Reviewerly/exHarmony huggingface dataset:
Smart Learning in the Community: Supporting Citizen Digital Skills and Literacies

This paper discusses how smart learning in urban environments can mediate citizen digital skills and competences learning initiatives supported by standards such as the European Commission citizen Digital Competences Framework [8], thus helping a broad range of urban populations to gain essential knowledge and skills for navigating the digitised services of the societal urban systems around them. Smart learning, based on cultural, civic or community interests and placed within a context of ad-hoc urban learning experiences set in authentic learning hyper-local environments might support and develop citizen digital literacies and competences through a wide variety of informal learning activities. This kind of technologically mediated learning acts as an implicit conduit to channel the development of a number of skills and literacies involved in the use of digital apps and devices, and the manipulation of knowledge content both digitally created as well as consumed. Additionally, development of 'soft' skills such as community participation, confidence building and language literacy are increased in digitally connected spheres, enabling citizens to act with more self assured agency within these territories. The paper refers to the author's own doctoral research findings developed from a phenomenographic investigation into smart learning journeys to suggest a 'pedagogy of experience complexity' for smart learning as support for these kinds of learning activities.
A Novel Method of Curve Fitting Based on Optimized Extreme Learning Machine

In this article, we present a new method based on extreme learning machine (ELM) algorithm for solving nonlinear curve fitting problems. Curve fitting is a computational problem in which we seek an underlying target function with a set of data points given. We proposed that the unknown target function is realized by an ELM with introducing an additional linear neuron to correct the localized behavior caused by Gaussian type neurons. The number of hidden layer neurons of ELM is a crucial factor to achieve a good performance. An evolutionary computation algorithm–particle swarm optimization (PSO) technique is applied to determine the optimal number of hidden nodes. Several numerical experiments with benchmark datasets, simulated spectral data and measured data from high energy physics experiments have been conducted to test the proposed method. Accurate fitting has been accomplished for various tough curve fitting tasks. Comparing with the results of other methods, the proposed method outperforms the traditional numerical-based technique. This work clearly demonstrates that the classical numerical analysis problem-curve fitting can be satisfactorily resolved via the approach of artificial intelligence.
Mobile Energy Requirements of the Upcoming NIST Post-Quantum Cryptography Standards

Standardization of Post-Quantum Cryptography (PQC) was started by NIST in 2016 and has proceeded to its second elimination round. The upcoming standards are intended to replace (or supplement) current RSA and Elliptic Curve Cryptography (ECC) on all targets, including lightweight, embedded, and mobile systems. We present an energy requirement analysis based on extensive measurements of PQC candidate algorithms on a Cortex M4 - based reference platform. We relate computational (energy) costs of PQC algorithms to their data transmission costs which are expected to increase with new types of public keys and ciphertext messages. The energy, bandwidth, and latency needs of PQC algorithms span several orders of magnitude, which is substantial enough to impact battery life, user experience, and application protocol design. We propose metrics and guidelines for PQC algorithm usage in IoT and mobile systems based on our findings. Our evidence supports the view that fast structured-lattice PQC schemes are the preferred choice for cloud-connected mobile devices in most use cases, even when per-bit data transmission energy cost is relatively high.
Evaluating Performance of Deep Learning Architectures for Image Classification

VGGNet is a significantly more accurate CNN architecture that is more recently introduced. There has always been a question of which neural network architecture performs well in which scenario. Thus, using the Fashion MNIST dataset, the performance of VGGNet and CNN deep learning architectures is reviewed, and the metrics are compared. A 3 Layer CNN architecture was used in this work to achieve 98.92% training accuracy and 0.02 training loss and a maximum test accuracy of 90.77% in classifying 10000 images of 10 different types.
Convolution Neural Network Models for Acute Leukemia Diagnosis

Acute leukemia is a cancer-related to a bone marrow abnormality. It is more common in children and young adults. This type of leukemia generates unusual cell growth in a short period, requiring a quick start of treatment. Acute Lymphoid Leukemia (ALL) and Acute Myeloid Leukemia (AML) are the main responsible for deaths caused by this cancer. The classification of these two leukemia types on blood slide images is a vital process of and automatic system that can assist doctors in the selection of appropriate treatment. This work presents a convolutional neural networks (CNNs) architecture capable of differentiating blood slides with ALL, AML and Healthy Blood Slides (HBS). The experiments were performed using 16 datasets with 2,415 images, and the accuracy of 97.18% and a precision of 97.23% were achieved. The proposed model results were compared with the results obtained by the state of the art methods, including also based on CNNs.
Encoder-Decoder Architecture for Image Caption Generation

