Reviewerly/exHarmony huggingface dataset:
Adaptive Video Streaming in Software-Defined Mobile Networks: A Deep Reinforcement Learning Approach

Both mobile edge cloud (MEC) and software-defined networking (SDN) are technologies for next generation mobile networks. In this paper, we simultaneously optimize energy consumption and quality of experience (QoE) in video streaming over software-defined mobile networks (SDMN) with MEC. Specifically, we propose to jointly consider buffer dynamics, video quality adaption, edge caching, video transcoding and transmission. We formulate two optimization problems which can be depicted as a constrained Markov decision process (CMDP) and a Markov decision process (MDP). Then we transform the CMDP problem into regular MDP by deploying Lyapunov technique. We utilize asynchronous advantage actor-critic (A3C) algorithm, one of the deep reinforcement learning (DRL) methods, to solve the corresponding MDP problems. Simulation results are presented to show that the proposed scheme can achieve the goal of energy saving and QoE enhancement with the corresponding constraints satisfied.
Modelling of transducers of nonsymmetrical signals of electrical nets

In the article given materials of modelling electromagnet transducers of nonsymmetrical signals of three phases electrical nets. Determined, that modelling electromagnet transducers of nonsymmetrical signals of three phases electrical nets of power systems require a modern mathematical method, which will submit opposite of calculate optimal construction parameters and quantities. In article electromagnet transducers of nonsymmetrical signals of three phases electrical nets modeled on the base practical and computer oriented matrise methods and analytical equations of algebraical equations systems.
One-Hour-Ahead Solar Power Forecasting Using Artificial Neural Networks in Taiwan

The development plan of the PV power plant in Taiwan with high penetration makes the grid operator has to prepare the strategies to mitigate its intermittency behaviour. The power grid must be more flexible to receive the fluctuated power from PV. There are some options to enhance system flexibility in dealing with renewable energy integration. One of the economical ways is to conduct a solar power forecasting. On the other hand, the use of machine learning is getting more popular in recent days. It has many applications including in prediction. In this paper, the solar power forecasting was conducted using artificial neural networks to predict the next hour of PV power output. The networks were trained using backpropagation (BPNN) and extreme learning machine (ELM). The historical PV power output, weather condition and time were used as the inputs. The extreme learning machine was used due to its advantages in accuracy and computational time over backpropagation neural networks.
Recognition and Prediction of Breast Cancer using Supervised Diagnosis

Breast cancer is the most common and a major death causing disease diagnosed among women worldwide. Early detection of this disease can reduce the death rates. Image processing techniques using machine learning are widely used in medical domain to improve the early detection of cancerous tumors in breast. In this proposed approach, supervised learning techniques are used to extract cancer defining features and classify cancerous images from the normal mammogram images. The supervised system is initially trained by extracting 13 features each from a dataset of 30 images. The extracted features of the image under test are associated with the features extracted from the database images to detect and predict the cancer tumor in the image. Support Vector Machine (SVM) and K-Nearest Neighbours(KNN) is used for classification. Based on the analysis, the system is capable to give a classification accuracy of 95%(SVM) and 97% (KNN). A GUI based interface is also developed for the same. Further, a user-friendly chatbot is developed using Dialog Flow, which interacts with patients to predict cancer based on the symptoms identified by the patient. This chatbot can be used by the patient to detect whether the symptoms are porne to cancer.
On the Number of Participants Needed for Subjective Quality Assessment of 360° Videos

Immersive multimedia applications such as 360° videos and virtual reality (VR) live broadcasting are expected to receive increased popularity in the consumer markets. These applications provide three-dimensional spatial information and require to develop the related advanced wired and wireless ecosystems ranging from head-mounted displays (HMDs) and processors to network connectivity. To develop immersive multimedia systems that provide acceptable user experiences, it is essential to conduct subjective experiments that support assessing subjective video quality as perceived by the users. In this paper, we focus on the question of how many participants are needed in subjective experiments for 360° videos such that a statistically meaningful subjective quality assessment is supported. For this purpose, a subjective experiment for 360° videos has been conducted using the modified absolute category rating with hidden reference (M-ACR-HR) method. A statistical analysis of the opinion scores given by the participants to the visual stimuli shown on an HMD is provided. In particular, mean opinion scores (MOS), differential MOS (DMOS), and the associated margin of error (MoE) and differential MoE are obtained. It is shown that 21 to 26 participants would be sufficient to drive the differential MoE of the DMOS below the given thresholds of $\epsilon $ =1% and $\epsilon $=0.75%, respectively.
Characterizing the architectures and brokering protocols for enabling clouds interconnection

