Reviewerly/exHarmony huggingface dataset:
A Comprehensive Review of ML-based Time-Series and Signal Processing Techniques and their Hardware Implementations

Advancements in technology and smart devices demand incubation of advanced learning methodologies to learn or capture the underlying patterns in the data and further utilize and analyze the extracted patterns for different purposes such as anomaly detection, face recognition, and data prediction. There are many techniques for learning and analyzing data patterns, each of which has different complexity and performance. Two important kinds of data analyzed in the current day technology are the time-series signals and the images (computer vision). In this work, we outline diverse machine learning techniques and their applicability for pattern analysis in the aforementioned kind of applications. This survey is motivated by the increasing need to analyze time-series signals and image processing in resource-constrained, embedded devices for a large variety of applications, including autonomous driving. First, we provide a comprehensive review of techniques for analyzing the signals based on numerous stochastic, traditional machine learning, deep neural networks, and other hybrid approaches. For all the prominent families of techniques, we discuss their use in prediction, anomaly detection, and classification. Moreover, we consider their algorithmic complexity and review their hardware implementations. Further, we compare and analyze the involved trade-offs among different techniques (including hardware requirements), and its suitability for analyzing various data sets such as image classification, recognition, and time-series analysis.
DCGAN-Based Data Augmentation for Document Classification

Document classification is a relevant task within every intelligent document processing system. With the advances in deep learning and computer vision techniques, this task has become a painless and straightforward process. However, the need for labelled data is always a hurdle to tackle before constructing, training, and validating classification models. The use of Generative modelling implies using a model to generate new samples that are similar to the training set but individually different from existing records. In this paper, we investigate using deep convolutional adversarial networks (DCGAN) to generate fake document images using existing scanned documents dataset. Moreover, we used a dataset created of generated images alongside original images to train an image classifier using a convolutional neural network (CNN) to classify documents, and we compared the overall accuracy with a model constructed on an original dataset of the same size. The constructed model performed as well as the model trained with authentic data (with an accuracy score of 90% and 91% respectively). This excellent performance can permit using DCGANs to augment existing datasets in case of a lack of labelled data while maintaining similar performance levels.
Evaluating effectiveness of innovative education formats for 21st century skills

It has become increasingly important for youngsters to acquire digital competencies and 21st century skills. Schools across different countries have introduced in their curricula activities and topics on these efforts, however, to varying degrees and with varying effectiveness, depending on a number of reasons. In this paper we report on a study on the effectiveness of the DaVinciLab Youth Hackathon (YH) workshops as a form of intervention in the school system in order to increase digital competencies and 21st skills (4Cs - Communication, Collaboration, Critical Reflection and Creativity) among students and teachers as part of the implementation of basic digital education (Digitale Grundbildung) in Austrian schools. Our methodical research approach included participating observations of YH workshops in and outside the school setting, quantitative surveys and a literature review in the field of learning science. Our evaluation showed that the workshops can be an effective addition and support for schools to close current gaps in the curriculum for basic digital education and to implement parts of the curriculum through project and team-based teaching and learning methods. In order to integrate the format more strongly into the Austrian educational ecosystem, longer-term funding and better integration into training programs for teachers/in-service teachers would be needed. Finally, long-term support through research studies that are carried out in cooperation with different educational institutions could be helpful in future improvements.
Phase retrieval for continuous Gabor frames on locally compact abelian groups

In this paper, we study continuous frames from projective representations of locally compact abelian groups of type $$\widehat{G}\times G$$ . In particular, using the Fourier transform on locally compact abelian groups, we obtain a characterization of maximal spanning vectors. As an application, for G, a compactly generated locally Euclidean locally compact abelian group or a local field with odd residue characteristic, we prove the existence of maximal spanning vectors, hence the phase retrievability, for the associated $$(\widehat{G}\times G)$$ -frames.
A Novel Internet-of-Vehicles Assisted Collaborative Low-visible Pedestrian Detection Approach