Describing the contents of an image without human intervention is a complex task. Computer Vision and Natural Language Processing are widely used for tackling this problem. It requires an approach with two distinct methods, to understand the contents of the image using computer vision, convert the understanding into semantically correct sentences. Convolutional Neural Network (CNN) is a widely used powerful image feature extraction algorithm for object detection and image classification. Gated Recurrent Unit (GRU) is typically used for effective sentence generation. A combined model of CNN and GRU was proposed to achieve accurate image captions. With the proposed model, an experimentation was done with various datasets and compared the results with existing work. BLEU evaluation metrics was used for benchmarking the results; The proposed model results in a BLEU-4 score (the higher the better) on the MS-COCO 2017 dataset as 53.5.
How Does Team Composition Affect Knowledge Gain of Users in Collaborative Web Search?

Studies in searching as learning (SAL) have revealed that user knowledge gain not only manifests over a long-term learning period, but also occurs in single short-term web search sessions. Though prior works have shown that the knowledge gain of collaborators can be influenced by user demographics and searching strategies in long-term collaborative learning, little is known about the effect of these factors on user knowledge gain in short-term collaborative web search. In this paper, we present a study addressing the knowledge gain of user pairs in single collaborative web search sessions. Using crowdsourcing we recruited 454 unique users (227 random pairs), who then collaboratively worked on informational search tasks spanning 10 different topics and information needs. We investigated how users' demographics and traits, and the interaction between these factors could influence their knowledge gain. We found that in contrast to offline collaboration cases, user demographics such as gender, age, etc. do not significantly effect users' knowledge gain in collaborative web search sessions. Instead, our results highlight the presence of labor division of queries and particular interaction patterns in communication that facilitate knowledge gain in user pairs. Based on these findings, we propose a multiple linear regression model to predict the knowledge gain of users in collaborative web search sessions from the perspective of team composition.
On the Optimal Encoding Ladder of Tiled 360° Videos for Head-Mounted Virtual Reality

Dynamic Adaptive Streaming over HTTP (DASH) has been widely used by several popular streaming services, such as YouTube, Netflix, and Facebook. Adopting DASH requires to pre-determine a set of encoding configurations, called encoding ladder, to generate a set of representations stored on the streaming server. These representations are adaptively requested by clients according to their network conditions during streaming sessions. In this article, we aim to solve the optimal laddering problem that determines the optimal encoding ladder to maximize the client viewing quality. In particular, we consider video models, viewing probability, and client distribution to formulate the mathematical problem. We use a divide-and-conquer approach to decompose the problem into two subproblems: (i) per-class optimization for clients with different bandwidths and (ii) global optimization to maximize the overall viewing quality under the storage limit of the streaming server. We propose two algorithms for each of the per-class optimization and global optimization problems. Analytical analysis and real experiments are conducted to evaluate the performance of our proposed algorithms, compared to other state-of-the-art algorithms. Based on the results, we recommend a combination of the proposed algorithms to solve the optimal laddering problem. The evaluation results show the merits of our recommended algorithms, which: (i) outperform the state-of-the-art algorithms by up to 52.17 and 26.35 in Viewport Video Multi-Method Assessment Fusion (V-VMAF) in per-class optimization, (ii) outperform the state-of-the-art algorithms by up to 43.14 in V-VMAF for optimal laddering in global optimization, (iii) achieve good scalability under different storage limits and number of bandwidth classes, and (iv) run faster than the state-of-the-art algorithms.
Implementation and Testing of a Blockchain based Recruitment Management System

Blockchain Technology is used to develop applications more easily and maintain the information as safe and secure. This work is aimed towards developing a recruitment process supported by blockchain technology. Currently, a manual system is used to recruit employees; Manual recruitment is a time consuming, costlier non-secure process. Selection and recruiting the candidates is a vital process for an organization. The proposed method uses blockchain technology for the recruitment process. The entered candidate details are verified by the college, organization for the last workplaces, and law enforcement. Based on the approval, the candidate data is stored in a block with its transaction hash value. The transaction hash consists of the unique predefined fixed-length text of the block and it secures the data from tampering. SHA-256 is one of the hashing algorithms to develop a transaction hash. Once the candidate information is stored in the blockchain, the recruiters read the candidate data from the blockchain and select the validated candidate for the recruitment process. It ensures that they are retrieving the valid details of candidates and can proceed for the further recruitment process. The technology uses a Smart contract under the Ethereum platform to provide security for the recruitment application and tested using the Ganache tool.
Real-time object tracking based on improved fully-convolutional siamese network

