Assist Application Developers Research Articles

Knowledge Mapping of Human Activity Recognition Techniques for Assistive Living

Purpose: Human Activity Recognition (HAR) is a subject of research that identifies an individual’s activities for assistive living. The proliferation of ICT and sensor technology prompted HAR to flourish beyond unfathomable levels, having immense human-centric applications. The development of accurate HAR systems involves complex statistical and computational tasks from signal acquisition to activity classification. This research aims to conduct a systematic review of recent techniques proposed for each stage of HAR application development. Methodology: The review is conducted following Kitchenham principles, using Scopus and Web of Science databases. Firstly, research questions were formulated, followed by the search strategy definition. Based on assessment criteria, 193 papers are shortlisted and thoroughly analyzed to extract research- related information. Results: The techniques identified in 193 articles are comprehensively mapped from four aspects: data acquisition, data preprocessing and feature engineering, learning algorithm, and evaluation. Each technique is examined for its strengths and limitations to assist application developers in selecting the best one for their needs. The prevailing challenges and upcoming research opportunities are thoroughly explored. Conclusion: The ever-expanding literature in the field necessitated an update to the status of HAR literature. Compared to other reviews that focused on specific methods, fields of application, and datatypes, to the best of our understanding, this is the first evaluation of its kind that provides a broader mapping of HAR approaches. The findings of this analysis will provide researchers and newcomers in the field an up-to-date and holistic view of the complete body of work in this area.

Smart Contract Security Vulnerabilities

A smart contract is a concept of computer protocols that helps to facilitate blockchain technology. This blockchain-based smart contract is a public ledger of all participating transactions. It is considered a self-executable application and contains predetermined rules. It also operates by decentralizing networks that are shared between all parties, and this execution of contracts between parties could be securely done without a middleman or a third party. With blockchain technology, developers could provide an efficient framework and ensure security issues. While the new blockchain has successfully been developed to prevent the problems of fraud and hacking, there is still a considerable risk concerning security and confidentiality. Therefore, we should not underestimate this matter. This study aims to review the potential risks that may take place on blockchain-based smart contracts. In addition, the options that may assist application developers in order to provide viable guidance, and to avoiding these security vulnerabilities.

Decision support for GPU acceleration by predicting energy savings and programming effort

As the number of heterogeneous embedded systems used in IoT applications increases, there is a lack of software tools to assist developers to meet the challenge of reducing energy consumption. Indeed, there are only few performance prediction tools for heterogeneous systems in the literature and they typically focus on the prediction of speedup by acceleration. In this work, we propose a methodology for analyzing CPU applications in order to estimate the potential Energy gains by offloading a piece of code on an embedded GPU. The proposed methodology provides several features beyond the state of the art of existing predictors, including the combination of static analysis and dynamic instrumentation approaches and the prediction of the programming effort of developing the CUDA kernel of a CPU code, using advanced metrics. The methodology is supported by a tool-flow and it is demonstrated and evaluated on modern heterogeneous embedded systems (Nvidia), where shows classification accuracy above 75%. The results show that the proposed methodology can assist application developers in the early design choice of investing effort to acceleration considering the expected Energy Savings and the Effort required to develop acceleration-specific code.

