Reviewerly/exHarmony huggingface dataset:
UK reveals new platform for infrastructure data analysis and simulation modelling

A prototype of the UK’s new Data and Analytics Facility for National Infrastructure was launched in June 2019 prior to being fully operational in 2022. Jim Hall of the University of Oxford explains its many benefits to civil engineers.
Technical and Managerial Difficulties in Postmortem Analysis in Software Projects

Software is successfully applied in a wide variety of areas. However, software projects have suffered from poor reputation by repeatedly bursting deadlines, costs or failing to fully meet user requirements. Postmortem Analysis is an activity to analyze what happened in projects in search of understanding the failures occurred and the achieved successes. Despite bringing interesting data for improving future projects, Postmortem Analysis is often neglected in organizations. This article seeks to identify and analyze the technical and managerial difficulties that exist in its accomplishment through bibliographical research. As a result, it is possible to conclude that the main difficulties for realizing postmortem activities are the shortage of time, lack of management support, conflicts between stakeholders, difficulty in extracting and collecting data, lack of agreement regarding evaluation criteria, lack of standards for achievement, and lack of useful or efficient historical data.
Fuzzy TF-IDF Weighting in Synonym for Diabetes Question and Answers

Currently, the synonyms are a problem to retrieved answer from question answering systems. A fuzzy based similarity is one method that many researchers used to solve this problem. This paper applied the fuzzy method with TF-IDF weighting in considering the alphabet of words in order to analysis a similarity between words. Our corpus consists of five hundred answers collected from reliable medical resources. Several fuzzy conditions were investigated to find out the best condition for answering the question. To evaluate our proposed method, thirty frequently asked questions are tested and compared to experts answers. The results showed that the acceptable answers were discovered on 80% words similarity above (fuzzy degree is greater than 0.8) with 80.09% or more of precision.
ECC Based Remote Mutual Authentication Scheme for Resource Constrained Client in Cloud

Cloud computing is a state-of-the-art technology using which a resource starved client can access various services from a remote cloud server. Accessing such remote services requires that the client and server authenticate each other and come to agree on a common session key in secure manner. Most of the recent mutual authentication and key agreement protocols use two and three factor smart card and biometric based techniques. However, due to the consequent increase in cost of resources and added operational complexity, one factor authentication schemes are still popular. In this paper a lightweight Elliptic Curve Cryptography based one factor three way mutual authentication and key negotiation scheme between a lightweight client and server is proposed with proven safety.
EA-PLRU

In SGX-based systems, cache lines belonging to enclaves must be encrypted when they are evicted from the last-level cache, and decrypted on a cache miss before they are brought into the cache from memory. Because encryption and decryption introduce overheads in terms of performance, memory pressure and power consumption, it is important to reduce the frequency of LLC misses and replacements of enclave lines. We consider a system where enclave and non-enclave applications co-exist in the system and share the last-level cache. To decrease the frequency of encrypt/decrypt operations, we introduce a new cache replacement policy that slightly favors enclave lines over non-enclave lines. Specifically, we modify the last level of pseudo-LRU replacement logic, so that it favors an enclave line over a non-enclave line regardless of how recently each line has been accessed. We also add a probabilistic component to this new policy to balance performance and make replacement policy non-deterministic and therefore resilient to side-channel attacks that exploit predictable patterns of cache line replacement.
Automatic Conformal Anti-radial Ultrasound Scanning for Whole Breast Screening

Anti-radial ultrasound scanning is one of the main scanning approaches used in ultrasound breast screening. It can be used for cross-sectional imaging of mammary ductal/lobular tissue and provide information about suspicious tissue. It has certain advantages compared to linear scanning, but automatic anti-radial scanning is not yet available. Our goal is to propose an ultrasound scanning system for whole breast anti-radial scanning. We previously developed an automatic ultrasound scanning system for whole breast screening involving linear scanning. The present study builds on our previous work by incorporating (1) surface-reconstruction algorithms, (2) a rotatable holder design, and (3) a scan-path-smoothing algorithm to achieve conformal anti-radial scanning. An improvement of approximately 40% in the normal-vector estimation is obtained with our new method, and the scan stability is improved by the scan-path-smoothing algorithm. 3D volume data of each scan are available. We have successfully developed an automatic ultrasound scanning system for anti-radial breast scanning. This type of system, which has not been reported previously in the literature, can be an effective tool for fully automatic ultrasound breast screening.
Quantum Fisher Information and Entanglement of Moving Two Two-Level Atoms under the Influence of Environmental Effects