Summary Interconnecting the computing services of different tiers by multiple cloud providers is indispensable to overcome the exponentially increasing demands of enterprises. In view of highly dynamic and unpredictable environment, cloud providers have to face numerous challenges related to service provisioning of diversified applications while handling the critical management operations on resource layers. Proper configuration and uniform development of architectural components and protocols to setup interconnected clouds are inevitable, which play effective role in management. Various approaches have been proposed by research community to interconnect the geographically dispersed clouds to extend the capabilities of standalone provider. Fundamentally, this study encompasses the requirement factors and classification of interconnected cloud management from various aspects. Mainstream resource management elements, architectural components, and brokering protocols with respect to functional impact, which proposed in literature, have been discussed. These protocols and components play pivotal role for aggregating computing services from distinct providers and maintaining the quality of service parameters of service provisioning. Finally, the analysis of various projects is also presented to highlight the functionality of collaborated ecosystem with issues and further directions.
Adaptive Decision-making for Robotic tasks

There is proposed the analysis of decision- making problem in its application for flexible integrated robotic systems. The intelligent systems in form of problem- solving components are considered as key component of automated control systems. There is observed the proposed concept of adaptive decision-maling for flexible robotic systems, based on a set of models and methods of adaptive decision-making, data science methods.
Formal development of an operation monitoring and control system for nuclear reactors using Event‐B method

Nuclear power plants are complex systems with many regulations, requirements, procedures, and operational constraints. The future autonomous safety monitoring and control system of nuclear plants require a constructive method in integrating and aggregating the accumulated knowledge database normally written in human language. For safety critical systems, formal methods such as model checking and theorem proving are used for verifying its conformance of safety requirements. However, formal specifications and implemented application codes are virtually unreadable for domain experts. To enhance this communication bottleneck and to boost productivity, we use semantic web technology to relate formal specifications with ontologies that are human-readable and have formal semantics. We introduce a nuclear reactor ontology of controlled vocabulary to link elements in a physical system and its Event-B model (a formal method for system-level modeling and analysis). Controlled vocabulary provides several benefits. It is still construction of a spoken language and thus, human readable and transparent. Their formal semantics make it easy to integrate them to the counterparts in their specification. We can generate system logs those are both human and machine friendly. Practitioners can write ad hoc query using SPARQL Protocol and RDF Query Language (SPARQL). As a proof of concept of our methodology, we build a system for safety monitoring and control of a nuclear reactor. We prototype our monitoring system in Event-B model and verify that it satisfies safety requirements and constraints. Then we build an OWL 2 ontology for the semantic web from the Event-B model annotating all metrics and events of the system monitors.
Machine Learning: An Overview of Classification Techniques

Nowadays, machine learning plays a vital role in software industry and is of utmost demand in day-to-day life activities. Machine learning has emerged as one of the popular technology for solving real-world applications as autonomous vehicle, speech recognition, image processing, natural language processing and so on. As per Intel/IDC, about 76% of Indian companies are feeling the dearth of skilled talented professional in machine learning. By 2025, it is estimated that the machine learning sector in software industries will grow up to the $16 billion share. In this paper, different machine learning model based on supervised approach is proposed and compared the performance in terms of accuracy on training data and test data, precision, recall and F measure. Python is used for experimental work.
Low-cost Portable Bioradar System for Fall Detection

Lack of effective non-contact ways for fall detection, which may result in development of health-threatening and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. In present work, we propose a concept of a low-cost portable fall detection framework utilizing bioradar technique for smart home environment. The bioradar was designed using a single-chip transceiver K-LC5 (RFbeam), a customized amplifier, a 16-bit precision ADC and a microcontroller. It was tested in experimental conditions with the help of 5 volunteers. We achieved accuracy of 97% and the Cohen's kappa of 89% for classifying fall episodes among other daily activities.
A Comparative study of parallel CPU/GPU implementations of the K-Means Algorithm

