Reviewerly/exHarmony huggingface dataset:
The Change before Behaviour: Closing the Value‐Action Gap Using a Digital Social Companion

This paper describes an experiment, designed and developed with the ultimate aim of fostering low‐pollution and low‐carbon social innovation. It offers an evidence‐based practical alternative to conventional, technological approaches and narratives of smart cities aimed at sensing air pollution and mitigating the effects of climate change. In this experiment a new voice user interface is designed, developed and tested with input from participants – to explore the potential of a new, more socially minded adaptation to current AI assistant devices in the home and enhance the field of smart technology design. The experiment is developed with a group of participants to demonstrate how design research can raise novel questions and inform disciplines with an interest in behaviour change, environmental pollution and smart homes. This work demonstrates the potential for technologies to increase the degree of participation in reducing pollution in cities and facilitate the articulation of agency in complex environmental matters. The work has value for designers, ethnographers and researchers working in design for behaviour change and participatory design, as well as practitioners and organisations who are interested in enabling low‐pollution lifestyles, developing smart homes and/or emerging AI technologies.
Acquiring and Analyzing Movement Detection through Image Granulation

Detection of moving object is a challenging task for any video surveillance. Generally, a video surveillance consists of three levels of processing - moving object extraction, recognition and tracking - that further process for decision of the corresponding activities. As pre- and post-processing of the video might be necessary to improve the detection of moving objects, we have proposed a method that uses the coordinates of the boundary of an object with a thick white line. This paper basically shows the results through which we specify the initial search condition, the connectivity and how many pixels should be returned and the direction in which to perform the search. Further traces the exterior boundaries of objects.
Quality Challenges in Internet of Things-based Services Implementation: An Exploratory Study

The real value of using Internet of Things-based services (IoTbS) comes from the establishments of services through effective utilization of technologies. However, during the implementation of IoTbS, Government Agencies (GA) are expected to overcome several challenges in dealing with employees who are reluctant to use the technology due to poor security system and overall quality. The primary aim of this research is to discover the quality factors that influence the successful use of IoTbS in Malaysian GA. Using the qualitative approach, the exploration has been made through a systematic literature review and a semi-structured interview with Government IoT Experts. The data have been analysed using Deductive Qualitative Analysis and coded using NVivo12 software. Primary data has discovered 23 quality factors that can be categorised under `Information System Quality' themes. The paper analyses these quality factors in detail and proposes a set of recommendations to overcome the challenges in order to ensure a successful implementation of IoTbS in GA. Accordingly, the paper proposes on how these quality factors could be overcome while making a worthwhile contribution to both theories and practices. In the future, these quality factors can be analysed on its suitability as a set of IoTbS implementation criteria towards the successful use of IoTbS in GA.
Face Recognition Using DELF Feature Descriptors on RGB-D Data

Face recognition is a very challenging and important task in many areas. This sort of algorithms is used in security systems, for person authorization tasks, person reidentification, etc. In face recognition speed and quality are very important, but in this research, we concentrate on quality improvement. In this paper we propose a new solution for face recognition using RGB-D data obtained with Kinect sensor. The proposed solution is based on a new type of feature descriptors – Deep Learning Features (DELF), which showed high classification results on Google landmarks dataset. We compared DELF descriptors with HOG, MSER and SURF descriptors using two classification schemes: error correcting output codes (ECOC) and decision trees. Conducted experiments showed an improvement in classification quality using F1 score metric when face recognition is based on DELF descriptors and ECOC classifier.
Confidence-based voting procedure for combining fuzzy systems and neural networks

Abstract In this study the confidence-based voting of neural net classifier and fuzzy logic based classifiers is proposed. In this method, for the cases when the fuzzy system is confident enough in its decision, i.e. when the membership value is large enough, fuzzy system makes the decision, otherwise, the neural net is applied. This allows classifying most of the objects by explainable interpretable fuzzy system, while using the more accurate neural network for the most difficult cases. The experiments are performed on a set of test datasets, and two problems of identifying the emotional state of a person using the data collected by non-contact vital Doppler sensors. The results show that this setup allows not only improving the classification quality, but also allows to explain the classification process the explanation of the classifier functioning.
Designing Supervised Learning-Based Incident Management Model : Case Study: Broadband Network Service Provider