We have investigated numerically the dynamics of quantum Fisher information (QFI) and quantum entanglement (QE) of a two moving two-level atomic systems interacting with a coherent and thermal field in the presence of intrinsic decoherence (ID) and Kerr (non-linear medium) and Stark effects. The state of the entire system interacting with coherent and thermal fields is evaluated numerically under the influence of ID and Kerr (nonlinear) and Stark effects. QFI and von Neumann entropy (VNE) decrease in the presence of ID when the atomic motion is neglected. QFI and QE show an opposite response during its time evolution in the presence of a thermal environment. QFI is found to be more susceptible to ID as compared to QE in the presence of a thermal environment. The decay of QE is further damped at greater time-scales, which confirms the fact that ID heavily influences the system’s dynamics in a thermal environment. However, a periodic behavior of entanglement is observed due to atomic motion, which becomes modest under environmental effects. It is found that a non-linear Kerr medium has a prominent effect on the VNE but not on the QFI. Furthermore, it has been observed that QFI and QE decay soon under the influence of the Stark effect in the absence of atomic motion. The periodic response of QFI and VNE is observed for both the non-linear Kerr medium and the Stark effect in the presence of atomic motion. It is observed that the Stark, Kerr, ID, and thermal environment have significant effects during the time evolution of the quantum system.
A Brief Analysis of the Literature Network Resource Mining and Data Development

The development of today’s society cannot be separated from the information technology, in which the literary network data resources occupy an important position. Especially with the continuous development of the network, the literature network resources are also growing rapidly, so the problem of the network information overload is becoming increasingly prominent, and the network information mining is also developed on this basis. Then this paper mainly summarizes the data mining and the application of the literature network information resources.
INTRA CORPORATE MODEL OF DEVELOPING COMPETENCE OF THE UNIVERSITY TEACHER IN THE FIELD OF DESIGNGING ELECTRONIC EDUCATIONAL RESOURCES

The article discusses the theoretical and practical aspects of the development of teacher competence in designing electronic educational resources (EER), due to the need to use e-learning and distance learning technologies in modern educational institutions. The article reviews scientific and pedagogical researches on the subject and analyzes the structure of the teacher ICT competence. The authors summarize the findings of researchers, define the need to improve the skills of teachers in the field of ICT and develop the models for teachers’ upgrading. They generate the structure of ICT competence of a teacher in the field of developing EER. The modern classifications of EER are considered, the stages in the creating EER are defined. The relationship between the development of teacher competence in designing EER and the emergence of new learning models, such as blended learning and e-learning, has been revealed. An intra corporate model of developing competence of university teacher in the field of designing EER, based on the goals and aims of a particular educational institution, is presented. Stages of model’s implementation are defined. The article describes the experience of the development and testing of a set of activities such as organization of advanced training courses registering e-learning resources in the university electronic resources fund, conducting an EER competition, conducting training seminars and individual counseling aimed at developing teacher competence in the field of EER design.
Interconnected living in a quarter for persons with dementia

Objective In this work, we propose a new care concept for dementia patients in their own apartments: interconnected living in a quarter. We describe a technical setup that is comprised of a safety system and an activity detection system. The latter detects, processes and illustrates activities of daily living to help the quarter managers to provide appropriate interventions for persons with dementia in the quarter.Participants The nine-month field study reported in this work was conducted in two quarters with eight participants.Methods We evaluated different possibilities to determine activity indicators with the aim of providing information that enables the quarter managers to offer exactly the level of support needed by each individual patient. To evaluate the usefulness and the perception of the technical infrastructure, qualitative interviews with the dementia patients and the quarter managers were conducted.Results The results indicate that the interconnected living concept helps to increase the safety of the dementia patients. Additionally, several activity indicators that help the quarter managers to offer the appropriate level of support to the dementia patients have been identified.Conclusion The presented concept, which has been evaluated in a real-world-setting, constitutes a new holistic and cross-disciplinary dementia care approach.
Secure Framework for Ambient Assisted Living System

The development in the field of miniature sensors and wireless communication enables the successful deployment of Internet of Things (IoT) in the healthcare sector. The healthcare data generated by the medical sensors is very sensitive and enough to qualify as an instance of big data. In this paper, a secure framework is designed to assist the diabetic patients. The sensors’ data of each patient is used to generate context aware correlation rules by using map-reduce apriori algorithm. From these rules another labeled dataset is created to build a classifier for predicting the present state of the patient. After the successful deployment of this classifier on the service provider side, secure communication has been provided between the patient and the service provider using Key-Policy Attribute Based Encryption. Hence, providing an IoT based secure ambient assisted living system for diabetic patient may be helpful to the healthcare sector.
Enabling Proof-of-Work for Low-End IoT Devices

