Reviewerly/exHarmony huggingface dataset:
The role of input selection and climate pre-classification on the performance of neural networks irradiance models

Neural Network (NN) models are widely accepted in the derivation of solar radiation models due to their ability to adapt to various geographic and environmental conditions. Despite this, it is unclear what training variables are required to enhance the precision of the NN model or which of them could be considered redundant; therefore, this paper intends to clarify this issue by investigating if the Köppen climate classification could be used to substitute climatic measurements. To this end, We analyzed a variety of NN architectures using 20 years of data from 1629 weather stations belonging to three different climate types (Climate A, B, and C). We found that Köppen climate sub-classification had a limited effect on the models’ performance when the information of all data types was processed together, resulting in barely noticeable improvements from 1.2% to 2.5%. However, if data were pre-classified according to climate type, the climate sub-classification input induced significant differences. Improvements up to 14% in the precision of the models were found for Climates B and A; moreover, temperature and relative humidity daily measurements could be replaced by Köppen climate information. Cross-validation analysis, using the same amount of data for all climate types, allowed us to confirm our findings for Climates A and B and revealed that data pre-classification according to climate type for Climate C, systematically increased errors from 10% to 24%, so replacing actual climatological measurements was not possible for this climate type. Revealing such patterns would facilitate the derivation of models for scenarios of limited information on temperature and relative humidity in some locations and reveals the usefulness of soft computing to go beyond understanding climate complexity.
Multi-attribute Open Set Recognition

Open Set Recognition (OSR) extends image classification to an open-world setting, by simultaneously classifying known classes and identifying unknown ones. While conventional OSR approaches can detect Out-of-Distribution (OOD) samples, they cannot provide explanations indicating which underlying visual attribute(s) (e.g., shape, color or background) cause a specific sample to be unknown. In this work, we introduce a novel problem setup that generalizes conventional OSR to a multi-attribute setting, where multiple visual attributes are simultaneously recognized. Here, OOD samples can be not only identified but also categorized by their unknown attribute(s). We propose simple extensions of common OSR baselines to handle this novel scenario. We show that these baselines are vulnerable to shortcuts when spurious correlations exist in the training dataset. This leads to poor OOD performance which, according to our experiments, is mainly due to unintended cross-attribute correlations of the predicted confidence scores. We provide an empirical evidence showing that this behavior is consistent across different baselines on both synthetic and real world datasets.
An Introductory Note on the Pros and Cons of Using Artificial Intelligence for Cybersecurity

Artificial intelligence is a kind of digital representation of human intellect. Its operations are similar to those of humans in that it can make the computing machines learn, de-learn, and re-learn iteratively over and over again. The roadmap to the application of AI to Cybersecurity has been encountering a lot of issues and challenges. Digital security issues are critical in the creation of methodologies and support measures in any organization to properly prepare for countermeasures to cybersecurity threats and attacks. The role of AI in these countermeasures without a doubt is of paramount importance at present or in future. Some of the worldwide frameworks and standards being used in the context of cybersecurity across the globe may have been advocating for using AI for security. But a lot more effort is needed before the start of the process of integrating AI into these frameworks and standards and bringing the digital transformation thus caused to fruition. This paper presents an account of some of the currently used frameworks and standards for cybersecurity by various organizations around the world followed by a snapshot of using AI in cybersecurity. It also presents a discussion on the pros and cons of integrating AI into cybersecurity countermeasures.
Evaluating E-Learning Process on Virtual Classroom Systems Using an ISO-Based Model

E-learning is the process of acquiring knowledge through electronic devices such as computers and smart devices connected to the internet, facilitating users to learn anytime and anywhere. E-learning through modern virtual classroom platforms combines a variety of technological tools aiming at successful and effective distance learning, enhancing the sense of social presence and team spirit among students, while allowing teachers to communicate with students in ways that traditional in-person learning does not offer. This article addresses the issue of evaluating e-learning systems that utilize the virtual classroom model, highlighting the features and requirements they must meet. As such, an ISO-based standard for the evaluation of e-learning platforms in a virtual classroom is proposed, aiming at the satisfaction of students and teachers. Finally, four modern software products that are widely used for the e-learning process in a virtual classroom are assessed, based on the proposed evaluation standard.
Discrete subspace structure constrained human motion capture data recovery