Incident management has play an important role in the business processes of service provider. Well implemented incident management can increase the availability of services and as well as user satisfaction level. However, incident management is a dominant process that consumes resources, both time and cost. Artificial intelligence-based technologies can be utilized to improve the efficiency of the incident management process. Thus, organizations can receive added value from more efficient process as well as more accurate information. This research proposed machine learning machine learning model for incident categorization and incident duration prediction that could be adopted into business process of incident handling.
Push-Based Network-efficient Hadoop YARN Scheduling Mechanism for In-Memory Computing

In the big data era, data-intensive cluster computing systems like Hadoop, have gained much popularity, and YARN, the second generation of Hadoop becomes the general resource manager in the Hadoop ecosystem. In the distributed computing scenarios, data locality (scheduling tasks on where the data resides) is essential to the performance since higher data locality brings lower network transmission cost and higher throughput. However, we find that the native YARN scheduling mechanism has little data locality and the delay scheduling strategy leads to the long-tail effect while achieving data locality for in-memory computing scenarios. Therefore, in this paper we propose the push-based YARN scheduling mechanism for the in-memory computing environment. First, we classify the Resource Requests into various categories. Then, we prune the non-local Resource Requests to achieve fast datalocality in-memory computation. Finally, we push the left longtail Resource Requests to the data-locality nodes to avoid the long-tail effect. The experimental results demonstrate that the proposed scheduling mechanism achieves nearly 100% datalocality percentage comparing to the native YARN scheduling mechanism that only achieves 10% 20% data-locality percentage. Under the identical data-locality percentage, the proposed push based scheduling mechanism promotes nearly 20% throughput and reduces nearly 10% application running time comparing to the existing delay scheduling mechanism used in YARN.
Fuzzy Association Rule Mining Algorithm Based on Load Classifier

In this paper, the fuzzy association algorithm based on Load Classifier is proposed to study the fuzzy association rules of numerical data flow. A method of dynamic partitioning of data blocks by load classifier for data stream is proposed, and the membership function of design optimization is proposed. The FP-Growth algorithm is used to realize the parallelization processing of fuzzy association rules. First, based on the load balancing classifier, variable window is proposed to divide the original data stream. Second, the continuous data preprocessing is performed and is converted into fuzzy interval data by the improved membership function. Finally through simulation experiments of the Load Classifier, compared with the four algorithms, the data processing time is similar after convergence, and the data processing time of SDBA (Spark Dynamic Block Adjustment Spark) is lower than 25 ms.
Reasoning Based Workload Performance Prediction in Cloud Data Centers

Cloud computing provides utility-based and scalable services to end-users. In the past decade, the demands for resource management in cloud computing have increased substantially which lead to certain challenges such as optimal resource utilization, power consumption, and service level agreement violations. Workload performance prediction serves as an assistance to address these issues. In this paper, we propose a prediction model based on clustered Case-Based Reasoning (CBR). The proposed model determines the performance metrics for workload prior to the co-operation of autonomic computing characteristics. Thus, CBR provides optimal scheduling of resources and workload monitoring for cloud data centers. In order to validate the proposed CBR-based prediction model, we perform a series of experiments and evaluate the effectiveness in terms of precision, recall, f-measure, and mean square error rate. We generate the cases for CBR using traces from the Google cluster data center. Moreover, we also validate our proposed prediction model against Support Vector Machine (SVM) prediction scheme. Experimental results show that the proposed CBR outperforms the SVM-based approach and yields 10% improvement in terms of precision.
PACT - Programming Assistant ChaTbot

Programmers face situations where they have to rely on messy documentation, other developers and online search for basic programming commands and queries when they encounter any new programming environment. This leads to the waste of time of developers and decreases productivity. In this paper, we present, “PACT”, a chat bot which assists the programmers with basic programming queries that they face when they are new to a programming environment. We use Neural Machine Translation architecture to generate coherent, non-rule based responses to a programmer's query. The data that is fed to the neural machine translation model is collected from websites like StackOverflow, technical sub-reddits and technical StackExchanges.
Unconventional Content Strategy and Hierarchical Taxonomy for Modern Web and Mobile App Design

