Reviewerly/exHarmony huggingface dataset:
Enhancing activity recognition using CPD-based activity segmentation

Segmenting behavior-based sensor data and recognizing the activity that the data represents are vital steps in all applications of human activity learning such as health monitoring, security, and intervention. In this paper, we enhance activity recognition by identifying activity transitions. To accomplish this goal, we introduce a change point detection-based activity segmentation model which partitions behavior-driven sensor data into non-overlapping activities in real time. In addition to providing valuable activity information, activity segmentation also can be used to improve the performance of activity recognition. We evaluate our proposed segmentation-enhanced activity recognition method on data collected from 29 smart homes. Results of this analysis indicate that the method not only provides useful information about activity boundaries and transitions between activities but also increases recognition accuracy by 7.59% and f measure by 6.69% in comparison with the traditional window-based methods.
User perception of Bitcoin usability and security across novice users

This paper investigates users’ perceptions and experiences of an anonymous digital payment system (Bitcoin) and its influence on users in terms of usability and security in comparison to other non-anonymous payment systems such as credit/debit cards. This paper identifies users’ perceptual differences in terms of usability and security. Two versions of user survey are used to collect data, which reveal significant differences in users’ perceptions of credit/debit cards and Bitcoin. The usability attributes of both systems examined show that respondents perceive the usability of credit/debit cards to be higher than Bitcoin. This has a great negative influence on users’ security perceptions of Bitcoin. We conclude that Bitcoin, as a crypto-currency, is still in its infancy and requires user education and a new way of thinking. The study recommends developing users’ mental models to deepen developers’ understanding of anonymous digital payment technology and improve user-centred design. We also make recommendations with respect to e-government services that may be developed relying on crypto-currencies.
Blockchain and Internet of Things-Based Technologies for Intelligent Water Management System

Water is a critical and indispensable resource for the sustainability of life, economic development, and the environment. According to the United Nations (UN) estimates, 70% of the world's population will live in cities by the year 2025, and the current centralized piped infrastructure relied upon by water utilities will be inadequate. Leveraging on the advancement in emerging blockchain, Internet of Things (IoT), and sensor technologies offers a means for efficient water management. In this era of Fourth Industrial Revolution (4IR), human creativity will be a critical requirement in this regard. This chapter explores the impact of blockchain and IoT on water management and examines the feasibility of its adoption in multiple case scenarios and instances such as stormwater management, water quality monitoring and reporting directly to consumers and other relevant stakeholders, and smart payment and contract, in order to sustainably deal with the challenges of global water crisis induced by climate change and rapid population growth. This chapter makes special and unique emphasis on the relevance of the research through an African perspective and view. Furthermore, the technical advantages, socioeconomic gains, and technological benefits of synergizing blockchain and IoT such as enhanced security and transparency, reduced operational cost, overall efficiency, and other merits are expatiated.
A Deep Collaborative Framework for Face Photo–Sketch Synthesis

Great breakthroughs have been made in the accuracy and speed of face photo-sketch synthesis in recent years. Regression-based methods have gained increasing attention, which benefit from deeper and faster end-to-end convolutional neural networks. However, most of these models typically formulate the mapping from photo domain X to sketch domain Y as a unidirectional feedforward mapping, G: X → Y , and vice versa, F: Y → X ; thus, the utilization of mutual interaction between two opposite mappings is lacking. Therefore, we proposed a collaborative framework for face photo-sketch synthesis. The concept behind our model was that a middle latent domain ~Z between the photo domain X and the sketch domain Y can be learned during the learning procedure of G: X → Y and F: Y → X by introducing a collaborative loss that makes full use of two opposite mappings. This strategy can constrain the two opposite mappings and make them more symmetrical, thus making the network more suitable for the photo-sketch synthesis task and obtaining higher quality generated images. Qualitative and quantitative experiments demonstrated the superior performance of our model in comparison with the existing state-of-the-art solutions.
Robust dimensionality reduction via feature space to feature space distance metric learning