The K-Means algorithm is one of the most sophisticated and known algorithms for data-clustering. In this study, we will show the K-Means algorithm as it relates to OpenCL, which is a widespread parallel ecosystem that is reliable for processing and mining datasets that are large in scale. Additionally, we propose a comparative study of the three most efficient K-means algorithm implementations: The Lloyd-Forgy's sequential Method Implementation, a parallel implementation targeting the CPU using OpenMP and finally one of the most complex implementations that uses an OpenCL language. Typically, the measure of performance is done using different data sizes. For large datasets under OpenCL, when comparing the GPU-based parallel algorithm to the CPU-based serial algorithm, the results have shown a good acceleration effect. On the other hand, for small data sets, the OpenMP implementation has turned out to be the best choice.
Leveraging Pretrained Image Classifiers for Language-Based Segmentation

Current semantic segmentation models cannot easily generalize to new object classes unseen during train time: they require additional annotated images and retraining. We propose a novel segmentation model that injects visual priors into semantic segmentation architectures, allowing them to segment out new target labels without retraining. As visual priors, we use the activations of pretrained image classifiers, which provide noisy indications of the spatial location of both the target object and distractor objects in the scene. We leverage language semantics to obtain these activations for a target label unseen by the classifier. Further experiments show that the visual priors obtained via language semantics for both relevant and distracting objects are key to our performance.
Multi-view Discriminant Analysis for Dynamic Hand Gesture Recognition

Although there have been attempts to tackle the problem of hand gesture recognition "in-the-wild", deployment of such methods in practical applications still face major issues such as view point change, clustered background and low resolution of hand regions. In this paper, we investigate these issues based on a frame-work that is intensively designed in terms of both varying features and multi-view analysis. In the framework, we embed both hand-crafted features and learnt features using Convolutional Neural Network (CNN) for gesture representation at single view. We then employ multi-view discriminant analysis (MvDA) based techniques to build a discriminant common space by jointly learning multiple view-specific linear transforms from multiple views. To evaluate the effectiveness of the proposed frame-work, we construct a new multi-view dataset of twelve gestures. These gestures are captured by five cameras uniformly spaced on the half of a circle frontally surrounding the user in the context of human machine interaction. The performance of each designed scheme in the proposed framework is then evaluated. We report accuracy and discuss the results in view of developing practical applications. Experimental results show promising performance for developing a natural and friendly hand-gesture based applications.
Age-invariant Face Recognition Based on Sample Enhancement of Generative Adversarial Networks

Age-invariant face recognition is very challenging. To solve the problem that it is difficult to collect cross-age face data of the same person, this paper proposes to use generative adversarial networks to generate the same person's young and elderly age data for sample enhancement, and use these data to train age-invariant face recognition model. The proposed method is verified on public face-aging datasets: FGNET and CADA-VS. Rank-1 recognition rates reaches 85.04% on FGNET data sets and verification accuracies reaches 96.99% on CADA-VS data sets.
A Robust, Non-blind High Capacity &amp; Secure Digital Watermarking Scheme for Image Secret Information, Authentication and Tampering Localization and Recovery via the Discrete Wavelet Transform

In today's world accessibility and trade of advance digital information over the web is of key significance. Security and verification of digital information, including digital images, is one of the greatest concerns. For this specific reason, in this paper we propose a non-blind watermarking technique for self-recovery and authentication of hidden images against unauthorized tampering by utilizing the Discrete Wavelet Transform (DWT) and the Arnold's Transform. Due to the nature of the Discrete Wavelet Transform, DWT-based watermarking schemes, in general, have the hidden images size limited to up to 1/4 of the carrier image size; that is, for example, for a 256×56-carrier image the size of the hidden images is usually up to 128×128. In this paper, we show how the proposed DWT-based technique (a) has a moderately high capacity capability as it allows hidden images of the same size as that of the carrier image, (b) uses a self-recovery framework for watermarking and information hiding that allows embedding twice the number of correction matrices as in prior related work. This makes the proposed non-blind technique equipped with about twice as much capability of self-recovery from unauthorized tampering as in prior work, (c) allows unauthorized tampering localization and recovery against unauthorized tampering such as image cropping, blurring, pixel tampering, (d) utilizes all three-color channels of a carrier image for watermarking and information hiding, and (e) adds security in the secret hidden image information. Experimental results are presented to show the potential value of the proposed non-blind method for multiple information hiding of same size and the carrier image, tampering detection/localization, authentication, and tampering self-recovery, including cropping, and robustness of the proposed scheme to JPEG compression and encryption, and other unauthorized tampering.
Hi-tech windows in the aircraft cabin: The impact of display latencies on passengers‘ comfort