User Experience Design has evolved in the past 10 years since most services and companies have come online to serve their customers/users. User Experience requires many years of experience, practice and overall experimentation. This research paper analysis the unconventional practices being used in modern day web and mobile app designs. Based on my study and practice, This paper will be highlighting three important User Experience concepts including Hierarchical Taxonomy, Content Strategy and Ambiguity, which are all crucial for developing and designing applications for millennials. Also, we will be writing about how UX principles have changed and evolved over the years to adjust to modern design trends and advancement in technology. We have studied and focused on designs for the latest gadgets including larger phones with bezel-less displays.
A Neural Network Based Approach for Operating System

The operating system is the central element of a computing device. It is the base level on which all the applications run. It allows the user to interact with the hardware with the help of the user interface. Creating a more efficient and capable software means less load on the hardware. Evolving nature of the neural network will help the operating system to learn about the user and will help in creating a better experience for the user. In this paper, we propose the integration of the neural network system at the kernel level of the operating system. Further, we show that the proposed scheme is more efficient and advanced than the current conventional system.
Individual, Social and Personnel Factors Influencing Modern Code Review Process

Modern (MCR) is a multistage process where developers evaluate source code written by another developer to enhance the software quality. The process is deeply dependent on interactions among the workforce (reviewer, and author) involved in process activities. Interactions among the MCR workforce are roots of various individual, social and personnel factors influencing the MCR process. Multiple research has been performed based on data analysis collected from review tools, surveys, and interviews at a single company concerning factors affecting MCR. However, MCR research is lacking systematized study concerning individual, social and personnel factors influencing the MCR process. Therefore, this study performs a Systematic Literature Review (SLR) to identify individual, social and personnel factors influencing the MCR process. The study findings highlight individual factors (individual skills, characteristics, emotions, etc.), social factors (trust, relationship, interaction, impression, etc.) and personnel factors (team, team interaction, and reviewer response) that influence the MCR process. Study findings have implications for software engineering researchers performing research in the area and MCR team members to extend the research and perform MCR activities effectively by considering and overcoming the negative impact of identified factors.
Target Detection and Tracking Based on Multi-feature Fusion

Aiming at the problem of tracking target loss in complex environment such as pedestrian illumination change, background and target color close and target occlusion, this paper proposes a target detection and tracking method which combines multi-feature Camshift algorithm with Kalman filter. Firstly, in order to solve the problem that pedestrian targets need to be selected manually in the initial frame, HOG features and SVM classifiers are used to detect pedestrian targets in the initial stage. Secondly, Euclidean distance is used to select pedestrians nearest to the central pixel coordinates of the image as tracking targets. At the same time, pedestrians are divided into five parts to extract the color features of clothes and HOG features respectively, and fuse them according to the weight to build a more robust target model. Finally, the occlusion threshold is set by using the similarity between the current frame and the target model of the previous frame, and the switch between Camshift algorithm and Kalman filter algorithm is realized to solve the occlusion problem and ensure the robustness of the algorithm. Experiments on OTB2013 test sets show that the proposed method improves the robustness of the algorithm and guarantees the real-time performance of the algorithm.
Blockchain Technology in Healthcare Domain: Applications and Challenges

Blockchain is a technology which is proposed to address various problems in different domains as like in the banking sector, supply chain. Blockchain is distributed public ledger that can hold permanent records in a secured way. Blockchain ensures that transactions can never be modified. Blockchain also provide data transparency, integrity, security and interoperability. To address the need for patients to manage and control their electronic health records, blockchain technology could be successfully applied in the healthcare sector. Using blockchain decentralized storage of patient information is possible. Health records which are up to date will be accessed by authenticated user only. It is possible for patient to share his records with other doctors for diagnosis of diseases for example chronological disease, thus blockchain provides personal data transaction in health care sector using blockchain. In this paper we reviewed different applications of blockchain in healthcare system and also challenges for deployment of this technology in healthcare industry.
Ensuring security of a telemedicine project in compliance with GDPR

Digital processing of medical image data is a very important factor in improving patient care. However, the use of these technologies also brings the need to ensure safety and reliability of the implementation of the technical background. The highest possible protection against a possible intruder should be employed, which might require monitoring of the data traffic. Compliance with legislative requirements for working with such sensitive data is an issue.
Multi-layer Weight-Aware Bilinear Pooling for Fine-Grained Image Classification