For releasing the public concern on road safety, as an essential driving assistant technique for supporting autonomous deriving, considerable research efforts have been paid on developing practical traffic-related target/object detection methods. In recent years, by exploiting the powerful parallel processing capability of GPU and the feature extraction ability of deep convolutional neural network (CNN), the visible light image-based pedestrian detection method has gradually been considered as a potential solution. However, although it has been proven in the existing literature that CNN-based pedestrian detection methods can greatly improve the detection efficiency for lightly occluded pedestrians, the detection of low-visible pedestrians is still an open challenge. Accordingly, in this paper, we propose a novel collaborative pedestrian detection frame based on the Internet-of-Vehicles (IoV) to detect low-visible/hidden pedestrians or even hidden pedestrians. We further evaluate the proposed pedestrian detection framework relying on simulation experiments.
Encoding of Nonbinary Entanglement-Unassisted and Assisted Stabilizer Codes

Quantum coding schemes over qudits using preshared entanglement between the encoder and decoder can provide better error correction capability than without it. In this article, we develop procedures for constructing encoding operators for entanglement-unassisted and entanglement-assisted qudit stabilizer codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> k, with p prime and k ≥ 1 from first principles, generalizing prior works on qubit-based codes and codes that work over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> . We also provide quantum encoding architectures based on the proposed encoding procedures using one and two qudit gates, useful toward realizing coded quantum computing and communication systems using qudits.
Intrinsic Meaning of Shapley Values in Regression

SHAP is a measurement based on Shapley values and has been used widely in machine-learning regressions. In the paper, I describe the intrinsic meaning of SHAP values and I propose that the SHAP was a better measurement for the performance evaluation of a company in the same industry, compared with a raw variable value such as ROE. In my regression analysis of company performance, I found that a linear relationship appeared between the target values and the SHAP values of the predictor variables, even when there was no linear relationship between the target values and the raw predictor values. This visualization of the relationships made us notice the intrinsic meaning and potential of SHAP values. In the SHAP calculation process, through each company's characteristics, how effective a predictor value works to increase the target value within the company is evaluated. The utility of the predictor depends on the individual company's characteristics. Because the individual company's characteristics are used as the characteristic function, the linear relationship could be extracted.
Role of Indian IT Laws in Smart Healthcare Devices in the Intensive Care Unit in India

Smart Devices revolutionized every area of Life, Industries, Home automation, Personal Healthcare, Patient Care. The accuracy of results, energy efficiency, saves money are some of the benefits of smart devices. Smart devices provide plays a significant role in healthcare, Regular monitoring of patients done by smart devices in intensive care units. This paper reviews the Role of Indian IT laws in the Management of Smart Healthcare Devices in the Intensive Care Unit in India. This paper also Proposes Blockchain technology for Safer management and auditing required for Smart healthcare devices data. PEP-PDP Model manages access Management by following various authorization policies. The proposed architecture tested in the Formal verification tool scyther and found no issues regarding this.
Part-Aware Attention Network for Person Re-identification

Multi-level feature aggregation and part feature extraction are widely used to boost the performance of person re-identification (Re-ID). Most multi-level feature aggregation methods treat feature maps on different levels equally and use simple local operations for feature fusion, which neglects the long-distance connection among feature maps. On the other hand, the popular horizon pooling part based feature extraction methods may lead to feature misalignment. In this paper, we propose a novel Part-aware Attention Network (PAN) to connect part feature maps and middle-level features. Given a part feature map and a source feature map, PAN uses part features as queries to perform second-order information propagation from the source feature map. The attention is computed based on the compatibility of the source feature map with the part feature map. Specifically, PAN uses high-level part features of different human body parts to aggregate information from mid-level feature maps. As a part-aware feature aggregation method, PAN operates on all spatial positions of feature maps so that it can discover long-distance relations. Extensive experiments show that PAN achieves leading performance on Re-ID benchmarks Market1501, DukeMTMC, and CUHK03.
A Novel Gaussian in Denoising Medical Images with Different Wavelets for Internet of Things Devices

Over recent years the focus on the comprehensive health-care system in IoT has become increasingly important, which considers in many ways a significant concept to promote health-care. It plays a positive role in increasing the highlight of the issue of medical disadvantage that threatens the medical diagnosis. Medical image constitutes a crucial carrier of the patient's diagnosis information. It nonetheless is exposed to several kinds of noise through transmission, and storage, which leads to impeding the full diagnosis for the patient and a loss of its quality as a medical digital image. Noise is a key factor in decreasing the image quality of different sorts of medical images (X ray, CAT scan, and MRI). Many techniques have been applied for image de-noising. The Discrete wavelet transform which is regarded as the most recent and optimum technique. This paper has been presented four levels of a discrete wavelet transform for the removal of Gaussian noise from several medical images based on diverse wavelet family transforms and median filtering. The proposed method submitted admissible results with regard to removing noise from medical images. The performance evaluation of the proposed algorithm is done by measuring the values PSNR, MSD, and NC.
Location-Based Mobile Augmented Reality Application for Tourism