Cyber-physical housing technology is going through a revolution, started the early 2000s and expected to continue in the years to come. In fact, according to market research reports, connected devices for smart homes will soon have the largest market share of all IoT devices. In contrast with siloed devices and applications, which until recently prevailed even in the most sophisticated dwellings, nowadays it is already possible to glimpse a complex blend of technologies seamlessly integrated to promote the residents' comfort. However, several challenges lie ahead before the technology can fully support such a visionary scenario. One of them is security, especially in scenarios encompassing low-end embedded devices. This paper describes a mechanism allowing limited resource embedded appliances to participate in a network of IoT devices secured by a distributed ledger technology named IOTA. The paper describes the devised proof-of-concept implementation and provides insight on preliminary performance results showing its feasibility.
Comparative User Experience Analysis of Pervasive Wearable Technology

Although the growing market of wearable devices primarily consists of smartwatches, fitness bands, and connected gadgets, its long tail includes a variety of diverse technologies based on novel types of input and interaction paradigms, such as gaze, brain signals, and gestures. As the offer of innovative wearable devices will increase, users will be presented with more sophisticated alternatives: among the several factors that influence product adoption, perceived user experience has a significant role. In this paper, we focus on human factors dynamics involved in the pre- and post-adoption phase, that is, before and after customers buy or use products. Specifically, objective of our research is to evaluate aspects that influence the perceived value of particularly innovative products and lead to purchasing them. To this end, we present the results of a pilot study that compared performance expectancy, effort expectancy, social influence, and facilitating conditions, in the pre- and post-adoption stages of three types of wearable technology, i.e., brain-computer interface, gesture controllers, and eye-tracking systems.
Pair-Comparing Based Convolutional Neural Network for Blind Image Quality Assessment

The introduction of convolutional neural network (CNN) in no-reference image quality assessment (NR-IQA) gains great success in improving its prediction accuracy, and the performance of CNN relies on the magnitude of training samples. However, many widely-used existing image databases cannot provide adequate samples for CNN training. In this paper, we propose a pair-comparing based convolutional neural network (PC-CNN) for blind image quality assessment. By taking reference images into consideration, we generate more training samples of patch pairs by different combinations of distorted images and reference image. We build a new CNN network which has two inputs for patch pairs and two outputs predicting the scores of patches. We conduct extensive experiments to evaluate the performance of our proposed PC-CNN, and the results show that it outperforms many state-of-the-art methods.
Evolutionary and Adaptive Elderly Care Ecosystem

Personalized care services for each individual are typically required in the aging process. Considering this domain, a collaborative elderly care ecosystem (ECE) is proposed to support the provision of composite services that may combine contributions from multiple service providers. A personalized solution is built through of services composition, tailored to the individual customer (senior), respecting her/his requirements, preferences, lifestyle, and constraints. An additional issue the ecosystem must deal with is the problem of evolution, as individual's care needs are not static over time. Consequently, the care services need to evolve accordingly to keep the elderly's requirements satisfied. This process of services' adaptation is challenging since many services can be dependent on each other, and there are various constraints that need to be observed before adaptating and enacting new services. In this paper, we exploit socio-technical aspects of service adaptation in the context of elderly care ecosystems. Starting with a service personalization method previously proposed (SCoPE method), we presented an adaptive and evolutionary system (SEvol) based on the MAPE-K methodology. The SEvol method considers customer's inputs and suggests evolution plans. In the end, a workflow diagram is presented considering the main processes of ECE demonstrating the roles of the ECE environments and the main stakeholders, according to three stages: preparation, execution and monitoring phase.
Transforming Meta-design from Paradigm to Working Theory

In a traditional product lifecycle, the design of the product typically predates its use. Meta-design is a paradigm rooted in end-user development research that aims to address this issue, by proposing a shift from the traditional design-time vs. use-time separation, towards a model where designers meta-design open ended systems that allow for future changes by end-users. Meta-design has existed as a paradigm for almost 20 years, but is still not widely adopted in practice. The application and study of meta-design has been mostly limited to retrospective analyses, rendering the paradigm descriptive instead of prescriptive. The challenge remains in understanding the factors impacting the adoption of meta-design, and in predicting and explaining the factors that determine successful meta-designed environments. This paper proposes an approach towards building this understanding, in order to connect conceptual notions related to meta-design with knowledge obtained from studying its implementation.
Design of the data logger with IEC standard for PV system

