Reviewerly/exHarmony huggingface dataset:
Recognition of Bird's Nest in High Voltage Power Line using SSD

To use this electrical energy wisely all over the world, it is carried out using a medium called high voltage power lines. To transfer the electrical energy safely without any interruption, Regular maintenance and monitoring of the powerlines is needed. The main reason for the faults and damage in the overhead powerlines are due to the presence of external hindrances so-called birds' nest. Our proposed work explores the use of deep learning (DL) to detect birds' nests in high voltage power lines automatically. Two effective approaches are used (i)Visual Geometry Group (VGG16) and (ii)EfficientNetB7 and they act as the backbone architectures to extract features and the extracted features maps with relevant data are transformed into Single Shot Multibox Detector (SSD) which locates the target object - bird's nest, which significantly increases accuracy, precision, recall, and F1-score.
Perimeter Intrusion Detection by Video Surveillance: A Survey

In recent times, we have seen a massive rise in vision-based applications, such as video anomaly detection, motion detection, object tracking, people counting, etc. Most of these tasks are well defined, with a clear idea of the goal, along with proper datasets and evaluation procedures. However, perimeter intrusion detection (PID), which is one of the major tasks in visual surveillance, still needs to be formally defined. A perimeter intrusion detection system (PIDS) aims to detect the presence of an unauthorized object in a protected outdoor site during a certain time. Existing works vaguely define a PIDS, and this has a direct impact on the evaluation of methods. In this paper, we mathematically define it. We review the existing methods, datasets and evaluation protocols based on this definition. Furthermore, we provide a suitable evaluation protocol for real-life application. Finally, we evaluate the existing systems on available datasets using different evaluation schemes and metrics.
An efficient and access policy-hiding keyword search and data sharing scheme in cloud-assisted IoT

By leveraging the cloud computing paradigm, the cloud-assisted Internet of Things (IoT) is able to offer the massive storage and highly available computation services for end-users. When the data collected from IoT devices is endlessly gathering to the cloud center, the data security become new challenges. Encrypting data is an effective measure to guarantee data confidentiality. However, traditional encryption techniques make the data searching and access control inoperative. Current attribute-based keyword search (ABKS) techniques provide a feasibility to achieve data searching and access control over encrypted data simultaneously. However, existing ABKS schemes leak sensitive information via clear access policy and may be unsuitable for certain IoT applications, where the access policies in data might contain private information of data owners such as the security number or the residential area of a resident in smart grids. In this work, we design an efficient and policy-hiding attribute-based keyword search and data sharing scheme (PH-ABKS-DS) in cloud-assisted IoT. To the best of our knowledge, this construction is the first PH-ABKS-DS with practical efficiency that is constructed on prime order group. We provide detailed correctness and security analyses for PH-ABKS-DS. Extensive experiments demonstrate that our proposed PH-ABKS-DS is correct and practical.
Business Intelligence Aspect for Emotions and Sentiments Analysis

Sentiment analysis is contextual mining of text and it analyses people's attitudes, opinions, sentiments, emotions and evaluations. Sentiment analysis is a type of text classification which involving natural language processing. The flexibility of access to social media made it the most influential and effective platform for customers opinion and feelings. Users’ emotional state extracted from social media contains business information and business value for business intelligence, where most of customers writing about their feelings or satisfaction about a product or service on social media. Therefore, this research aims to study emotion and sentiment analysis from business intelligence aspect by analysing the emotion and real reactions in Twitter on PlayStation official account as case study and comparing between the emotion with and without mentioning the official account. The result showed a difference in people's emotions in the two cases where users are more open to expressing their dissatisfaction or opinions when speaking in general to their friends and followers.
Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA

In cryptography, elliptic curve cryptography (ECC) is considered an efficient and secure method to implement digital signature algorithms (DSAs). ECC plays an essential role in many security applications, such as transport layer security (TLS), internet protocol security (IPsec), and wireless sensor networks (WSNs). The proposed designs of ECC hardware implementation only focus on a single ECC variant and use many resources. These proposals cannot be used for resource-constrained applications or for the devices that need to provide multiple levels of security. This work provides a multi-functional elliptic curve digital signature algorithm (ECDSA) and Edwards-curve digital signature algorithm (EdDSA) hardware implementation. The core can run multiple ECDSA/EdDSA algorithms in a single design. The design consumes fewer resources than the other single-functional design, and is not based on digital signal processors (DSP). The experiments show that the proposed core could run up to 112.2 megahertz with Virtex-7 devices while consuming only 10,259 slices in total.
Mining high occupancy patterns to analyze incremental data in intelligent systems

