Search results for: cloud computing privacy

Authors: Mustapha Benssalah, Mustapha Djeddou, Karim Drouiche

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The random number generation (RNG) presents a significant importance for the security and the privacy of numerous applications, such as RFID technology and smart cards. Since, the quality of the generated bit sequences is paramount that a weak internal generator for example, can directly cause the entire application to be insecure, and thus it makes no sense to employ strong algorithms for the application. In this paper, we propose a new pseudo random number generator (PRNG), suitable for cryptosystems ECC-based, constructed by randomly selecting points from several elliptic curves randomly selected. The main contribution of this work is the increasing of the generator internal states by extending the set of its output realizations to several curves auto-selected. The quality and the statistical characteristics of the proposed PRNG are validated using the Chi-square goodness of fit test and the empirical Special Publication 800-22 statistical test suite issued by NIST.

Keywords: PRNG, security, cryptosystem, ECC

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Suyeon Bae, Abimbola Asojo, Denise Guerin, Caren Martin

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Post-occupancy evaluations (POEs) have been recognized for documenting occupant well-being and responses to indoor environmental quality (IEQ) factors such as thermal, lighting, and acoustic conditions. Sustainable Post-Occupancy evaluation survey (SPOES) developed by an interdisciplinary team at a Midwest University provides an evidence-based quantitative analysis of occupants’ satisfaction in office, classroom, and residential spaces to help direct attention to successful areas and areas that need improvement in buildings. SPOES is a self-administered and Internet-based questionnaire completed by building occupants. In this study, employees in three different office buildings rated their satisfaction on a Likert-type scale about 12 IEQ criteria including thermal condition, indoor air quality, acoustic quality, daylighting, electric lighting, privacy, view conditions, furnishings, appearance, cleaning and maintenance, vibration and movement, and technology. Employees rated their level of satisfaction on a Likert-type scale from 1 (very dissatisfied) to 7 (very satisfied). They also rate the influence of their physical environment on their perception of their work performance and the impact of their primary workspaces on their health on a scale from 1 (hinders) to 7 (enhances). Building A is a three-story building that includes private and group offices, classrooms, and conference rooms and amounted to 55,000 square-feet for primary workplace (N=75). Building B, a six-story building, consisted of private offices, shared enclosed office, workstations, and open desk areas for employees and amounted to 14,193 square-feet (N=75). Building C is a three-story 56,000 square-feet building that included classrooms, therapy rooms, an outdoor playground, gym, restrooms, and training rooms for clinicians (N=76). The results indicated that 10 IEQs for Building A except acoustic quality and privacy showed statistically significant correlations on the impact of the primary workspace on health. In Building B, 11 IEQs except technology showed statistically significant correlations on the impact of the primary workspace on health. Building C had statistically significant correlations between all 12 IEQ and the employees’ perception of the impact of their primary workspace on their health in two-tailed correlations (P ≤ 0.05). Out of 33 statistically significant correlations, 25 correlations (76%) showed at least moderate relationship (r ≥ 0.35). For the three buildings, daylighting, furnishings, and indoor air quality IEQs ranked highest on the impact on health. IEQs about vibration and movement, view condition, and electric lighting ranked second, followed by IEQs about cleaning and maintenance and appearance. These results imply that 12 IEQs developed in SPOES are highly related to employees’ perception of how their primary workplaces impact their health. The IEQs in this study offer an opportunity for improving occupants’ well-being and the built environment.

Keywords: post-occupancy evaluation, built environment, sustainability, well-being, indoor air quality

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Authors: Shuqi Zhang

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Federated learning allows users to participate in collaborative model training without sending data to third-party servers, reducing the risk of user data privacy leakage, and is widely used in smart finance and smart healthcare. However, the distributed architecture design of federation learning itself and the existence of secure aggregation protocols make it inherently vulnerable to backdoor attacks. To solve this problem, the federated learning backdoor defense framework FLEX based on group aggregation, cluster analysis, and neuron pruning is proposed, and inter-compatibility with secure aggregation protocols is achieved. The good performance of FLEX is verified by building a horizontal federated learning framework on the CIFAR-10 dataset for experiments, which achieves 98% success rate of backdoor detection and reduces the success rate of backdoor tasks to 0% ~ 10%.