Recently, low-rank matrix completion (LRMC) has shown a promising performance in human motion capture (mocap) data recovery, which assumes that the input data is low-rank. However, for most mocap data comprising multiple different activities, only using LRMC to restore the data may result in erroneous results, since the low-rank assumption is not necessarily satisfied. The current method solves this problem in a two-stage manner by combining the LRMC and subspace clustering. But it ignores the fact that the processes of recovering and clustering depend on each other, and so is sub-optimal. In this paper, we propose a novel discrete subspace structure (DSS) constrained approach to recover human mocap data, which jointly optimizing the tasks of subspace clustering and the LRMC. The proposed DSS algorithm learns different subspaces’ indicators such that the mocap recovery problem is divided into several LRMC subproblems, where each matrix of the points drawn from a single subspace is low-rank. As a byproduct, we can obtain the temporal clustering results of mocap data. To better approximate the low-rank property, we utilize the tth power of Schatten p-norm to approximate the rank instead of the nuclear norm. Moreover, we add two regularization terms to take care of the noise effect and temporal stability of mocap data. The obtained model comes down to a non-convex optimization problem, which we solve tactically and efficiently by employing the alternating direction method of multipliers (ADMM). Experiments on the CMU dataset and the HDM05 dataset validate the effectiveness of the proposed algorithm in both mocap data recovery and temporal subspace clustering.
Making sense of digital health data: Negotiating epistemological tensions of everyday eating in Singapore

Engagements with personal digital data are growing in recent decades, as many digital technologies offer any interested user the opportunity to track their habits and practices. Yet, far from being objective and neutral conduits of knowledge, digital data is often politicised, invested with particular values, norms, and assumptions about human experiences. Digital health technologies, in particular, are often found to be coded through biomedical understandings of health and the individualism of Western ethics. Such politics of data knowledge inevitably has implications on the ways people in different socio-cultural contexts make sense of their health, bodies, and correspondingly, experience spaces of health through engagements with digital data. Drawing on diaries and diary-interviews with users of digital food-tracking devices in Singapore, this paper develops the idea of 'epistemological tensions' to theorise the socio-cultural differences in digital practices beyond the dominant focus on Anglo-American experiences. The findings suggest that people's engagements with digital health technologies can be experienced as 'glitchy' and fraught with epistemological tensions, as they constantly position their healthy eating practices between algorithmic advice offered by these technologies, and the socio-cultural norms of health and eating that they subscribe to. In negotiating such tensions, people are also found to express agency that has spatial and temporal implications on their health practices. In sum, this paper underscores the importance of critically situating, instead of simply scrutinising, everyday geographies of datafication. Doing so can yield more contextually nuanced insight into the potential and limits of digital technologies in mediating health geographies in different social and cultural contexts.
Efficiency of Virtual Machine Replication in the Data Center

Data centers store the necessary data for the operation of services and applications used by users. They usually consist of many physical servers connected to each other, with virtual machines and other systems, applications, databases installed. In order to increase the reliability of data access and to protect the system from damage or destruction, data from servers are replicated to other centers, servers and instances. The article examines the average replication time for whole virtual machines of various sizes and differential replication of virtual machines for exemplary data center. The research was conducted in order to investigate the influence of bandwidth constraints between data centers on replication times. The article presents sample results from a real system.
Object Detection at the Edge: Off-the-shelf Deep Learning Capable Devices and Accelerators

The advent of energy efficient, embedded Deep Learning accelerators have brought inference capabilities in conjunction with Internet of Things devices in a pervasive manner. Once limited to passive data-gathering, such devices are now able to actively take part in data processing operations and predictive tasks with acceptable performance. Shifting such computation to the edge allows the creation of interconnected environments able to achieve efficient and low powered inference capabilities at the edge, without being dependent on external services in the cloud. Despite significant recent advancements, the field of Edge-ML is still maturing. Therefore, it is important to develop a framework to evaluate the performance of off-the-shelf hardware and state-of-the-art Deep Learning models suited for low-powered devices. Such a framework can be applied to new devices and models as they become available in the future. This paper describes such an evaluation framework, as well as a broad study of different Edge-ML devices, with comparisons in terms of performance, capabilities, limitations, and possible applications with a focus on deploying state-of-the-art Object Detection models. The end objective is enabling the deployment of low-latency and independent decision making processes in both civilian and military contexts.
Deep Learning-Based Detection of Inappropriate Speech Content for Film Censorship

