Reviewerly/exHarmony huggingface dataset:
The life span of a print textbook: An investigation in the utility of aging textbook collections in academic libraries

This paper will use circulation data to explore usage trends for physical textbooks in an academic library over a sustained period of time. Conclusions will be drawn from the analysis to determine the usefulness of aging physical textbook collections in academic libraries and inform recommendations for efficient models of lending and management.
Dynamic model‐based feature aided data association filter in target tracking

In this study, the authors consider the data association problem where the feature of a target is available. Most existing data association filters mainly use a statistical or simple model of the feature without explicitly considering the correlation between the target behaviour and feature characteristics. The inaccurate model of the feature could lead to divergence of the estimation error or the loss of a target in heavily cluttered and/or low signal-to-noise ratio environments. To address the problem, they first develop feature models (e.g. target dimensions) dependent on the target behaviour (i.e. the distance between the sensor and the target, and the aspect angle between the longitudinal axis of the target and the axis of sensor line of sight). Then they propose a data association filter which can facilitate the feature models dependent on the target kinematics to reduce the misassociations. The performance of the proposed feature models and data association filter are demonstrated with illustrative target tracking scenarios.
Active Entity Recognition in Low Resource Settings

The task of Named Entity Recognition (NER) has been well studied under high-resource conditions (e.g., extracting named mentions of PERSON, ORGANIZATION and LOCATION from news articles). However, there are very few studies of the NER task for open-domain collections and in low-resource settings. We focus on NER for low-resource collections, in which any entity types of practical interest to the users of the system must be supported. We try to achieve this with a low cost of annotation of data from the target domain/collection. We propose an entity recognition framework that combines active learning and conditional random fields (CRF), and which provides the flexibility to define new entity types as needed by the users. Our experiments on a help & support corpus show that the system can achieve F1 measure of 0.77 by relying on only 100 manually-annotated sentences.
Privacy Preserving AKMA in 5G

3GPP is currently studying AKMA (Authentication and Key Agreement for Applications): a mobile network service intended to support authentication and key management based on 3GPP credentials in 5G system, for third-party applications and 3GPP services. AKMA extends and evolves two earlier services that 3GPP specified for previous generations of mobile systems. Those are GBA (Generic Bootstrapping Architecture) and BEST (Battery Efficient Security for very low Throughput Machine Type Communication (MTC) devices), In this paper, we have first analyzed potential AKMA requirements in the 3GPP study of AKMA vs. GBA and BEST. We have identified two new privacy requirements that could be useful to protect the privacy of user transactions with AKMA application function against, e.g., an insider attacker in the home network of that user. Second, we have developed a privacy-mode for AKMA that fulfills those new requirements.
Using Machine Learning Techniques to Optimize Fall Detection Algorithms in Smart Wristband

The consumer electronics market is already saturated with wearable devices that intend to be used to detect falls and request help from carers or family members. However, these products have a high rate of false alarms which affect their reliable performance. To provide the high accuracy and high precision of fall detection for the elderly, this paper presents a machine learning approach to improve the fall detection accuracy and reduce the false alarms. Three machine learning algorithms are deployed in this research, namely the K-Means, Perceptron Neural Network (PNN), and Convolutional Neural Network (CNN) algorithms. A development board with a 9-axis inertial sensor unit is used as a prototype of wristband to collect data and identify falls from seven daily activities. These data is then used to train and test machine learning algorithms. Experimental results show that the CNN algorithm achieves the highest accuracy comparing with K-mean, PNN and the algorithm used in the existing wristbands.
Keystroke dynamics obfuscation using key grouping

Keystroke dynamics is one of the most widely adopted identity verification techniques in remote systems. It is based on modeling users’ specific patterns of typing on the keyboard. When utilized in conjunction with the commonly used passwords, the use of keystroke dynamics can dramatically increase the level of security without interfering with the user experience. However, aspects of keystroke dynamics that applied on passwords, such as processing keystroke events and storing feature vectors or user models, can expose users to identity theft and a new set of privacy risks, thus questioning the added value of keystroke dynamics. In addition, common encryption techniques will be unable to mitigate these threats, since the user's behavior changes from one session to another. In this paper, we suggest key grouping as an obfuscation method to ensure keystroke dynamics privacy. When applied on the keystroke events, the key grouping dramatically reduces the possibility of password theft. To perform the key grouping optimally, we present a novel method which produces groups that can integrated with any keystroke dynamics algorithm. Our method divides the keys into groups using hierarchical clustering with dedicated statistical heuristics algorithm. We tested our method's key grouping output on five keystroke dynamics algorithms using a public dataset and managed to show a consistent improvement of up to 7% in the AUC over other, more intuitive key groupings and random key groupings.
Students’ reading skills related to science learning using big book media