High occupancy pattern mining has been recently studied as an improved method for frequent pattern mining. It considers the proportion of each pattern in the transactions where the pattern occurred. The results of high occupancy pattern mining can be employed for automated control systems in order to make decisions. Meanwhile, the features of the databases have changed, because information technology has advanced. In real-world databases, new transactions are inserted in real time. However, the state-of-the-art approach to high occupancy pattern mining cannot handle incremental databases. Moreover, the existing method also requires a large amount of memory space, because it adopted a BFS-based search in order to find patterns. In this paper, we propose an approach, which is called HOMI (High Occupancy pattern Mining on Incremental databases), that uses a DFS-based search in order to detect patterns, and it mines high occupancy patterns on incremental databases. The performance analysis for both real and synthetic datasets indicates that HOMI has better performance than the state-of-the-art approaches and related algorithms.
Transforming Deep Learning Models for Resource-Efficient Activity Recognition on Mobile Devices

Mobile and wearable sensor technologies have gradually extended their usability into a wide range of applications, from well-being to healthcare. The amount of collected data can quickly become immense to be processed. These time and resource-consuming computations require efficient methods of classification and analysis, where deep learning is a promising technique. However, it is challenging to train and run deep learning algorithms on mobile devices due to resource constraints, such as limited battery power, memory, and computation units. In this paper, we have focused on evaluating the performance of four different deep architectures when optimized with the Tensorflow Lite platform to be deployed on mobile devices in the field of human activity recognition. We have used two datasets from the literature (WISDM and MobiAct) and trained the deep learning algorithms. We have compared the performance of the original models in terms of model accuracy, model size, and resource usages, such as CPU, memory, and energy usage, with their optimized versions. As a result of the experiments, we observe that the model sizes and resource consumption were significantly reduced when the models are optimized compared to the original models.
DASS-CARE 2.0: Blockchain-Based Healthcare Framework for Collaborative Diagnosis in CIoMT Ecosystem

Due to current Covid-19 pandemic, several countries enforce lock-down to prevent pandemic outspread. Hence, the mode of delivery of health services shall change as physical visits are not allowed. As such, the need of tele-medicine service and remote diagnosis have become a necessity. To provide reliable, safe, secure, and sustainable tele-medicine consultancy services, the supporting IT infrastructure need to be transformed. Therefore, it is necessary to use new generation of information technologies such as loT, Blockchain, and cloud computing to transform the traditional medical systems to smart healthcare systems. In this paper, we propose a proof of concept (PoC) of an ameliorated version of our DASS-CARE framework that supports decentralized, accessible, scalable, and secure access to healthcare services based on Internet of Medical things (IoMT) and Artificial Intelligence (AI). In this paper, we propose DASS-CARE 2.0 that offers more medical services including: (a) the real time health monitoring, (b) the collaborative and secure access to medical records, (c) the storage of medical history diagnosis and prescriptions, and (d)the patient&#x0027;s discharge and bills&#x0027; payments. The paper concludes with future changes to the framework that can furnish further services.
Adaptive Streaming of Content-Aware-Encoded Videos in dash.js

In Hypertext Transfer Protocol (HTTP) adaptive streaming, the client makes rate adaptation decisions based on the measured network bandwidth and buffer fullness. This simplifies the adaptation logic; however, it often produces noticeable quality fluctuations during the streaming session. With content-aware encoding (CAE), one can improve the visual quality without increasing the total number of bits spent by carefully choosing where the bits are spent based on human perception. However, an adaptation logic that is unaware of the resulting variable-size segments may cause more stalls, defeating the purpose of improving viewer experience through CAE. This article explains the design steps of a size-aware rate adaptation (SARA) logic for one of the most popular Dynamic Adaptive Streaming over HTTP (DASH) clients, namely dash.js, and shows the improvements in rebuffering behavior and fetching top-resolution segments as a result of applying this logic.
Adaptive User Profiling in E-Commerce and Administration of Public Services

The World Wide Web is evolving rapidly, and the Internet is now accessible to millions of users, providing them with the means to access a wealth of information, entertainment and e-commerce opportunities. Web browsing is largely impersonal and anonymous, and because of the large population that uses it, it is difficult to separate and categorize users according to their preferences. One solution to this problem is to create a web-platform that acts as a middleware between end users and the web, in order to analyze the data that is available to them. The method by which user information is collected and sorted according to preference is called ‘user profiling‘. These profiles could be enriched using neural networks. In this article, we present our implementation of an online profiling mechanism in a virtual e-shop and how neural networks could be used to predict the characteristics of new users. The major contribution of this article is to outline the way our online profiles could be beneficial both to customers and stores. When shopping at a traditional physical store, real time targeted “personalized” advertisements can be delivered directly to the mobile devices of consumers while they are walking around the stores next to specific products, which match their buying habits.
Consistency-Aware Graph Network for Human Interaction Understanding