This research was to study the design of a data logger with IEC standard for PV system using Arduino microcontroller. The Arduino microcontroller received a signal from analog sensor. The main processor board of the Arduino microcontroller was evaluated and display to 20 x 4 characters (4 rows and 20 characters per row) on LCD monitor. Then, the data were recorded in a memory card. Furthermore, the data logger was tested with the pyranometer, fixed PV module and tracking PV module. Those of the values were compared with the data from a standard data logger (Wisco DL 2100) and calculated the values of percentage of an error. It was observed that the maximum values of the percentage of the error from the recorded data was 25.60. The minimum values of the percentage of the error from the recorded data was 0.
DMC: A Differential Marketplace for Cloud Resources

The currently trending paradigms of edge and fog computing attempt to provide services close to the end user, to meet the demands of latency-sensitive applications and to limit bandwidth consumption in the network core. One open issue is the pricing of edge and fog resources. Current pricing schemes are usually oligopolistic and not fair. In this work, we propose DMC, a marketplace that can dynamically determine the fair price for arbitrary resource types and instances based on supply and demand existing at that period. Unlike the state-of-the-art solutions, DMC performs integral allocation of resources and thereby avoids the unbounded integrality gap. Additionally, DMC provides differential pricing among instances to allow varying prices based on the perceived value of a resource. We evaluate DMC with both heavy and non-heavy tailed distributions to reflect diverse buying interests and the number of resources sold to demonstrate the feasibility of our solution for several realistic scenarios. We observe that (i) DMC arrives at market-clearing prices; (ii) DMC generates 10x to 100x more profit than state-of-the-art solutions, while still maximizing the Nash Social Welfare to achieve prices that are fair to both buyers and the resource providers; and (iii) the computation time for DMC does not exceed 10 seconds even in the case of 500 resource types with 500 buyers each, making it applicable for real-time use cases.
A Survey on the Skills, Activities and Role of the Software Architect in Brazil

Although the skills and knowledge of software architects have already been the subject of some studies in recent years, researchers and practitioners still have not come to a clear consensus about the activities that a software architect is often responsible in practice in order to be considered successful. In recent years, due to occurrence of successive changes and evolution of new technologies, the roles of the architect and even practices related to software architecture have been continuously changed in the software development life cycle. The software architect is expected to possess a diversity of skills. In addition to technical knowledge, domain knowledge and communication skills must be considered. However, there are many job offerings for this position which have in their description skills and roles totally different from the ones already known and considered essential by academic and industry studies. In order to better understand what software architects actually do in their daily activities in practice, and how this resembles or distances themselves from the skills, roles and knowledge cited in the literature as essential, in this article we have conducted a large-scale survey with 536 professionals who currently work or have worked at some period in their careers as software architects. Among the results, it is clear that the roles, responsibilities, activities and tasks performed by software/system architects are still largely unknown and diffuse in organizations, as important tasks to be performed by software architects are still not common sense in industry.
Towards Distributed Multi-model Learning on Apache Spark for Model-Based Recommender

Model-based approaches for Content-based Filtering (CBF) recommendation have the potential of generating representative users models owing to their ability to learn from users actions. However, the need for training an individual model for each user leads to a scalability issue and brings a high computational cost that contributes to the limited adaptation of model-based approaches as efficient CBF recommenders. This is particularly relevant for production systems where the recommender is expected to serve a large number of users. In this work, we address the efficiency issue of model-based CBF recommender systems and present a new approach for distributed multi-model learning based on Apache Spark. We use Ranking SVM as the underlying recommendation algorithm and present a distributed implementation that allows efficient training of multiple models in parallel using a collection of machines. We demonstrate the efficiency of our approach on a real-world dataset from citeulike and show that our approach can reduce the cost of multi-model learning without affecting the prediction accuracy.
Some Thoughts on Forward Induction in Multi-Agent-Path Finding Under Destination Uncertainty

While the notion of implicit coordination helps to design frameworks in which agents can cooperatively act with only minimal communication, it so far lacks exploiting observations made while executing a plan. In this note, we have a look at what can be done in order to overcome this shortcoming, at least in a specialized setting.
Development of a Repository of Virtual 3D Conversational Gestures and Expressions

This paper outlines a novel framework that has been designed to create a repository of “gestures” for embodied conversational agents. By utilizing it, the virtual agents can sculpt conversational expressions incorporating both verbal and non-verbal cues. The 3D representations of gestures are captured in EVA Corpus, and then stored as a repository of motor skills in the form of expressively tunable templates.