Keywords: federated learning, secure aggregation, backdoor attack, cluster analysis, neuron pruning

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Authors: Aarohi Surya

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The number of mobile banking users continues to climb across the world due to its increasing popularity, and UAE is no exception. This type of banking is part of the core strategy of most of the financial institutions that allows its customers to conduct a range of financial transactions through mobile apps to cash in the high demand from the bankers. This study aims at evaluating service quality of online banking in Dubai, one of the swiftly growing cities of Middle East. The paper mainly compares online banking services of Multinational bank and National Bank with special reference to Citibank and Emirates NBD. A structured questionnaire survey is conducted among various target groups. The research has been focused on mainly 4 significant areas of online banking, i.e. Privacy, Responsiveness, Reliability, and Efficiency of customer data. Information was analyzed statistically on SPSS to investigate the service quality of e-banking.

Keywords: customer satisfaction, service quality, responsiveness, online banking

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Authors: Shahin Zanbaghi, Saeed Samet

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The internet has transformed the way we shop. Previously, most of our purchases came in the form of shopping trips to a nearby store. Now, it’s as easy as clicking a mouse. But with great convenience comes great responsibility. We have to be constantly vigilant about our personal information. In this work, our proposed approach is to encrypt the information printed on the physical packages, which include personal information in plain text, using a symmetric encryption algorithm; then, we store that encrypted information into a Blockchain network rather than storing them in companies or corporations centralized databases. We present, implement and assess a blockchain-based system using Ethereum smart contracts. We present detailed algorithms that explain the details of our smart contract. We present the security, cost, and performance analysis of the proposed method. Our work indicates that the proposed solution is economically attainable and provides data integrity, security, transparency, and data traceability.

Keywords: blockchain, Ethereum, smart contract, commit-reveal scheme

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Authors: Yaxin Bi, Peter Nicholl

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The rapid advancement of Information and communication technologies (ICT) is extremely influencing every aspect of Higher Education. It has transformed traditional teaching, learning, assessment and feedback into a new era of Digital Education. This also introduces many challenges in capturing and understanding student engagement with their studies in Higher Education. The School of Computing at Ulster University has developed a Feedback And Notification (FAN) Online tool that has been used to send students links to personalized feedback on their submitted assessments and record students’ frequency of review of the shared feedback as well as the speed of collection. The feedback that the students initially receive is via a personal email directing them through to the feedback via a URL link that maps to the feedback created by the academic marker. This feedback is typically a Word or PDF report including comments and the final mark for the work submitted approximately three weeks before. When the student clicks on the link, the student’s personal feedback is viewable in the browser and they can view the contents. The FAN tool provides the academic marker with a report that includes when and how often a student viewed the feedback via the link. This paper presents an investigation into student engagement through analyzing the interaction timestamps and frequency of review by the student. We have proposed an approach to modeling interaction timestamps and use sentiment classification techniques to analyze the data collected over the last five years for a set of modules. The data studied is across a number of final years and second-year modules in the School of Computing. The paper presents the details of quantitative analysis methods and describes further their interactions with the feedback overtime on each module studied. We have projected the students into different groups of engagement based on sentiment analysis results and then provide a suggestion of early targeted intervention for the set of students seen to be under-performing via our proposed model.