Abstract Reading skill is an active receptive language skill to get a message from the writer in the written variety. This research is based on the phenomenon reading skill of students in Madrasah Ibtidaiyah Bongas IV Cililin related to science including low category. This study aims to determine the enhancing reading skills of a student after applying Big Book media in science learning. The research method used is a pre-experiment design with one group pretest-posttest design. The subjects of this study are the third-grade students of Madrasah Ibtidaiyah Bongas IV Cililin selected by purposive sampling technique amounted to 22 people. Student reading skill is measured through multiple choice test and supporting instrument in the form of an observation sheet to know student and teacher activity in using Big Book media. The results showed that there was enhancing reading skill of student with normalized gain &lt;g&gt; including high category. Based on the paired sample t-test, using Big Book media in science learning can improve the reading skills of the student. The average results of observation sheet analysis in the book Big Book to improve reading skills of the student at every meeting good category. It can be concluded that the use of Big Book in science learning can be an alternative to enhancing reading skills of the student. Thus, Big Book media can motivate students to have a high read culture.
Building Cryptotokens Based on Permissioned Blockchain Framework

Blockchain technology has a lot of applications including but not limited to cryptotokens. For example, permissioned blockchains for state registries can include no money logic inside. But the creation of cryptotoken is a typical example of blockchain application and is a popular model to measure performance. In this paper, we described how to construct an account-based cryptotoken using Exonum, an open-source framework for creating blockchain applications, and by the means of performance tests shown that the proposed solution meets real-life throughput requirements for many applications. The proposed approach can be used to create cryptocurrencies in other open-source frameworks focused on permissioned blockchain applications.
Cyber-Physical Ubiquitous Cycling With Fuzzy-Controlled Panorama Manifestation Based on Internet of Things Technologies

In this paper, we propose a cyber-physical ubiquitous cycling (CPUC) framework for cyclists using smartphones to ride a bicycle around the world through street view and virtual reality based on Internet of Things (IoT) technologies. Through the proposed CPUC framework, the cyclist can visit everywhere on demand including pedestrian-only/vehicle-prohibited places in a cyber-physical cycling manner. To provide the best ubiquitous cycling experience, a fuzzy control mechanism is designed in CPUC to determine the feasible video frame updating based on the defined fuzzy sets of wheel rotation periods and cycling acceleration. According to our review of relevant research, CPUC is the first cycling solution for spatiotemporal liberation that can achieve the sensitive interaction of street view riding and provide the immersive experience of virtual reality biking through the designed fuzzy-controlled panorama manifestation. In particular, an Android-based prototype with the Arduino developing board is implemented to verify the feasibility and superiority of our CPUC system. Experimental results show that the designed fuzzy control mechanism outperforms the non-fuzzy scheme and can provide the smooth manifestation and high sensitivity of street view/virtual reality.
Augmented Reality Guided Respiratory Liver Tumors Punctures: A Preliminary Feasibility Study

CT-guided radiofrequency ablation (RFA) has evolved rapidly over the past decade and become a widely accepted treatment option for patients with liver tumors. However, it is hard for doctors to locate tumors precisely while avoid damaging the surrounding risk structures with 2D CT images, which only provides limited static information, especially in case of respiratory motion. This paper presents a novel augmented reality guidance modality for improving the precision of liver tumors punctures by providing visual cue of 3D personalized anatomy with respiratory motion. Optical see-through display devices Epson MoveRio BT300 and Microsoft HoloLens are used to mix pre-operative 3D personalized data and intra-operative physical scene. Here an augmented reality based surgical navigation pipeline is proposed to achieve the transformation from raw medical data to virtual guidance information and precisely superimpose this information onto real experimental animal. In addition, to alleviate the difficulty during needle placement induced by respiratory motion, we proposed a correlation model to real-timely predict the tumor position via regression based respiration state estimation and the statistical tumor motion model. We experimentally validated the proposed system on in vivo beagle dogs with artificial lesion, which can effectively improve the puncture efficiency and precision. The proposed augmented reality modality is a general strategy to guide the doctors perform precise percutaneous puncture under respiration conditions and has the potential to be used for other surgical navigation tasks.
Counting solutions to generalized Markoff-Hurwitz-type equations in finite fields