Audible content has become an effective tool for shaping one’s personality and character due to the ease of accessibility to a huge audible content that could be an independent audio files or an audio of online videos, movies, and television programs. There is a huge necessity to filter inappropriate audible content of the easily accessible videos and films that are likely to contain an inappropriate speech content. With this in view, all the broadcasting and online video/audio platform companies hire a lot of manpower to detect the foul voices prior to censorship. The process has a large cost in terms of manpower, time and financial resources. In addition to inaccurate detection of foul voices due to fatigue of manpower and weakness of human visual and hearing system in long time and monotonous tasks. As such, this paper proposes an intelligent deep learning-based system for film censorship through a fast and accurate detection and localization approach using advanced deep Convolutional Neural Networks (CNNs). The dataset of foul language containing isolated words samples and continuous speech were collected, annotated, processed, and analyzed for the development of automated detection of inappropriate speech content. The results indicated the feasibility of the suggested systems by reporting a high volume of inappropriate spoken terms detection. The proposed system outperformed state-of-the-art baseline algorithms on the novel foul language dataset evaluation metrics in terms of macro average AUC (93.85%), weighted average AUC (94.58%), and all other metrics such as F1-score. Additionally, proposed acoustic system outperformed ASR-based system for profanity detection based on the evaluation metrics including AUC, accuracy, precision, and F1-score. Additionally, proposed system was proven to be faster than human manual screening and detection of audible content for films’ censorship.
Learning Antenna Radiation Patterns Through Gaussian Process Regression

Knowledge of an antenna system’s radiation pattern is important for the optimal deployment of radio systems in the field. However, while an antenna’s radiation pattern can be measured in an anechoic chamber, in real-world applications, the radiated signal can also be attenuated by the environment in a manner that is angle dependent. For directional antenna systems, in order to properly steer radiated energy in desired directions, the behavior of the radio system in the real world must be characterized. In this work, we propose a method for using Gaussian Processes regression to learn the deviations of an antenna’s array pattern due to its signal’s interactions with the environment. We evaluate this approach on publicly available benchmark datasets collected on antenna systems at 2.4GHz.
Urdu Wikification and Its Application in Urdu News Recommendation System

Wikification is the process of linking the entities found in a sample text to their individual Wikipedia or Wikidata pages. Many natural language processing applications, including question-answering systems, information retrieval, fraud detection, and recommendation systems(RS), can benefit from this information extraction technique. There has been a great deal of effort put towards entity-linking(EL) for both Asian and Western languages, with several datasets and numerous proposed methodologies. Despite millions of Urdu language users globally, the entity-linking for the Urdu language has not been worked on, to the best of our knowledge. This work proposes an Urdu EL pipeline to identify named entities in text and link them to Wikidata. Secondly, a dataset of 550 Urdu news titles relating to their respective Wiki-ids has been prepared for the examination. Third, utilizing the proposed EL pipeline, 16738 news articles from the first-ever Urdu news RS dataset of 100 users are annotated. Fourthly, a sub Knowledge graph (KG) of 8439 entities and 23080 relationship tuples is retrieved from Wikidata. The Trans-E algorithm is then used to create KG embeddings so that the extracted KG may be used in an Urdu news RS. The final accuracy of Urdu news RS is 60.8%.
Efficient Data Generation for Stroke Classification via Multilayer Perceptron

The aim of this paper is to overcome one of the main problems of machine learning when it faces the medical world: the need of a large amount of data. Through the distorted Born approximation, the scattering parameters and the dielectric contrast in the domain of interest are linked by a linearized integral operator. This method allows to generate a large data-set in a short time. In this work, machine learning is exploited to classify brain stroke presence, typology and position. The classifier model is based on the multilayer perceptron algorithm and it is used firstly for validation and then with a testing set composed by full-wave simulations. In both cases, the model reaches very high level of accuracy.
Phoebe: QoS-Aware Distributed Stream Processing through Anticipating Dynamic Workloads

Distributed Stream Processing systems have become an essential part of big data processing platforms. They are characterized by the high-throughput processing of near to real-time event streams with the goal of delivering low-latency results and thus enabling time-sensitive decision making. At the same time, results are expected to be consistent even in the presence of partial failures where exactly-once processing guarantees are required for correctness. Stream processing workloads are oftentimes dynamic in nature which makes static configurations highly inefficient as time goes by. Static resource allocations will almost certainly either negatively impact upon the Quality of Service and/or result in higher operational costs.In this paper we present Phoebe, a proactive approach to system auto-tuning for Distributed Stream Processing jobs executing on dynamic workloads. Our approach makes use of parallel profiling runs, QoS modeling, and runtime optimization to provide a general solution whereby configuration parameters are automatically tuned to ensure a stable service as well as alignment with recovery time Quality of Service targets. Phoebe makes use of Time Series Forecasting to gain an insight into future workload requirements thereby delivering scaling decisions which are accurate, long-lived, and reliable. Our experiments demonstrate that Phoebe is able to deliver a stable service while at the same time reducing resource over-provisioning.
Utility-aware Semantics for Alternative Service Expressions in Federated SPARQL Queries