Fine-grained images have similar global structure but exhibit variant local appearance. Bilinear pooling models have been proven to be effective in modeling different semantic parts and capturing the effective feature learning for fine-grained image classification. However, the bilinear models do not consider that convolutional neural networks (CNNs) may lose important semantic information during forward propagation, and feature interactions of different convolutional layers enhance feature learning which improves classification performance. Therefore, we propose a multi-layer weight-aware bilinear pooling method to model cross-layer object parts feature interaction as the feature representation, and different weights are assigned to each convolutional layer to adaptively adjust the outputs of the convolutional layers to highlight more discriminative features. The proposed method results in great performance improvement compared with previous state-of-the-art approaches. We demonstrate the effectiveness of our method on the CUB-200-2011 and FGVC-Aircraft datasets.
Analysis of Joints for Tracking Fitness and Monitoring Progress in Physiotherapy

This paper proposes a framework to track the progress in angles and range of motion of joints in physiotherapy. Using a sensor, the 3D skeletal information of a subject undergoing the therapy is extracted. Using time-frequency features of the skeletal profile based on the approximation coefficients of the Discrete Wavelet Transform (DWT), the exercise the subject is engaged in is identified by a recurrent neural network in conjunction with long-and-short term memory. Subsequently, each instance of the exercise is segmented. The best of these is used as a reference and various instances of the exercise are compared against the reference for repeatability, fidelity, etc., to study muscle and/ or joint fatigue and progress. Finally, Joint performance analysis is carried out using metrics evaluated at the end of each engaging session. Experimental results demonstrate that with such progressive analysis, it is possible to quantify the performance through the course of the regimen.
Strategic decision-making in the police and contemporary models of CI-intelligence collection

The beginning of the 21st century was marked by major changes in the sphere of serious crime. New technologies, and primarily the development of information systems, but also the expansion of illegal manufacture of new types of artificial narcotic drugs, globalization and abolition of state borders, occurrence of a large number of war conflicts throughout the planet, all have lead to the occurrence of new forms of crime and a faster growth of the already existing ones, primarily organized and transnational crime. Organized criminal groups increasingly use the proceeds obtained by human trafficking and from smuggling migrants from the territories in Asia and Africa into Europe for buying arms and explosives intended for carrying out terrorist attacks in the furtherance of their goals. The terrorist attacks on the World Trade Center and the Pentagon in the US on September 11, 2001, in Bali on April 12, 2002, and the bombing attack in London on 7 July 2005 influenced strategic decision-making in security structures by raising awareness of uncertainty in our understanding of security, from crime in the local area to national security. In order to observe the basic principles of efficiency, effectiveness and economy, it is necessary to establish a different way of operating security services. Police should be able to foresee criminal trends and actions by organized criminal groups both in the short and in the long run. Chiefs of police should think strategically and be prudent in the allotment of resources. The traditional reactive ways of work, directed at tactic investigations focused on resolving individual criminal offences, achievement of momentary operative results and the attitude of police chiefs that "next year won't be my concern, but someone else's" have to be obliterated. The paper presents some of the contemporary models of policing based on strategic thinking and decision-making. Research results show that police work should inevitably be aimed at crime prevention. Yet the objective has to be realistic, as it is impossible to eradicate crime, but it is possible to contain it and keep it within certain socially acceptable limits. Furthermore, it is vital to incorporate the subject of Crime investigation strategy into police education. It would teach police officers how to think strategically, in a proactive way, in order to combat crime. The police should focus their activities on the monitoring of recidivists in order to prevent the perpetration of new offences and thus reduce the overall crime rate. It should be borne in mind that preventing a crime is a more valuable result of police work than apprehending the perpetrator of a committed crime. The citizen who has not been victimized will be more satisfied than the damaged party who receives compensation in a criminal proceeding.
Fast Base Extension using Single Redundant Modulus in a Residue Number System

The Residue Number Systems (RNS) has been around for a long time. Even though RNS proposes carry free addition and multiplication, there has been hesitancy in utilizing RNS to build efficient hardware. This is in small part due to the inability of RNS to perform division and scaling natively. In this paper we propose a new method which would allow us to scale any number in an RNS with respect to another modulus. This paper proposes a new technique for the calculation of base extension in Residue Number System using a single redundant modulus. The method is based upon the usage of the well-known Chinese Remainder Theorem for base extension. The base extension is achieved in modest time as the operands at all levels are kept as reminders of smaller numbers, therefore, allowing us to attain superior hardware implementation over the naive method.