Images are often represented as vectors with high dimensions when involved in classification. As a result, dimensionality reduction methods have to be developed to avoid the curse of dimensionality. Among them, Laplacian eigenmaps (LE) have attracted widespread concentrations. In the original LE, point to point (P2P) distance metric is often adopted for manifold learning. Unfortunately, they show few impacts on robustness to noises. In this paper, a novel supervised dimensionality reduction method, named feature space to feature space distance metric learning (FSDML), is presented. For any point, it can construct a feature space spanned by its k intra-class nearest neighbors, which results in a local projection on its nearest feature space. Thus feature space to feature space (S2S) distance metric will be defined to Euclidean distance between two corresponding projections. On one hand, the proposed S2S distance metric displays superiority on robustness by the local projection. On the other hand, the projection on the nearest feature space contributes to fully mining local geometry information hidden in the original data. Moreover, both class label similarity and dissimilarity are also measured, based on which an intra-class graph and an inter-class graph will be individually modeled. Finally, a subspace can be found for classification by maximizing S2S based manifold to manifold distance and preserving S2S based locality of manifolds, simultaneously. Compared to some state-of-art dimensionality reduction methods, experiments validate the proposed method's performance either on synthesized data sets or on benchmark data sets.
The Influence Ranking for Testers in Bug Tracking Systems

At present, bug tracking systems are used to collect and manage bug reports in many software projects. As participants, the testers not only submit bug reports to the system, but also comment on bug reports in the system. The tester’s behaviors of submitting and commenting reflect his/her influence in bug tracking systems. However, with the rapid increase of the bug reports in software projects, evaluating the testers’ influence in the projects accurately becomes more and more difficult. Aiming at solving this problem, the submission and comment on bug report can be regarded as social behaviors of the testers, and thus the method of Influence Ranking for Testers (IRfT) in bug tracking systems is presented and used for measuring the influence of the testers in this paper. The case study of the Eclipse project in Bugzilla shows that the result produced by IRfT is consistent with the actual performance of the testers in this project. The ranking results can keep stable in the cases of link adding or removing and tester removing in tester networks, and the results are also proved to be valid in the future. The further investigation on the speed of network break-down by node removal demonstrates that the top-ranking testers are important in the organization of tester networks. Additionally, the results also show that the ranking of the testers is related to the existence time in bug tracking system. Therefore, IRfT is proved to be an effective measurement for evaluating the influence of the testers in bug tracking system, and it can further demonstrate the testers’ contributions in software testing, such as bug validations, bug fixes, etc.
Assessing recall bias and measurement error in high-frequency social data collection for human-environment research

A major impediment to understanding human-environment interactions is that data on social systems are not collected in a way that is easily comparable to natural systems data. While many environmental variables are collected with high frequency, gridded in time and space, social data is typically conducted irregularly, in waves that are far apart in time. These efforts typically engage respondents for hours at a time, and suffer from decay in participants’ ability to recall their experiences over long periods of time. Systematic use of mobile and smartphones has the potential to transcend these challenges, with a critical first step being an evaluation of where survey respondents experience the greatest recall decay. We present results from, to our knowledge, the first systematic evaluation of recall bias in components of a household survey, using the Open Data Kit (ODK) platform on Android smartphones. We tasked approximately 500 farmers in rural Bangladesh with responding regularly to components of a large household survey, randomizing the frequency of each task to be received weekly, monthly, or seasonally. We find respondents’ recall of consumption and experience (such as sick days) to suffer much more greatly than their recall of the use of their households’ time for labor and farm activities. Further, we demonstrate a feasible and cost-effective means of engaging respondents in rural areas to create and maintain a true socio-economic “baseline” to mirror similar efforts in the natural sciences.
Tracing research trends of 21st‐century learning skills

Abstract Recently, learning technologies have become a pivotal constituent of teaching–learning processes. Contemporary studies indicate that in order to effectively utilize these technologies, instructors and learners alike must master a range of cognitive and socio‐emotional competencies, commonly termed “digital literacy competencies” or “21 st century skills.” This study employs a content analysis methodology to trace trends of change in the research of core digital literacy competencies as reflected in the educational academic literature of the past 37 years (1980–2016). Based on well‐established frameworks of digital literacy competencies, this research examined seven skills: Collaboration, Communication, Creativity, Critical thinking, Information literacy, Problem‐solving and Socio‐emotional skills. Data were collected through advanced search queries of peer‐reviewed publications in the Education Resources Information Center (ERIC) database. Findings reveal that among the examined terms, Communication is the most prominent digital literacy skill, followed by Problem Solving and Collaboration . Furthermore, within the context of Skills , the most prominent terms are Information Literacy and Critical thinking ; Technology and Collaboration are least mentioned. Our findings suggest that awareness of digital learning competencies in educational research literature is marginal, implying that educational research seems to lag behind the need to understand the ever‐changing digital competencies that instructors and learners need.
Dictionary Representation of Deep Features for Occlusion-Robust Face Recognition