SPARQL endpoints are web services that allow for querying RDF knowledge graphs online using the SPARQL query language. In SPARQL 1.1, the federation extension allows for explicitly evaluating expressions of a query at certain SPARQL endpoints using the SERVICE keyword. However, the current foundations of federated SPARQL query processing rely on two central assumptions: (i) members of the federation are assumed to be equal in terms of quality, and (ii) a strict definition of answer completeness, where a SPARQL expression is evaluated over all relevant endpoints. These assumptions hinder the scalability of federated query processing approaches, as the number of contacted sources is potentially high. To address this challenge, in this work, we propose a solution that supports quality descriptions about federation members, and further exploit these descriptions to prune low-quality sources and to speed up query execution. To this end, we present a definition of quality-aware federations of SPARQL endpoints. Moreover, we define and investigate the semantics of recurring ALTERNATIVE SERVICE expressions which are the union of SERVICE requests around the same expression. We then devise a utility-aware semantics for them, in which quality information about the federation is leveraged to potentially reduce the number of endpoints to be contacted. We evaluate the proposed semantics in an experimental study and investigate the utility, execution time, and answer completeness of the proposed semantics for queries in over 60 quality-aware federations of varying size and quality.
A DevSecOps-based Assurance Process for Big Data Analytics

Today big data pipelines are increasingly adopted by service applications representing a key enabler for enterprises to compete in the global market. However, the management of non-functional aspects of the big data pipeline (e.g., security, privacy) is still in its infancy. As a consequence, while functionally appealing, the big data pipeline does not provide a transparent environment, impairing the users’ ability to evaluate its behavior. In this paper, we propose a security assurance methodology for big data pipelines grounded on the DevSecOps development paradigm to increase trustworthiness allowing reliable security and privacy by design. Our methodology models and annotates big data pipelines with non-functional requirements verified by assurance checks ensuring requirements to hold along with the pipeline lifecycle. The performance and quality of our methodology are evaluated in a real walkthrough analytics scenario.
SSP Framework: A New Approach to Designing Lightweight Convolutional Neural Networks

Most convolutional neural network (CNN) designs are still bottlenecked by costly computational load, which impedes their utilization in some industrial applications.To address this issue, a novel design framework is proposed in this paper, which fuses three design strategies that can be applied to the majority of the state-of-the-art popular block-based CNN models to lighten their computing budget, while maintaining comparable accuracies.The novel concept of sparse skip connections is introduced as a block-level design strategy in the models, where each module output is used at both an input to the next module and an input to a three-step-away module to efficiently exploit feature reuse across the blocks.In modulelevel design, a proportional channel split operation strengthened by concatenation is employed to achieve accuracy and model size trade-off.As part of the layer-level strategy, an equal number of input and output channels in layers is selected so that the degree of parallelization is increased for a faster inference time.To demonstrate the framework's efficacy, comprehensive experiments are conducted performing highly competitive visual recognition tasks, on the ImageNet datasets for image classification and on the MS COCO dataset for object detection.The evaluation results, which are based on DenseNet, ResNet, ShiftNet, ShuffleNet, and ShuffleNet-v2, verify that the proposed SSP framework is notably capable of reducing the parameter number, FLOPs, and inference time of existing CNN models with quite alike accuracies.
Databox-based Delivery Service via Blockchain

The ubiquity of sensor technology through the Internet of Things and mobile devices has led to a surge in the generation of personal data. The use of personal data to provide personalized services and optimize business decision-making has recently become a popular trend. The data marketplace is one of the most critical places where companies can source data to organize the variety of data source formats and improve individuals’ data self-management (e.g., data access control and usage). This paper proposes a Databox-based delivery service via blockchain that provides data consumers with secure and controlled access to requested data sources of interest. It consists of a databox-based data delivery module and decentralized activities service module. The former is responsible for data delivery and the latter is to provide a one-stop data sharing service on blockchain. We implement a preliminary prototype and conduct an evaluation of system activities and smart contracts utility.