a virtual space, using online platforms as potential temporary alternatives to real-life performances and reflection of the streaming culture in general. Connected to all that matters are problems and difficulties during these types of concerts deriving through the existing technological limits.
Detection of Malicious Video Modifications using Perceptual Video Hashing

The perceptual video hashing (PVH) function produces fixed-length and a compact video fingerprint (or perceptual video hash) based on the perceptual contents of the video. This fingerprint can be used for detection of malicious video modifications or video tamper detection based on its perceptual contents, video authentication and video copyright protection. Here, the video fingerprint is extracted using the three-dimensional (3D) radial projection (RP) technique and the two-dimensional (2D) discrete cosine transform (DCT). The proposed algorithm is inspired from the following algorithms: video hashing based on 2D-RP of key frames and the video hashing based on 3D-RP technique. The performance of all the aforementioned algorithms are assessed using the receiver operating characteristics curves. The proposed algorithm perform better in the context of detecting malicious attacks.
Over-Exposure Correction via Exposure and Scene Information Disentanglement

Over-exposure correction is an important problem of great consequence to social media industries. In this paper, we propose a novel model to tackle this task. Considering that reasonable enhanced results can still vary in terms of exposure, we do not strictly enforce the model to generate identical results with ground-truth images. On the contrary, we train the network to recover the lost scene information according to the existing information of the over-exposure images and generate naturalness-preserved images. Experiments compared with several state-of-the-art methods show the superior performance of the proposed network. Besides, we also verify our hypothesis with ablation studies. Our source code is available at https://github.com/0x437968/overexposure-correction-dise.
A Method to Prove the Existence of a Similarity

Each object will build its own vector space through naming and weighting its characteristics. Based on the vector space, it is possible to measure the similarity between objects where certain parts of an object no longer need to decipher its behavior by simply referring to the measured part of another object. In this case, the similarity measurement tool can streamline the process or reduce the complexity of processing data by referring to other object characteristics. However, there are many similarities in the used measurement. Each measurement has a meaning, and this requires proof of each measurement in common so that the meaning indicates the measurement function either in theory or computationally in simulation.
Deep Learning Based Secure MISO Transmission

This paper investigates the secrecy parameter of the Multiple Input Single Output(MISO) system based on Unsupervised Deep Learning using an autoencoder fitted with a single antenna Eavesdropper. We worked on co-occurring physical layer illustration optimization via encryption and decryption procedures, having an end-to-end scenario of a transmitter having two antennas and a receiver having one antenna. During the work, the road of thinking was extended to the training schemes that performed more accurately than current-day schemes. In the communication scenario, a confusion matrix is given as an input to the Eavesdropper to keep it ignorant concerning the communication. The secrecy is achieved by optimizing the loss function using cross-entropy applied on the Rayleigh fading channel; thus, balance is maintained between a reliable association and data secrecy using spatial diversity and a confusion matrix. Therefore the Eavesdropper with higher noise is not able to decode the symbols correctly.
Evolution of Issues in Distributed Systems: A Systematic Review

The globalization has increased the demand for complex distributed systems that can be accessed at multiple locations by multiple users. However, the issues pertaining to the distributed systems need specific attention in order to minimize the performance failures and increase the Quality of Service. This study can be a useful source of information to understand the evolution of the issues associated with distributed systems, and the current state of issues, which can help the developers in managing the issues during the development and maintenance. This study reviewed various issues related to distributed systems under 12 themes. We found that Security and Privacy, quality of service, resource management, and synchronization issues are a few major subjects that are being focused on current research studies. This study has concluded that the issues relating to distributed systems and issues related to the changing requirements and technologies are evolving from time to time according to each stage of development of distributed systems such as the design phase or the maintenance phase, in addition to its changing according to the new developments in the technology.
Aspect sentiment analysis based on gating convolutional network and attention weighting mechanism