Compared with the progress made on human activity classification, much less success has been achieved on human interaction understanding (HIU). Apart from the latter task is much more challenging, the main cause is that recent approaches learn human interactive relations via shallow graphical models, which is inadequate to model complicated human interactions. In this paper, we propose a consistency-aware graph network, which combines the representative ability of graph network and the consistency-aware reasoning to facilitate HIU. Our network consists of three components, a backbone CNN to extract image features, a factor graph network to learn third-order interactive relations among participants, and a consistency-aware reasoning module to enforce labeling and grouping consistencies. Our key observation is that the consistency-aware-reasoning bias for HIU can be embedded into an energy, minimizing which delivers consistent predictions. An efficient mean-field inference algorithm is proposed, such that all modules of our network could be trained jointly in an end-to-end manner. Experimental results show that our approach achieves leading performance on three benchmarks. Code is available at https://git.io/CAGNet.
Matching in the Dark: A Dataset for Matching Image Pairs of Low-light Scenes

This paper considers matching images of low-light scenes, aiming to widen the frontier of SfM and visual SLAM applications. Recent image sensors can record the brightness of scenes with more than eight-bit precision, available in their RAW-format image. We are interested in making full use of such high-precision information to match extremely low-light scene images that conventional methods cannot handle. For extreme low-light scenes, even if some of their brightness information exists in the RAW format images’ low bits, the standard raw image processing on cameras fails to utilize them properly. As was recently shown by Chen et al. [14], CNNs can learn to produce images with a natural appearance from such RAW-format images. To consider if and how well we can utilize such information stored in RAW-format images for image matching, we have created a new dataset named MID (matching in the dark). Using it, we experimentally evaluated combinations of eight image-enhancing methods and eleven image matching methods consisting of classical/neural local descriptors and classical/neural initial point-matching methods. The results show the advantage of using the RAW-format images and the strengths and weaknesses of the above component methods. They also imply there is room for further research.
Anonymous Quantum Sealed-Bid Auction

In this brief, we present a novel quantum protocol for sealed-bid auction and design the corresponding quantum circuits. In the proposed protocol, we introduce two auctioneers to ensure that they can supervise each other, where one is a classical auctioneer, and the other is a quantum auctioneer. Furthermore, we turn sealed-bid auction into secure multiparty summation by employing the Chinese Remainder Theorem to achieve the anonymity. Compared with the related protocols, the proposed protocol can meet more security properties, e.g., anonymity, fairness, verifiability, secrecy and non-repudiation.
Artificial Intelligence

Artificial intelligence (AI) is a scientific discipline that seeks to understand intelligence through the design and construction of intelligent machines. AI and cognitive science have a strong two-way relationship: Cognitive psychology often has inspired AI theories, and AI research has led to new theories of cognition that have been tested through psychological experimentation. While AI theories of cognition often are under-constrained, cognitive theories of AI tend to be over-constrained. Nevertheless, AI is useful for cognitive psychologists both as a source of new ideas and insights, and an experimental testbed. In this chapter, we describe some of the basic concepts and methods of AI by taking robot navigation in a city as an illustrative example. We also briefly discuss the history of AI, methods for assessing progress in AI, and some of AI’s potential impacts on society.
Literature Review of Augmented Reality Application in the Architecture, Engineering, and Construction Industry With Relation to Building Information

The rapid development and adoption of AR applications creates numerous opportunities for integrating AR with BIM and improving conventional methods used in the fields of architecture, engineering, and construction (AEC). In this chapter, the current trends in the development of AR applications and the application of AR technologies in the fields of AEC are proposed. Also, the relation between AR application and BIM in the AEC industry with the benefits of this integration and possible issues is discussed. The related examples of BIM+AR are described during the literature review. The authors believe the papers presented in this document cover the latest research trends and developments in the use of AR and its combination with BIM for AEC applications. In the future, it is expected that AR applications will be further utilized in the AEC field to enhance productivity, safety, and efficiency.
Introduction

Data science has been growing in popularity over the past few years. In the introduction, the terms “data science” and its taxonomy are defined. This chapter covers the relationship between artificial intelligence, machine learning, and data science, provides the motivation for data science, an introduction to key algorithms, and presents a roadmap for rest of the book.
Collaboration in Global Software Development: An Investigation on Research Trends and Evolution