Let Fq be the finite field of order q. Let Nq denote the number of solutions to the generalized Markoff-Hurwitz-type equationx1m1+x2m2+⋯+xnmn=bx1t1x2t2⋯xntn with mi,ti∈Z>0 and b∈Fq⁎. Carlitz proposed the problem of finding an explicit formula for Nq for the special form. Let m=m1⋯mn. Cao proved that Nq=qn−1+(−1)n−1 if gcd⁡(∑i=1ntim/mi−m,q−1)=1. In this paper, we obtain an explicit formula for Nq under certain case when gcd⁡(∑i=1ntim/mi−m,q−1)>1. In particular, for the case of mi=ti=2(i=1,…,n), if either gcd⁡(n−1,q−1)=1 or 2n≡4(modq−1), the formula for Nq can be easily deduced. This generalizes Cao's result as well as partially solves Carlitz's problem.
Abstraction as a Way of Uncertainty Representation in Smart Rules Engine

When creating the Internet of Things and Smart Machines, the RULES ENGINE pattern is developed. The tasks that Rules Engine solves in real time are characterized by large amounts of data from various sensors. To account for uncertainty, a new model of Smart Rules Engine is proposed with a two-step inference engine, including an engine of abstraction from data. A model based on quantitative, definitive and generalization type of abstraction is considered. It is shown that the proposed model solves the problem of the bridge between natural language, as a carrier of the concept's meaning, and data from sensors, as carriers of the perception. A model for presenting a prototype is given in which incomplete knowledge, expert's uncertainty in the definition of the concept and aging of data are taken into account. The prototype and data model representation and the abstraction engine are built based on fuzzy L-R numbers and arithmetic operations on them. The preliminary step of abstraction from the data, besides taking into account the uncertainty of a different nature, significantly reduces the dimension of the fuzzy inference space. A comparison of the two-step fuzzy inference engine with the traditional inference engine Fuzzy Logic System Type-1 is given using the example of a wheeled robot making the decision to safely cross an unregulated crossroad.
Research Progress of Construction of Smart Campus

Abstract Smart campus is a new stage in the development of educational informatization. Focusing on present construction situation of smart campus in universities in China and abroad, the paper firstly reviews of typical construction modes, types of design architecture and major functional modules of smart campus in universities. Secondly, from the perspective of application, service and management for smart education, some key technologies of construction of smart campus in universities are analyzed and summarized. Finally, further work of promoting the construction standards and evaluation of smart campus, training and improving the big data literacy for teachers and students in campus, and exploring effectively on sharing and constructing mechanism on smart campus for local application-oriented universities is expected in this paper.
Securing the Data in Cloud Environment Using Parallel and Multistage Security Mechanism

Cloud computing technology is emerging rapidly due to increasing demand of service required by the different organizations, institutions, and individuals. Nonetheless, the data in the cloud environment is still not completely secure from the outside and inside intrusions and attacks. In this paper, an improvised approach for securing the data in cloud computing environment using parallel and multistage security mechanism (PMSSM) is proposed to provide simultaneous security checks and procedural multistage security using authentication techniques, intrusion detection and encryption. By taking this approach into consideration, the chances of identifying the attack can be increased as the parallelism in the verification and multistage security will enhance the probability of identifying the intrusion or the attack being performed by the attacker. Also, considering the dynamic nature of the intrusion, the parallel and multistage security can help in avoiding such scenario. The discussion of the proposed mechanism suggested that this model can be used to provide enhanced cloud data security to the cloud users.
Meta-control and Self-Awareness for the UX-1 Autonomous Underwater Robot

Autonomous underwater robots, such as the UX-1 developed in the UNEXMIN project, need to maintain reliable autonomous operation in hazardous and unknown environments. Because of the lack of any kind of real-time communications with a human operated command and control station, the control architecture needs to be enhanced with mission-level self-diagnosis and self-adaptation properties an additional provided by some kind of supervisory or “metacontrol” component to ensure its reliability. In this paper, we propose an ontological implementation of such component based on Web Ontology Language (OWL) and the Semantic Web Rule Language (SWRL). The solution is based on an ontology of the functional architecture of autonomous robots, which allows inferring the effects of the performance of its constituents components in the functions required during the robot mission, and generate the reconfigurations needed to maintain operation reliably. The concept solution has been validated using a hypothetical set of scenarios implemented in an OWL ontology and an OWLAPI-based reasoner, which we aim at validating by integrating the metacontrol reasoning with a realistic simulation of the underwater robot.
Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment

For histopathological tumor assessment, the count of mitotic figures per area is an important part of prognostication. Algorithmic approaches - such as for mitotic figure identification - have significantly improved in recent times, potentially allowing for computer-augmented or fully automatic screening systems in the future. This trend is further supported by whole slide scanning microscopes becoming available in many pathology labs and could soon become a standard imaging tool. For an application in broader fields of such algorithms, the availability of mitotic figure data sets of sufficient size for the respective tissue type and species is an important precondition, that is, however, rarely met. While algorithmic performance climbed steadily for e.g. human mammary carcinoma, thanks to several challenges held in the field, for most tumor types, data sets are not available. In this work, we assess domain transfer of mitotic figure recognition using domain adversarial training on four data sets, two from dogs and two from humans. We were able to show that domain adversarial training considerably improves accuracy when applying mitotic figure classification learned from the canine on the human data sets (up to +12.8% in accuracy) and is thus a helpful method to transfer knowledge from existing data sets to new tissue types and species.
A blockchain-based adaptive middleware for large scale internet of things data storage selection

IoT applications with massive generation of data have different service requirements in terms of performance, security, privacy, availability and price. Relying on a single cloud storage puts limits on the applications, which could be alleviated by another storage solution. Moreover multi-cloud storage architecture has limited impact due to the lack of differentiation between competing cloud solutions. Recently, decentralized storage solutions are gaining attraction as an alternative to cloud storage. In this work, we propose a blockchain-based adaptive middleware for intelligent selection of storage technology for IoT applications and explore the open questions regarding the integration of middleware with IoT applications, storage technologies and blockchain network.
A New Active Contours Image Segmentation Model Driven by Generalized Mean with Outlier Restoration Achievements

In this paper, we propose a robust variational segmentation model capable of overcoming the problem of the negative effects of outliers. The proposed method is based on the combination of the characteristics of the generalized mean with the concept of the active contours. The optimization problem raising in this combination employs a power method technique. We demonstrate the performance of the proposed model on a series of sample images from diverse modalities and show an outperforming proposed model in comparison with the state-of-the-art methods. The proposed method shows better or equivalent performance in terms of accuracy and robustness than the conventional state-of-the-art models. The validation of the efficiency of the proposed two-phase algorithm is further extended to vector-valued images and multi-phase formulation.
Wavelet domain directional binary pattern using majority principle for texture classification

In this paper, we propose a texture descriptor, called Wavelet Domain Directional Binary Pattern using Majority Principle (WDDBPθ) which integrates the discrete wavelet transform and Local Binary Pattern (LBP). The discrete wavelet transform provides the directional information, while LBP describes the texture features predominately. The proposed approach consists of four phases. In the first phase, the image is subjected to single level decomposition; resulting in discrete wavelet coefficients. The wavelet coefficients present at various distances; one, two and three are compared among themselves which results in sign pattern. In the second phase, the difference of wavelet coefficients located at different distances are compared with global difference of wavelet coefficient and results in magnitude pattern. In the third phase, the majority principle is applied for both sign and magnitude pattern resulting in WDDBP_S and WDDBP_M pattern. Finally, the histogram of both WDDBP_S and WDDBP_M is concatenated. The proposed method is also evaluated by involving the wavelet coefficients present at twelve different directions. The K-NN algorithm is applied to classify the input and database images. The experimental results demonstrate that the proposed method outperforms the other existing methods on Brodatz and Outex database with improved classification accuracy.
An Internet of Things-Based System Integrated with Blockchain to Manage Patient Data in the Healthcare Sector

Health is the biggest challenge to a majority of citizens regardless of age group and with the shortage of major components in the healthcare sector, life expectancy tends to reduce due to a lack of facilities. Technology on the other hand is reaching out to meet some of the demands that the healthcare will continuously require in order to deliver their services on time. One such technology is known as the internet of things (IoT). IoT has already proven its great potential towards the healthcare domain. IoT has the ability to allow patients to be monitored from the comfort of their homes through sensors equipped within devices deployed to perform specific tasks. The other technology is none other than the Blockchain which was introduced to deliver its capabilities in the financial ledger. The Blockchain helps to keep track of all the transactions being recorded, which in healthcare sector the need for Blockchain can be transformed to keep a trace of patient records. Since the IoT devices have the capability to monitor vital signs in the human body, it will be of a great achievement to integrate it with the Blockchain to keep an efficient system of healthcare records. The approach of this work is presented based on an Blockchained-IoT system to improve challenges of storing patients’ data captured by wearable IoT devices to help medical caregivers to make more informed decisions based on the efficient healthcare record-keeping. The design science approach will be proposed in our research to run the full experiment of our work.