In recent years, the tracking model based on the Siamese Network has been widely used in the object tracking field to model the object tracking task as a similarity matching problem, which balances the tracking speed and accuracy. However, there are insufficient robustness, discriminative ability and generalization ability for object deformation and complex background interference. In this paper, an improved Fully-convolutional Siamese Network is proposed. The Triplet Loss function is used as the model objective function instead of logistic loss, and the multi-channel attention mechanism is introduced to make the model pay more attention to the tracking related information and enhance the model discriminating ability. In the offline training phase, an effective data augmentation strategy is used to control the uneven distribution of sample categories and improve the generalization ability of the model. In the tracking phase, the Distractor-aware module is used to transfer the general feature representation domain to a specific object domain, thereby improving model discriminating ability. In experiments, the results on VOT2016 tracking benchmark shows that our model has a significant improvement over the SiamFC tracker in multiple evaluation indicators.
Improving Object Detection Performance Using Scene Contextual Constraints

Contextual information, such as the co-occurrence of objects and the spatial and relative size among objects, provides rich and complex information about digital scenes. It also plays an important role in improving object detection and determining out-of-context objects. In this work, we present contextual models that leverage contextual information (16 contextual relationships are applied in this article) to enhance the performance of two of the state-of-the-art object detectors (i.e., faster RCNN and you look only once), which are applied as a postprocessing process for most of the existing detectors, especially for refining the confidences and associated categorical labels, without refining bounding boxes. We experimentally demonstrate that our models lead to enhancement in detection performance using the most common data set used in this field (MSCOCO), where in some experiments, PASCAL2012 is also used. We also show that iterating the process of applying our contextual models also enhances the detection performance further.
Cultural Heritage Design Element Labeling System With Gamification

Cultural heritage (CH) artifacts, such as ceramics and clothes, reflect the unique characteristics of ancient cultures and have the potential to be sustainably employed in modern design and entertainment. In particular, the shape of ceramics reflects regional and historical characteristics, so datafication is a promising avenue to preserve these assets for future generations. However, design is a specialized domain that requires significant human (expert and novice) labor. This often tedious process decreases the labeler's motivation to complete the task, and data consistency varies with the experience and motivation of the labeler. To increase engagement, we developed an image labeling platform with graphical icon-based labeling methods and introduced gamification. The robust labeling methods with gamification increased novices' engagement and decreased the workload of expert and novice labelers, but decreased data agreement between experts and novices, so we consider opportunities for gamification within the specialized cultural heritage domain.
Beyond the Buzzwords: On the Perspective of AI in UX and Vice Versa

Integrating Artificial Intelligence (AI) technologies promises to open new possibilities for the development of smart systems and the creation of positive user experiences. While the acronym «AI»has often been used inflationary in recent marketese advertisements, the goal of the paper is to explore the relationship of AI and UX in concrete detail by referring to three case studies from our lab. The first case study is taken from a project targeted at the development of a clinical decision support system, while the second study focuses on the development of an autonomous mobility-on-demand system. The final project explores an innovative, AI-injected prototyping tool. We discuss challenges and the application of available guidelines when designing AI-based systems and provide insights into our learnings from the presented case studies.
Frequency Manipulations in Single-Photon Quantum Transport under Ultrastrong Driving

All-optical manipulation of a single-photon quantum transport is of fundamental importance in quantum nanophotonics. Here, a system consisting of a single-mode waveguide coupling with a V-type three-level atom is proposed, where two excited states of the atom are strongly driven by the external coherent field. Transmission and reflection spectra for the output photon at different frequency components, including virtual transitions induced by the ultrastrong driving, are calculated. The interaction between the waveguide photon and the driven atom experiences quantum interferences, which leads to a variety of capabilities for manipulating the single-photon transport in the spectral domain. We perform simulations to demonstrate the possibilities of achieving desired frequency conversions as well as generating different correlated single-photon states by using external drive field at chosen strength. Our work therefore studies the single-photon transport in a strongly driven atom–waveguide system and shows applications for all-optically manipulating the single photon in the field of quantum information processing.
An Ensemble Based Classification Approach for Persian Sentiment Analysis