Keywords: feedback, engagement, interaction modelling, sentiment analysis

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Authors: Loreta Kaklauskiene, Arturas Kaklauskas

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This employee productivity and work well-being effective system aims to maximise the work performance of personnel and boost well-being in offices. Affective computing, decision support, and knowledge-based systems were used in our research. The basis of this effective system is our European Patent application (No: EP 4 020 134 A1) and two Lithuanian patents (LT 6841, LT 6866). Our study examines ways to support efficient employee productivity and well-being by employing mass-customised, personalised office environment. Efficient employee performance and well-being are managed by changing mass-customised office environment factors such as air pollution levels, humidity, temperature, data, information, knowledge, activities, lighting colours and intensity, scents, media, games, videos, music, and vibrations. These aspects of management generate a customised, adaptive environment for users taking into account their emotional, affective, and physiological (MAP) states measured and fed into the system. This research aims to develop an innovative method and system which would analyse, customise and manage a personalised office environment according to a specific user’s MAP states in a cohesive manner. Various values of work spaces (e.g., employee utilitarian, hedonic, perceived values) are also established throughout this process, based on the measurements that describe MAP states and other aspects related to the office environment. The main contribution of our research is the development of a real-time mass-customised office environment to boost employee performance and well-being. Acknowledgment: This work was supported by Project No. 2020-1-LT01-KA203-078100 “Minimizing the influence of coronavirus in a built environment” (MICROBE) from the European Union’s Erasmus + program.

Keywords: effective decision support and a knowledge-based system, human resource management, employee productivity and work well-being, affective computing

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Authors: Mehrab Faraghti

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Trojan horses, a form of malware masquerading as legitimate software, pose significant cybersecurity threats. These malicious programs exploit user trust, infiltrate systems, and can lead to data breaches, financial loss, and compromised privacy. This paper explores the mechanisms through which Trojan horses operate, including delivery methods such as phishing and software vulnerabilities. It categorizes various types of Trojan horses and their specific impacts on individuals and organizations. Additionally, the research highlights the evolution of Trojan threats and the importance of user awareness and proactive security measures. By analyzing case studies of notable Trojan attacks, this study identifies common vulnerabilities that can be exploited and offers insights into effective countermeasures, including behavioral analysis, anomaly detection, and robust incident response strategies. The findings emphasize the need for comprehensive cybersecurity education and the implementation of advanced security protocols to mitigate the risks associated with Trojan horses.

Keywords: Trojan horses, cybersecurity, malware, data breach

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Authors: Moudi Almousa

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This research paper explores Saudi Arabian female consumers’ experiences using 3D body scanning technology and their level of acceptance of possible market applications of this technology to adopt for apparel online shopping. Data was collected for 82 women after being scanned then viewed a short video explaining three possible scenarios of 3D body scanning applications, which include size prediction, customization, and virtual try-on, before completing the survey questionnaire. Although respondents have strong positive responses towards the scanning experience, the majority were concerned about their privacy during the scanning process. The results indicated that size prediction and virtual try on had greater market application potential and a higher chance of crossing the gap based on consumer interest. The results of the study also indicated a strong positive correlation between respondents’ concern with inability to try on apparel products in online environments and their willingness to use the 3D possible market applications.

Keywords: 3D body scanning, market applications, online, apparel fit

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Authors: Mustafa Kamoona, Mohamed El-Sharkawy

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Although the latest Wireless Local Area Network technology Wi-Fi 802.11i standard addresses many of the security weaknesses of the antecedent Wired Equivalent Privacy (WEP) protocol, there are still scenarios where the network security are still vulnerable. The first security model that 802.11i offers is the Personal model which is very cheap and simple to install and maintain, yet it uses a Pre Shared Key (PSK) and thus has a low to medium security level. The second model that 802.11i provide is the Enterprise model which is highly secured but much more expensive and difficult to install/maintain and requires the installation and maintenance of an authentication server that will handle the authentication and key management for the wireless network. A central issue with the personal model is that the PSK needs to be shared with all the devices that are connected to the specific Wi-Fi network. This pre-shared key, unless changed regularly, can be cracked using offline dictionary attacks within a matter of hours. The key is burdensome to change in all the connected devices manually unless there is some kind of algorithm that coordinate this PSK update. The key idea of this paper is to propose a new algorithm that proactively and effectively coordinates the pre-shared key generation, management, and distribution in the cheap WPA/WPA2 personal security model using only a DD-WRT router.