Aspects Sentiment analysis is a fine-grained text on emotional classification. Aiming at the problem that traditional attention mechanism can't effectively combine contextual meaning an spectoward with information, and single level attention can't obtain deep emotional information features, a gated convolutional network model with attention weights is proposed. Firstly, the word layer is modeled by two-way long-term and short-term memory network, and context semantic information is captured in different directions. In the meantime, different weights are assigned to context words with different positions, and then sentences are gated by convolutional network. The layers are modeled to capture the importance of different sentences, and finally the softmax regression is used for classification. The laboratory finding on the Restaurant DS and the Laptop DS in SemEval2014 indicate that the classification accuracy is better than the classification effect of GCN.
Performance Evaluation of Word Representation Techniques using Deep Learning Methods

Word vectors are the real-valued numbers which allow machine learning algorithms to extract the semantic information concern with the words when trained on natural language corpora. The paper explores word representation techniques with evaluation criteria to measure the quality of representation through deep learning models like BERT. The performance of these words vectors can be evaluated using certain measures. Broadly, the two classes of evaluation are intrinsic and extrinsic evaluation. Intrinsic evaluators directly extract syntactic or semantic relationships between the words independent of any language processing task. These evaluators focus on subtasks while extrinsic evaluators consider complete natural language processing task as a measure of performance like chunking, sentiment analysis etc. The experiments have been performed using BOW model, Word2Vec and BERT language model. In this research work word-similarity task is considered for intrinsic evaluation and part-of-speech (POS) tagging task is used as a measure for extrinsic evaluation. The experiments have been performed using python, sklearn machine learning toolkit and keras deep learning framework. BERT language model is used which has recently emerged as the prominent tool for natural language processing. The result obtained from the experiment in this research for word embedding representation techniques are efficient and better compared to other existing traditional models. However, considering large datasets this can be enhanced for better accuracy
A trust and relevance-based Point-Of-Interest recommendations method with inaccessible user location

Point of interest recommender services are widely studied and applied in various areas. In most cases, these services are based on user location data. However, in some cases, the user’s location may not be available. For example, if the user has closed access to this data on a mobile device or in the case of using a web service that does not have accurate location information. Nevertheless, it is necessary to provide the user with the most relevant POIs. In this paper, we propose a modification of the collaborative filtering method based on the trust and relevance of user experience to solve this problem. We compare the quality of ranking using the proposed method with a set of classical interpreted recommender algorithms, such as content algorithms, and matrix decomposition algorithms. Public catering enterprises (bars, cafes, restaurants) in St. Petersburg were used as data example. The feedback data of 90814 customers and 4256 catering places in St. Petersburg were collected from “Tripadvisor” and “Restoclub” services. To estimate the quality of the results, ranking metric [email protected] was used. Experimental results showed that the proposed method is 1.104 times more efficient (from the point of view of the [email protected] metric) than existing approaches.
Current Trends, Challenges, and Future Prospects for Augmented Reality and Virtual Reality

In the recent years, innovations such as Augmented Reality (AR), Virtual Reality (VR), and internet of things have enhanced user experience dramatically. In general, AR is completely different from VR and provides real-time solutions to users by projecting layers of information on real-world environments. Advancements in computer-generated sensory have made the concept of believable virtual environments a reality. With the availability of such technologies, one can investigate “how these technologies can be applied beyond gaming or other useful applications” and “how further improvements can be made to allow for full digital immersion.” This chapter provides a detailed description about AR and VR, followed by interesting real-world examples of AR applications. In addition, this chapter discusses the issues and challenges faced with AR/VR with a motivation of exploring the options for improvement.
Adaptive extreme learning machine ‐ Fuzzy system framework for energy optimization of <scp>IOTs</scp> in wireless sensor networks

In this paper, a new adaptive extreme learning machine (ELM) neural network‐Fuzzy system framework is developed for effective and efficient network energy optimization of internet‐of‐things (IoTs) sensor nodes in wireless sensor networks (WSNs). Each sensor nodes in a WSN communicates with one another in varied methods to transfer the data from IoT cloud to the other virtual modes. Optimization of IoTs in sensor networks tends to handle the network energy and accuracy with the help of extremely complex clustering techniques. Henceforth, this paper has developed a novel adaptive ELM‐Fuzzy system framework to handle the network energy level in an optimum way and thus achieving better data accuracy and network throughput by reducing the overhead of the network to the maximum possible. Numerical simulations carried out proves the consistency and efficacy of the modeled novel adaptive ELM‐ Fuzzy system framework for IoT based sensor network on comparing with traditional techniques from previous literatures.