In recent years, sentiment analysis received a great deal of attention due to the accelerated evolution of the Internet, by which people all around the world share their opinions and comments on different topics such as sport, politics, movies, music and so on. The result is a huge amount of available unstructured information. In order to detect positive or negative subject’s sentiment from this kind of data, sentiment analysis technique is widely used. In this context, here, we introduce an ensemble classifier for Persian sentiment analysis using shallow and deep learning algorithms to improve the performance of the state-of-art approaches. Specifically, experimental results show that the proposed ensemble classifier achieved accuracy rate up to 79.68%.
Semi-Steady-State Jaya Algorithm for Optimization

The Jaya algorithm is arguably one of the fastest-emerging metaheuristics amongst the newest members of the evolutionary computation family. The present paper proposes a new, improved Jaya algorithm by modifying the update strategies of the best and the worst members in the population. Simulation results on a twelve-function benchmark test-suite as well as a real-world problem of practical importance show that the proposed strategy produces results that are better and faster in the majority of cases. Statistical tests of significance are used to validate the performance improvement.
Multi-path connected network for medical image segmentation

In recent years, deep learning has been successfully applied to medical image segmentation. However, as the network extends deeper, the consecutive downsampling operations will lead to more loss of spatial information. In addition, the limited data and diverse targets increase the difficulty for medical image segmentation. To address these issues, we propose a multi-path connected network (MCNet) for medical segmentation problems. It integrates multiple paths generated by pyramid pooling into the encoding phase to preserve semantic information and spatial details. We utilize multi-scale feature extractor block (MFE block) in the encoder to obtain large and multi-scale receptive fields. We evaluated MCNet on three medical datasets with different image modalities. The experimental results show that our method achieves better performance than the state-of-the-art approaches. Our model has strong feature learning ability and is robust to capture different scale targets. It can achieve satisfactory results while using only 0.98 million (M) parameters.
Conceptual Framework for Auditing Agile Projects

Most organizations use IT projects as vehicles to implement the IT strategy which contributes to achieving organizational strategic objectives and goals.Agile project management has been a potential solution to deliver successful IT projects instead of the traditional waterfall approach.This potential has resulted in organizations adopting agile project management to deliver IT projects on time and realise benefits.However, auditors especially those whose experience lies with more traditional and system development life cycle controls are struggling with how to audit agile projects.The problem addressed in this article is lack of an audit framework for auditing agile projects to ensure IT project success.The purpose of this article is to present the proposed conceptual framework for auditing agile projects that are implemented using Scrum methodology.The framework introduces audit processes in each process of the Scrum methodology.The quantitative research method used online survey questionnaire to validate the conceptual framework amongst IT professionals in South Africa.The data were analyzed using SPSS 26.0.The research revealed that there was a significant correlation between the identified audit processes and agile project success.This research emphasizes on the need to take into account auditing agile project from its initiation to closure.This article contributes to the body of knowledge with regard to project auditing.Auditors can use the proposed framework to audit agile projects which are implemented using Scrum methodology to ensure successful completion of IT projects.
Automated Vulnerability Testing via Executable Attack Graphs

Cyber risk assessments are an essential process for analyzing and prioritizing security issues. Unfortunately, many risk assessment methodologies are marred by human subjectivity, resulting in non-repeatable, inconsistent findings. The absence of repeatable and consistent results can lead to suboptimal decision making with respect to cyber risk reduction. There is a pressing need to reduce cyber risk assessment uncertainty by using tools that use well defined inputs, producing well defined results. This paper presents Automated Vulnerability and Risk Analysis (AVRA), an end-to-end process and tool for identifying and exploiting vulnerabilities, designed for use in cyber risk assessments. The approach presented is more comprehensive than traditional vulnerability scans due to its analysis of an entire network, integrating both host and network information. AVRA automatically generates a detailed model of the network and its individual components, which is used to create an attack graph. Then, AVRA follows individual attack paths, automatically launching exploits to reach a particular objective. AVRA was successfully tested within a virtual environment to demonstrate practicality and usability. The presented approach and resulting system enhances the cyber risk assessment process through rigor, repeatability, and objectivity.
Explainable Artificial Intelligence: What Do You Need to Know?

Abstract In domains which require high risk and high consequence decision making, such as defence and security, there is a clear requirement for artificial intelligence (AI) systems to be able to explain their reasoning. In this paper we examine what it means to provide explainable AI. We report on research findings to propose that explanations should be tailored, depending upon the role of the human interacting with the system and the individual system components, to reflect different needs. We demonstrate that a ‘one-size-fits-all’ explanation is insufficient to capture the complexity of needs. Thus, designing explainable AI systems involves careful consideration of context, and within that the nature of both the human and AI components.