New Zealand is a popular tourist destination for global tourists, where several cities in New Zealand attract a multitude of tourists annually. This research aims to exploit mobile technologies, including mobile augmented reality (MAR) and location-based service to implement a mobile tourist guide known as InvercARgill, to help promote tourism in Invercargill. The research focuses on the design and implementation of InvercARgill, where the functionality and usability are prioritised. Besides, the client-server architecture of InvercARgill, along with various SDKs and APIs exploited for the implementation are discussed in this paper. Lastly, results of the research reveal that InvercARgill could enhance the tourist's travel experience in Invercargill by distributing tourist information of various attractions through MAR and location-based service on the smartphone, as well as reducing the travel time to various attractions in Invercargill.
Ontologies in Knowledge Organization

Within the knowledge organization systems (KOS) set, the term “ontology” is paradigmatic of the terminological ambiguity in different typologies. Contributing to this situation is the indiscriminate association of the term “ontology”, both as a specific type of KOS and as a process of categorization, due to the interdisciplinary use of the term with different meanings. We present a systematization of the perspectives of different authors of ontologies, as representational artifacts, seeking to contribute to terminological clarification. Focusing the analysis on the intention, semantics and modulation of ontologies, it was possible to notice two broad perspectives regarding ontologies as artifacts that coexist in the knowledge organization systems spectrum. We have ontologies viewed, on the one hand, as an evolution in terms of complexity of traditional conceptual systems, and on the other hand, as a system that organizes ontological rather than epistemological knowledge. The focus of ontological analysis is the item to model and not the intentions that motivate the construction of the system.
Comparative Study between the Genetic Algorithm and the Artificial Bee Colony Technique: RF Circuits Application

Integrated inductors are the most important components of many canals of RF circuits. Tools of the simulation are widely used by the designers; however, these techniques are time-consuming and do not guarantee to reach the optimal geometrical of spiral inductors. Some metaheuristics have been widely used in circuit design, they require less time to converge and provide a better solution. In this work, we concentrate on the use of two metaheuristics to the optimal sizing of RF integrated circular inductors. This article presents a comparative study of convergence, robustness, computing time, and circuit size between the Genetic Algorithm (GE) and the Artificial Bee Colony Technique (ABC). The accuracy of the results is checked against non-commercial software.
Scan path during change-detection visual search

When observing a particular image or object, one's perception depends upon prior expectations, memory, and cognitive abilities. It is hypothesized that cognitive processing in the form of top-down or bottom-up processing could be determined via analysis of the eye fixation scan path. To assess the variations in scan paths, 7 subjects underwent 5 change-detection trials. During each trial, they were presented with a specific set of images via a MATLAB program, in which the original image alternated with a modified image consisting of a single change. An open-source program called GazeRecorder was used to track the subject's eye movements and to record the eye fixations. The scan path was then analyzed through the use of a 4 by 4 grid pattern superimposed on the image to determine the subject's eye fixation distribution pattern in terms of Boxes Viewed and Concentration within a single area. It was determined that higher Concentration was positively correlated with faster Detection Speed (R = 0.84), while higher number of Boxes Viewed was negatively correlated with Detection Speed (R = −0.71). Among the subjects, the more optimal scan paths were found in those with a balance between Concentration and Boxes Viewed, as subjects with a more balanced approach had the greatest Accuracy (p = 0.02). This indicates an optimal scan path involves both top-down and bottom-up processing to more efficiently identify a change. Moreover, the methodology developed in this study could be used in the home or clinic for quantitative assessment of improvement following therapy in patients with neurological deficits.
Highly Efficient Lossless Coding for High Dynamic Range Red, Clear, Clear, Clear Image Sensors

In this paper we present a highly efficient coding procedure, specially designed and dedicated to operate with high dynamic range (HDR) RCCC (red, clear, clear, clear) image sensors used mainly in advanced driver-assistance systems (ADAS) and autonomous driving systems (ADS). The coding procedure can be used for a lossless reduction of data volume under developing and testing of video processing algorithms, e.g., in software in-the-loop (SiL) or hardware in-the-loop (HiL) conditions. Therefore, it was designed to achieve both: the state-of-the-art compression ratios and real-time compression feasibility. In tests we utilized FFV1 lossless codec and proved efficiency of up to 81 fps (frames per second) for compression and 87 fps for decompression performed on a single Intel i7 CPU.
Transdisciplinary System for Student Youth Educational and Research Activities Support