Training Machines to Identify Species using GBIF-mediated Datasets

Advances in machine vision technology are rapidly enabling new and innovative uses within the field of biodiversity. Computers are now able to use images to identify tens of thousands of species across a wide range of taxonomic groups in real time, notably demonstrated by iNaturalist.org, which suggests species IDs to users (https://www.inaturalist.org/pages/computer_vision_demo) as they create observation records. Soon it will be commonplace to detect species in video feeds or use the camera in a mobile device to search for species-related content on the Internet. The Global Biodiversity Information Facility (GBIF) has an important role to play in advancing and improving this technology, whether in terms of data, collaboration across teams, or citation practice. But in the short term, the most important role may relate to initiating a cultural shift in accepted practices for the use of GBIF-mediated data for training of artificial intelligence (AI). “Training datasets” play a critical role in achieving species recognition capability in any machine vision system. These datasets compile representative images containing the explicit, verifiable identifications of the species they include. High-powered computers run algorithms on these training datasets, analysing the imagery and building complex models that characterize defining features for each species or taxonomic group. Researchers can, in turn, apply the resulting models to new images, determining what species or group they likely contain. Current research in machine vision is exploring (a) the use of location and date information to further improve model results, (b) identification methods beyond species-level into attribute, character, trait, or part-level ID, with an eye toward human interpretability, and (c) expertise modeling for improved determination of “research grade” images and metadata. The GBIF community has amassed one of the largest datasets of labelled species images available on the internet: more than 33 million species occurrence records in GBIF.org have one or more images (https://www.gbif.org/occurrence/gallery). Machine vision models, when integrated into the data collection tools in use across the GBIF network, can improve the user experience. For example, in citizen science applications like iNaturalist, automated species suggestion helps even novice users contribute occurrence records to GBIF. Perhaps most importantly, GBIF has implemented uniform (and open) data licensing, established guidelines on citation and provided consistent methods for tracking data use through the Digital Object Identifiers (DOI) citation chain. GBIF would like to build on the lessons learned in these activities while striving to assist with this technology research and increase its power and availability. We envisage an approach as follows: To assist in developing and refining machine vision models, GBIF plans to provide training datasets, taking effort to ensure license and citation practice are respected. The training datasets will be issued with a DOI, and the contributing datasets will be linked through the DOI citation graph. To assist application developers, Google and Visipedia plan to build and publish openly-licensed models and tutorials for how to adapt them for localized use. Together we will strive to ensure that data is being used responsibly and transparently, to close the gap between machine vision scientists, application developers, and users and to share taxonomic trees capturing the taxon rank to which machine vision models can identify with confidence based on an image’s visual characteristics. To assist in developing and refining machine vision models, GBIF plans to provide training datasets, taking effort to ensure license and citation practice are respected. The training datasets will be issued with a DOI, and the contributing datasets will be linked through the DOI citation graph. To assist application developers, Google and Visipedia plan to build and publish openly-licensed models and tutorials for how to adapt them for localized use. Together we will strive to ensure that data is being used responsibly and transparently, to close the gap between machine vision scientists, application developers, and users and to share taxonomic trees capturing the taxon rank to which machine vision models can identify with confidence based on an image’s visual characteristics.

Perancangan Knowledge Management Perawatan Dan Perbaikan Mesin Pompa Sentrifugal Menggunakan Metode Case Based Reasoning

ABSTRACTThe utilization and existence of centrifugal machine pumps in the industrial world is very important to assist the production process, at least almost 75% of industries use this type of machine pump. As an operational tool in the industry, centrifugal pumps must be able to be used and maintained properly because the damage to the pumping machine causes the operational or production process to be not maximal even can lead to cessation of the production process, and the impact on financial losses are not small. This study aims to create a knowledge management design to assist in the manufacture of software so that ultimately can facilitate the users (technicians) centrifugal pump machine do maintenance and repair the machine. To achieve the purpose of the research the researcher uses the stages of research consisting of three stages, namely: Phase 1. Preparation and Identification of knowledge; Phase 2. Analysis and Design of Knowledge Management and Phase 3. Design of Knowledge Management Prototype. Meanwhile, to manage the knowledge and explicit knowledge of the researcher using Case-Based Reasoning (CBR) method by using Nearest Neighbor Retrieval algorithm to find the best solution in maintaining and repairing centrifugal pump machine by calculating the proximity between new technical constraints and the old technical obstacle already in the database. This study resulted in a knowledge management design consisting of functional requirements and flow and software algorithms to assist application developers in the creation of knowledge management software interfaces to maintain and repair centrifugal pump machines.Keywords: Centrifugal Pump Machine, Knowledge Management, Case-Based Reasoning