Hi-tech cabin windows offer exciting opportunities for future aviation industry for cost and design reasons. However, little is known about how displays replacing real windows in an airplane influence passenger comfort. Research here mainly focusses on pilots in flight simulators or not flight-related laboratory virtual environments but not on passengers. One result in these studies is that display latencies may cause symptoms of sickness which can be a crucial factor for comfort. Therefore, the purpose of the present study is to evaluate the impact of latency on passengers’ comfort. 104 participants were recruited as passengers in a full-motion cabin simulator with high-resolution digital displays as hi-tech cabin windows. In three flights the delay between actual motion of the cabin and windows’ outside view was varied (0/0.5/10 s) and four flight segments (take-off, cruise, turbulence and approach) were compared. Inflight and post-flight measures of sickness and estimates of realness were analyzed. Results indicate that only a latency of 10 s had an impact on sickness. Moreover, sickness was mostly characterized by oculomotor symptoms. Inflight measurements revealed that the first flight segments (take-off and cruise) were less affected by long latency than the last flight segments (turbulence and approach). Participants who rated the situation as unreal suffered more from sickness; seating position had no impact on the ratings. The study showed that small latency is a negligible problem for passengers and hi-tech outside view is suitable for future mobility.
Proposing distortion compensation algorithm for determining distance using two cameras

Imaging system is lens in the pinhole camera model. In fact, it is an optical system with complex structure including many different dimensions. Cheap cameras have poor quality lens since they appear distortion while capturing image. Besides, cameras with wide-angle or special-purpose have complex lens that cause distortion. We find that there is a kind of distortion that depends on structure and shape of lens (radial distortion). Therefore, we propose a solution to eliminate it in this paper. The results show that image after de-distortion improves quality that is suitable for application to determine distance to object.
Identifying Diseases and Diagnosis Using Machine Learning

Machine learning is once a computer system has been trained to identify patterns by provided it with information and a set of rules to help recognize that data. We demand the process of knowledge ‘training’ and the output that this method produces is called a ‘model’. The possible of ML in this area is meaningfully from top to bottom meanwhile it delivers us with computational approaches for accruing, altering and informing information in smart medical duplicate understanding systems, and, in specific, knowledge machines that will benefit us to tempt information from instances or information. This Chapter is selected to describe an inconsistency of groupings calculated to describe, rise, and approve multi-disciplinary and multi-institutional machine learning exploration in healthcare Perceptive. While the healthcare part is certainty transformed by means of the ability to uppermost huge dimensions of data about separate patients, the enormous volume of information certainty for human beings to scrutinizes. To identify and diagnosing diseases using Machine learning, brings a method to mechanical discovery of outlines and aim about information, which permits healthcare experts to interchange the adapted care known as precision medicine.
Modeling coherence by ordering paragraphs using pointer networks

Coherence is a distinctive feature in well-written documents. One method to study coherence is to analyze how sentences are ordered in a document. In Multi-document Summarization, sentences from different sources need to be ordered. Cluster-based ordering algorithms aim to study various themes or topics that are present in a set of sentences. After the clusters of sentences have been identified, sentences are ordered within each cluster in isolation. One challenge that remains is to order these clusters or paragraphs to obtain a coherent ordering of information. Inspired by the success of deep neural networks in several NLP tasks, we propose an RNN-based encoder-decoder system to predict order for a given set of loose clusters or paragraphs. Universal Sentence Encoder (USE) is used to encode paragraphs into high dimensional embeddings, which are then fed into an LSTM encoder and consecutively passed to a pointer network, which finally outputs the paragraph order. Since Wikipedia is a source of well- structured articles, it is used to generate multiple datasets. Based on our experimental results, the proposed model satisfactorily outperforms the baseline model across multiple datasets. We observe a two-fold increase in Kendall's tau values for the final paragraph orderings.
Character Decomposition for Japanese-Chinese Character-Level Neural Machine Translation

After years of development, Neural Machine Translation (NMT) has produced richer translation results than ever over various language pairs, becoming a new machine translation model with great potential. For the NMT model, it can only translate words/characters contained in the training data. One problem on NMT is handling of the low-frequency words/characters in the training data. In this paper, we propose a method for removing characters whose frequencies of appearance are less than a given minimum threshold by decomposing such characters into their components and/or pseudo-characters, using the Chinese character decomposition table we made. Experiments of Japanese-to-Chinese and Chinese-to-Japanese NMT with ASPEC-JC (Asian Scientific Paper Excerpt Corpus, Japanese-Chinese) corpus show that the BLEU scores, the training time and the number of parameters are varied with the number of the given minimum thresholds of decomposed characters.