Keywords: Wi-Fi, WPS, TLS, DD-WRT

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Authors: Nina Dasari, Satvik Dasari

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Smart city solutions are being developed across the globe to transform urban areas. However, the infrastructure enablement for alerting natural disasters such as floods and wildfires is deficient. This paper discusses an innovative device that could be used as part of the smart city initiative to detect and provide alerts in case of floods at road crossings and wildfires. An Internet of Things (IoT) smart city node was designed, tested, and deployed with collaboration from the City of Austin. The end to end solution includes a 3G enabled IoT device, flood and fire sensors, cloud, a mobile app, and IoT analytics. The real-time data was collected and analyzed using IoT analytics to refine the solution for the past year. The results demonstrate that the proposed solution is reliable and provides accurate results. This low-cost solution is viable, and it can replace the current solution which costs tens of thousands of dollars.

Keywords: analytics, internet of things, natural disasters, smart city

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Authors: Adnan Baig, Ishteeaq Naeem, Saad Mansoor

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The Internet of Things (IoT) gadgets have advanced quickly in recent years, and their use is steadily rising daily. However, cyber-attackers can target these gadgets due to their distributed nature. Additionally, many IoT devices have significant security flaws in their implementation and design, making them vulnerable to security threats. Hence, these threats can cause important data security and privacy loss from a single attack on network devices or systems. Botnets are a significant security risk that can harm the IoT network; hence, sophisticated techniques are required to mitigate the risk. This work uses a machine learning-based method to identify IoT orchestrated by botnets. The proposed technique identifies the net attack by distinguishing between legitimate and malicious traffic. This article proposes a hyperparameter tuning model to improvise the method to improve the accuracy of existing processes. The results demonstrated an improved and more accurate indication of botnet-based cyber-attacks.

Keywords: Internet of Things, Botnet, BoT-IoT dataset, ML techniques

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Authors: Xun Li, Haojie Wang

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Federated learning is a kind of distributed training and centralized training mode, which is of great value in the protection of user privacy. In order to solve the problem that the model is vulnerable to backdoor attacks in federated learning, a backdoor attack detection method based on a weighted k-medoids algorithm is proposed. First of all, this paper collates the update parameters of the client to construct a vector group, then uses the principal components analysis (PCA) algorithm to extract the corresponding feature information from the vector group, and finally uses the improved k-medoids clustering algorithm to identify the normal and backdoor update parameters. In this paper, the backdoor is implanted in the federation learning model through the model replacement attack method in the simulation experiment, and the update parameters from the attacker are effectively detected and removed by the defense method proposed in this paper.

Keywords: federated learning, backdoor attack, PCA, k-medoids, backdoor defense

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Authors: Angelis P. Barlampas

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Purpose of Learning Objective: This case depicts the need for cooperation between the emergency department and the radiologist to achieve the best diagnostic result for the patient. The clinical picture must correlate well with the radiology report and when it does not, this is not necessarily someone’s fault. Careful interpretation and good knowledge of the limitations, advantages and disadvantages of each imaging procedure are essential for the final diagnostic goal. Methods or Background: A patient was brought to the emergency department by their relatives. He was suddenly confused and his mental status was altered. He hadn't any history of mental illness and was otherwise healthy. A computing tomography scan without contrast was done, but it was unremarkable. Because of high clinical suspicion of probable neurologic disease, he was admitted to the hospital. Results or Findings: Another T was done after 48 hours. It showed a hypodense region in both thalamic areas. Taking into account that the first CT was normal, but the initial clinical picture of the patient was alerting of something wrong, the repetitive CT exam is highly suggestive of a probable diagnosis of bilateral thalamic infractions. Differential diagnosis: Primary bilateral thalamic glioma, Wernicke encephalopathy, osmotic myelinolysis, Fabry disease, Wilson disease, Leigh disease, West Nile encephalitis, Greutzfeldt Jacob disease, top of the basilar syndrome, deep venous thrombosis, mild to moderate cerebral hypotension, posterior reversible encephalopathy syndrome, Neurofibromatosis type 1. Conclusion: As is the case of limitations for any imaging procedure, the same applies to CT. The acute ischemic attack can not depict on CT. A period of 24 to 48 hours has to elapse before any abnormality can be seen. So, despite the fact that there are no obvious findings of an ischemic episode, like paresis or imiparesis, one must be careful not to attribute the patient’s clinical signs to other conditions, such as toxic effects, metabolic disorders, psychiatric symptoms, etc. Further investigation with MRI or at least a repeated CT must be done.