Global software development (GSD) done by geographically distributed teams of developers is one of the most common ways of developing software nowadays. Though GSD has various benefits, it also introduces challenges that have led to a plethora of research. This paper analyzes research papers published in top software engineering venues in recent years (2009-2018) focusing on team collaboration in order to understand the trend in GSD research. Out of 4,292 papers published in these venues, we found 33 papers that focused on team collaboration in the context of GSD. We study the kinds of data used in these papers and classify them into primary data (i.e., interview and observation data) and secondary data (i.e., repository and communication data) and found that interview data is the dominant type of data in these papers. We also found that the strength of evidence presented in most papers tends to be moderate.
ApparelNet: Person Verification Encompassing Auxiliary Attachments Variation

Auxiliary attachments change is more frequent than essential clothing in human beings. Usually, auxiliary attachments include coat, jumper, hat and bag etc. It is one of the hardest recognition task in machine vision. It becomes more difficult if a specific person is reappearing after a longer time period while the other influential factors are angle variation and walking speed etc. One of the key application areas for person verification is border control where auxiliary attachments variation is more common. It is usually reflection of ethnicity or fashion. In machine vision, availability of such datasets is very limited, in particular, having reappearance after longer time period i.e., more than weeks or months. To overcome limited dataset problem, transfer learning is a leading solution for improved verification. In this paper, we proposed an aggregated deep learning model called, ApparelNet, more specifically for person verification in border control environment. We used Front-View Gait (FVG) to evaluate the performance of our aggregated model. The FVG is a pedestrian dataset of people encompassing auxiliary attachments variation, having three different angles from the camera and three different walking speeds. Our ApparelNet acquires single image based detection confidence using OpenPose and later additional layers of pre-trained EfficientNetB0 are trained on custom FVG dataset, including fine tuning of the overall EfficientNetB0. The EfficientNetB0 is highly efficient and scalable transfer learning model from the family of Deep-CNN. Overall, our ApparelNet reported training and validation accuracy of 98%, while looking at border control scenario, model verification is performed by selecting random images of 12 different individuals and prediction probability is computed which accumulates to 96%. In our opinion, the model has strong candidature for person reidentification where goal is one-to-many recognition. It may become an ancillary component of any biometrics system too.
Human Behavior and Anomaly Detection using Machine Learning and Wearable Sensors

This paper addresses the problem of detecting and analyzing human behavior using a set of non-privacy invasive wearable sensors aiming to identify potential anomalies. This may be an important tool for increasing the independence and delaying the institutionalization of older adults allowing them to live alone in their homes with little support from caregivers. We propose an experimental web-based distributed system that incorporates data from wearable sensors and machine learning-based algorithms for monitoring the person's behavior and detection of anomalies. Various configurations of feature selection techniques and features as well as manual labeling for supervised learning have been used. In case of anomalies detected in older adult behavior, the caregiver is notified. Finally, we illustrate the system implementation and functionality considering Fitbit smart band sensor and integration with Fitbit Cloud. The results obtained using a public activity dataset with different configurations of machine learning anomaly detection algorithms and features are promising, showing an accuracy of 87% and an F1-score of 0.9.
Improving Teaching Practice in Early Childhood Supported by Mobile Technology

This chapter discusses the findings of an ethnographic case study investigating the implementation of mobile learning at an early childhood centre in Hawkes Bay, New Zealand. The study describes how mobile technology is being used to support children's learning and communication. The findings show that the devices are an integral part of the learning culture of the centre. The devices are being used to actively engage children in the learning environment and support teaching inquiry. As one of the early studies to investigate how mobile technology is being used in early childhood education, the current study provides pedagogically sound examples and insight on how mobile technology can be embedded into early childhood. The study is seen as a starting place for more in-depth investigations into the impact of mobile learning on young children's learning.
Spatio-Temporal Online Matrix Factorization for Multi-Scale Moving Objects Detection

Detecting moving objects from the video sequences has been treated as a challenging computer vision task, since the problems of dynamic background, multi-scale moving objects and various noise interference impact the corresponding feasibility and efficiency. In this paper, a novel spatio-temporal online matrix factorization (STOMF) method is proposed to detect multi-scale moving objects under dynamic background. To accommodate a wide range of the real noise distractions, we apply a specific mixture of exponential power (MoEP) distributions to the framework of low-rank matrix factorization (LRMF). For the optimization of solution algorithm, a temporal difference motion prior (TDMP) model is proposed, which estimates the motion matrix and calculates the weight matrix. Moreover, a partial spatial motion information (PSMI) post-processing method is further designed to implement multi-scale objects extraction in varieties of complex dynamic scenes, which utilizes partial background and motion information. The superiority of the STOMF method is validated by massive experiments on practical datasets, as compared with state-of-the-art moving objects detection approaches.