Deep learning has achieved exciting results in face recognition; however, the accuracy is still unsatisfying for occluded faces.To improve the robustness for occluded faces, this paper proposes a novel deep dictionary representation-based classification scheme, where a convolutional neural network is employed as the feature extractor and followed by a dictionary to linearly code the extracted deep features.The dictionary is composed by a gallery part consisting of the deep features of the training samples and an auxiliary part consisting of the mapping vectors acquired from the subjects either inside or outside the training set and associated with the occlusion patterns of the testing face samples.A squared Euclidean norm is used to regularize the coding coefficients.The proposed scheme is computationally efficient and is robust to large contiguous occlusion.In addition, the proposed scheme is generic for both the occluded and non-occluded face images and works with a single training sample per subject.The extensive experimental evaluations demonstrate the superior performance of the proposed approach over other state-of-the-art algorithms.
Bidirectional Quantum Teleportation by Using Six-Qubit Cluster State

Quantum teleportation is considered as the most basic process of quantum communication. It is undoubtedly beneficial for the realization of quantum teleportation while transmitting the maximum number of quantum bits with the minimum number of quantum channel resources. An improved bidirectional quantum teleportation protocol based on a quantum channel of six-qubit cluster state is proposed. In this paper, the user Alice can transmit an unknown three-qubit entangled state to the user Bob, and at the same time, the user Bob can transmit an arbitrary single-qubit state to the user Alice by utilizing GHz-state measurement, bell-state measurement, single-qubit Von Neumann measurement, and unitary operations. Compared with other schemes in quantum resource consumption, necessary operation complexity including operation difficulty and intensity, classical resource consumption, quantum information bits transmitted, and intrinsic efficiency, our scheme has the remarkable advantages in transmitting more quantum information and possesses higher intrinsic efficiency.
A framework and a performance assessment for serverless MapReduce on AWS Lambda

MapReduce is one of the most widely used programming models for analysing large-scale datasets, i.e. Big Data. In recent years, serverless computing and, in particular, Functions as a Service (FaaS) has surged as an execution model in which no explicit management of servers (e.g. virtual machines) is performed by the user. Instead, the Cloud provider dynamically allocates resources to the function invocations and fine-grained billing is introduced depending on the execution time and allocated memory, as exemplified by AWS Lambda. In this article, a high-performant serverless architecture has been created to execute MapReduce jobs on AWS Lambda using Amazon S3 as the storage backend. In addition, a thorough assessment has been carried out to study the suitability of AWS Lambda as a platform for the execution of High Throughput Computing jobs. The results indicate that AWS Lambda provides a convenient computing platform for general-purpose applications that fit within the constraints of the service (15 min of maximum execution time, 3008 MB of RAM and 512 MB of disk space) but it exhibits an inhomogeneous performance behaviour that may jeopardise adoption for tightly coupled computing jobs.
A novel bit level multiphase algorithm for image encryption based on PWLCM chaotic map

In recent years, various algorithms have been proposed to provide security to send multimedia content over insecure data channels. In this paper, an algorithm for image encryption is proposed based on PWLCM chaotic map and a hash algorithm. Chaos is used in several steps to encrypt gray-scale images. The chaotic function is used to create dynamic behavior in some parts of the algorithm. The proposed algorithm performs the encryption at the bit level. Although any hash algorithm can be used in the proposed algorithm, The SHA-1 hash algorithm was used to implement the proposed algorithm. The analysis result shows that the algorithm can reach a high-security efficiency in just one round while the encryption and decryption time is low.
Segment-level joint topic-sentiment model for online review analysis

With the rapid development of the Internet, an increasing number of users enjoy to shop online and express their reviews on the products and services. Analysis of these online reviews can not only help potential users make rational decisions when purchasing but also improves the quality of products and services. Hence, sentiment analysis for online reviews has become an important and meaningful research domain.
Hidden Ciphertext Policy Attribute-Based Encryption With Fast Decryption for Personal Health Record System

Since cloud computing has been playing an increasingly important role in real life, the privacy protection in many fields has been paid more and more attention, especially, in the field of personal health record (PHR). The traditional ciphertext-policy attribute-based encryption (CP-ABE) provides the fine-grained access control policy for encrypted PHR data, but the access policy is also sent along with ciphertext explicitly. However, the access policy will reveal the users' privacy, because it contains too much sensitive information of the legitimate data users. Hence, it is important to protect users' privacy by hiding access policies. In most of the previous schemes, although the access policy is hidden, they face two practical problems: 1) these schemes do not support large attribute universe, so their practicality in PHR is greatly limited and 2) the cost of decryption is especially high since the access policy is embedded in the ciphertext. To address these problems, we construct a CP-ABE scheme with efficient decryption, where both the size of public parameters and the cost of decryption are constant. Moreover, we also show that the proposed scheme achieves full security in the standard model under static assumptions by using the dual system encryption method.
Inconsistencies on TripAdvisor reviews: A unified index between users and Sentiment Analysis Methods