The article proposes an approach to the specialized transdisciplinary system development that allows ensuring access to modern achievements in the field of education, science and technology. Such an approach involves an information-analytical system designing for supporting the educational and research activities of the student youth by means of the software platform "Trans-disciplinary Educational Dialogues of Applications' Ontology Systems" (TEDAOS). The TEDAOS software tools provide the formation of ontological models in the form of knowledge prism, that are proposed to be used to present the results of student youth activities, the results of scientific and technical researches held in fundamental and applied research institutions, as well as curriculum, educational and methodological materials.
FollowMe@LS: Electricity price and source aware resource management in geographically distributed heterogeneous datacenters

With rapid availability of renewable energy sources and growing interest in their use in the datacenter industry presents opportunities for service providers to reduce their energy related costs, as well as, minimize the ecological impact of their infrastructure. However, renewables are largely intermittent and can, negatively affect users’ applications and their performance, therefore, the profit of the service providers. Furthermore, services could be offered from those geographical locations where electricity is relatively cheaper than other locations; which may degrade the applications’ performance and potentially increase users’ costs. To ensure larger providers’ profits and lower users’ costs, certain non-interactive workloads could be either: moved and executed in geographical locations offering the lowest energy prices; or could be queued and delayed to execute later (in day or night time) when renewables, such as solar and wind energies, are at peak. However, these may have negative impacts on the energy consumption, workloads performance, and users’ costs. Therefore, to ensure energy, performance and cost efficiencies, appropriate workload scheduling, placement, migration, and resource management techniques are required to mange the infrastructure resources, workloads, and energy sources. In this paper, we propose a workload placement and three different migration policies that maximize the providers’ revenues, ensure the workload performance, reduce energy consumption, along with reducing ecological impacts and users’ costs. Using real workload traces and electricity prices for several geographical locations and distributed, heterogeneous, datacenters, our experimental evaluation suggest that the proposed approaches could save significant amount of energy (∼15.26%), reduces service monetary costs (∼0.53% - ∼19.66%), improves (∼1.58%) or, at least, maintains the expected level of applications’ performance, and increases providers’ revenue along with environmental sustainability, against the well-known first fit (FF), best fit (BF) heuristic algorithms, and other closest rivals.
Hierarchical Attention Networks for Grid Text Classification

With the development and popularization of power Internet of Things (IoT), IoT produces a large number of distributed data, the electronic text data and the concurrency of multiple services. To support unified management of all data on an existing basis and make the system has the ability to accurate and efficient data analysis, many researchers have devoted efforts to studying an effective algorithms for solving the problem of the electronic text retrieval, information extraction, and classification in the power grid system. However, the accuracy of these algorithms is not satisfactory. In our paper, we will use an effective deep learning model to classify the grid text. i.e., the hierarchical attention networks model. In particular, Attention mechanism is a commonly model with long-term memory mechanism in the field of NLP, which can intuitively evaluate the contribution of each word to the result in a text. Experimental results show that the Hierarchical Attention Networks model obtains the highest accuracy than the existing methods.
A spatiotemporal prediction approach for a 3D thermal field from sensor networks

Thermal fields exist widely in engineering systems and are critical for engineering operation, product quality and system safety in many industries. An accurate prediction of thermal field distribution, that is, acquiring any location of interest in a thermal field at the present and future time, is essential to provide useful information for the surveillance, maintenance, and improvement of a system. However, thermal field prediction using data acquired from sensor networks is challenging due to data sparsity and missing data problems. To address this issue, we propose a field spatiotemporal prediction approach based on transfer learning techniques by studying the dynamics of a 3 D thermal field from multiple homogeneous fields. Our model characterizes the spatiotemporal dynamics of the local thermal field variations by considering the spatiotemporal correlation of the fields and harnessing the information from homogeneous fields to acquire an accurate thermal field distribution in the future. A real case study of thermal fields during grain storage is conducted to validate our proposed approach. Grain thermal field prediction results provide a deep insight of grain quality during storage, which is helpful for the manager of grain storage to make further decisions about grain quality control and maintenance.