Malware Threats and Detection for Industrial Mobile-IoT Networks

Industrial IoT networks deploy heterogeneous IoT devices to meet a wide range of user requirements. These devices are usually pooled from private or public IoT cloud providers. A significant number of IoT cloud providers integrate smartphones to overcome the latency of IoT devices and low computational power problems. However, the integration of mobile devices with industrial IoT networks exposes the IoT devices to significant malware threats. Mobile malware is the highest threat to the security of IoT data, user’s personal information, identity, and corporate/financial information. This paper analyzes the efforts regarding malware threats aimed at the devices deployed in industrial mobile-IoT networks and related detection techniques. We considered static, dynamic, and hybrid detection analysis. In this performance analysis, we compared static, dynamic, and hybrid analyses on the basis of data set, feature extraction techniques, feature selection techniques, detection methods, and the accuracy achieved by these methods. Therefore, we identify suspicious API calls, system calls, and the permissions that are extracted and selected as features to detect mobile malware. This will assist application developers in the safe use of APIs when developing applications for industrial IoT networks.

PeaPaw

Performance and energy are two major concerns for application development on heterogeneous platforms. It is challenging for application developers to fully exploit the performance/energy potential of heterogeneous platforms. One reason is the lack of reliable prediction of the system’s performance/energy before application implementation. Another reason is that a heterogeneous platform presents a large design space for workload partitioning between different processors. To reduce such development cost, this article proposes a framework, PeaPaw, to assist application developers to identify a workload partition (WP) that has high potential leading to high performance or energy efficiency before actual implementation. The PeaPaw framework includes both analytical performance/energy models and two sets of workload partitioning guidelines. Based on the design goal, application developers can obtain a workload partitioning guideline from PeaPaw for a given platform and follow it to design one or multiple WPs for a given workload. Then PeaPaw can be used to estimate the performance/energy of the designed WPs, and the WP with the best estimated performance/energy can be selected for actual implementation. To demonstrate the effectiveness of PeaPaw, we have conducted three case studies. Results from these case studies show that PeaPaw can faithfully estimate the performance/energy relationships of WPs and provide effective workload partitioning guidelines.

Migrating enterprise applications to the cloud: methodology and evaluation

Migrating existing on-premise applications to the cloud is a complex and multi-dimensional task and may require adapting the applications themselves significantly. For example, when considering the migration of the database layer of an application, which provides data persistence and manipulation capabilities, it is necessary to address aspects like differences in the granularity of interactions and data confidentiality, and to enable the interaction of the application with remote data sources. In this work, we present a methodology for application migration to the cloud that takes these aspects into account. In addition, we also introduce a tool for decision support, application refactoring and data migration that assists application developers in realising this methodology. We evaluate the proposed methodology and enabling tool using a case study in collaboration with an IT enterprise.

GIS‐Based Subsurface Data Management

Abstract: Effective subsurface data management is essential for economical and accurate site characterization. Subsurface data are directly related to specific locations on the earth's surface and thus appear to be well suited for GIS‐based management. However, subsurface data differ from traditional GIS data in that each sampled location includes information associated with multiple z coordinates below the surface. This paper describes the computational aspects of implementing a GIS‐based workbench for managing subsurface data and supporting site characterization. The scope of data management includes modeling, structure, standards, queries, sources, conversion, and integration. The concepts presented may assist application developers to fully utilize GIS technology as a major software component for effective subsurface data management.

The andrew system: The role of human interface guidelines in the design of multimedia applications

As computer systems are becoming more advanced, the user interface issues involved in system design are increasing in complexity. Multimedia applications add yet another layer of complexity to this already intricate task and guidelines are needed to help ensure consistency, both within a given computer system as well as across systems. The Human Factors Design Group of the Information Technology Center (ITC) at Carnegie Mellon University has developed a set of user interface guidelines for the Andrew system. These guidelines are intended to assist application developers, both within the ITC and externally, in creating application programs whose interfaces are consistent with the look and feel of Andrew.