TripAdvisor is an opinion source frequently used in Sentiment Analysis. On this social network, users explain their experiences in hotels, restaurants or touristic attractions. They write texts of 200 character minimum and score the overall of their review with a numeric scale that ranks from 1 (Terrible) to 5 (Excellent). In this work, we aim that this score, which we define as the User Polarity, may not be representative of the sentiment of all the sentences that make up the opinion. We analyze opinions from six Italian and Spanish monument reviews and detect that there exist inconsistencies between the User Polarity and Sentiment Analysis Methods that automatically extract polarities. The fact is that users tend to rate their visit positively, but in some cases negative sentences and aspects appear, which are detected by these methods. To address these problems, we propose a Polarity Aggregation Model that takes into account both polarities guided by the geometrical mean. We study its performance by extracting aspects of monuments reviews and assigning to them the aggregated polarities. The advantage is that it matches together the sentiment of the context (User Polarity) and the sentiment extracted by a pre-trained method (SAM Polarity). We also show that this score fixes inconsistencies and it may be applied for discovering trustworthy insights from aspects, considering both general and specific context.
Multi-Scale Dense Networks for Deep High Dynamic Range Imaging

Generating a high dynamic range (HDR) image from a set of sequential exposures is a challenging task for dynamic scenes.The most common approaches are aligning the input images to a reference image before merging them into an HDR image, but artifacts often appear in cases of large scene motion.The state-of-the-art method using deep learning can solve this problem effectively.In this paper, we propose a novel deep convolutional neural network to generate HDR, which attempts to produce more vivid images.The key idea of our method is using the coarse-to-fine scheme to gradually reconstruct the HDR image with the multi-scale architecture and residual network.By learning the relative changes of inputs and ground truth, our method can produce not only artificial free image but also restore missing information.Furthermore, we compare to existing methods for HDR reconstruction, and show high-quality results from a set of low dynamic range (LDR) images.We evaluate the results in qualitative and quantitative experiments, our method consistently produces excellent results than existing state-of-the-art approaches in challenging scenes.
Applying spatial prospectivity mapping to exploration targeting: Fundamental practical issues and suggested solutions for the future

Despite many decades of development, spatial prospectivity modelling is not yet widely used or accepted throughout the global mineral exploration industry. A common criticism of the method is that it is not practically useful because it has a bias to mature, well-known areas and generates excessively large areas of high-prospectivity. It is suggested that the reason for this is not primarily related to limitations in the prospectivity mapping algorithms but rather to issues relating to the use of input data sets. Specifically, it is common that the input data (such as geological interpretations) do not uniformly and objectively represent the search space of interest, omit critical targeting-relevant geoscientific elements (such as major, deep-seated ore-controlling structures) and have a large degree of unrecognised dependence. It is considered that these problems are not in principle barriers to the eventual successful deployment of this technology. However, future approaches to spatial prospectivity modelling need to explicitly address these concerns. It is suggested that the most effective method may be a hybrid of subjective human geological interpretation and objective, machine-based analysis, that captures the best aspects of these alternative approaches; i.e., an intelligence amplification (IA) rather than an artificial intelligence (AI) approach. A roadmap is proposed for improving the effectiveness of spatial prospectivity modelling that has implications for the broader community interested in mineral exploration targeting.
OOABKS: Online/offline attribute-based encryption for keyword search in mobile cloud

Mobile cloud computing is a new computing method for mobile applications, which enables storage and computation migration from mobile users to resource-rich and powerful cloud server. More and more mobile phone users share their information through the mobile cloud. As the cloud server is not fully trusted, for security and privacy concerns, data owners usually encrypt their data before outsourcing them to the cloud server. In response to above questions, some attribute-based keyword search (ABKS) schemes have been proposed. However, the key generation, encryption and decryption for ABE take up a lot of computing resources in these schemes. In this paper, we propose an online/offline attribute-based keyword search scheme for mobile cloud (OOABKS). We use online/offline ABE technology and outsource ABE technology to reduce the online calculation cost and the local calculation cost of mobile users. And, we implement the fine-grained access control for the user. Security analysis demonstrates that our scheme can achieve trapdoor unlinkability, keyword security, data privacy security and search controllability. Efficiency analysis shows, in terms of functionalities and the computation overhead, OOABKS is more practical and efficient than existing approaches.