Keywords: CNS, CT, thalamus, emergency department

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Authors: Zainab Khalifah, Wong Chiet Bing, Noor Hazarina Hashim

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The evolution of customer behavior in purchasing products or services through the Internet leads to airline companies engaging in the e-ticketing process in order to maintain their business. A well-designed website is vitally significant for the airline companies to provide effective communication, support, and competitive advantage. This study was conducted to identify the dimensions of website quality for low cost airline and to investigate the relationship between the website quality and customer e-satisfaction at low cost airline. A total of 381 responses were conveniently collected among local passengers at Low Cost Carrier Terminal, Kuala Lumpur via questionnaire distribution. This study found that the five determinant factors of website quality for AirAsia were Information Content, Navigation, Responsiveness, Personalization, and Security and Privacy. The results of this study revealed that there is a positive relationship between the five dimensions of website quality and customer e-satisfaction, and also information content was the most significant contributor to customer e-satisfaction.

Keywords: website quality, customer e-satisfaction, low cost airline, e-ticketing

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Authors: Po-Chen Chen, Tsung-Han Yang, Zhi-Wei Zheng, Shih-Tsang Tang

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Falls are the most common accidents during mountain hiking, especially in high-altitude environments with unstable terrain or adverse weather. Balance ability is a crucial factor in hiking, effectively ensuring hiking safety and reducing the risk of injuries. If balance ability can be assessed simply and effectively, hikers can identify their weaknesses and conduct targeted training to improve their balance ability, thereby reducing injury risks. With the widespread use of smartphones and their built-in inertial sensors, this project aims to develop a simple Inertial Measurement Unit (IMU) balance measurement technique based on smartphones. This will provide hikers with an easy-to-use, low-cost tool for assessing balance ability, monitoring training effects in real-time, and continuously tracking balance ability through uploading cloud data uploads, facilitating personal athletic performance.

Keywords: balance, IMU, smartphone, wearable devices

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Authors: Terungwa Simon Yange, Agana Moses A.

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This paper studied steganography and designed a simplistic approach to a steganographic tool for hiding information in image files with the view of addressing the security challenges with data by hiding data from unauthorized users to improve its security. The Structured Systems Analysis and Design Method (SSADM) was used in this work. The system was developed using Java Development Kit (JDK) 1.7.0_10 and MySQL Server as its backend. The system was tested with some hypothetical health records which proved the possibility of protecting data from unauthorized users by making it secret so that its existence cannot be easily recognized by fraudulent users. It further strengthens the confidentiality of patient records kept by medical practitioners in the health setting. In conclusion, this work was able to produce a user friendly steganography software that is very fast to install and easy to operate to ensure privacy and secrecy of sensitive data. It also produced an exact copy of the original image and the one carrying the secret message when compared with each.

Keywords: steganography, cryptography, encryption, decryption, secrecy

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Authors: Kunihiro Kamataki

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In this study, active learning based on simple science experiments was developed in a university class of the freshman, in order to improve their scientific literacy. Through the active learning based on simple experiments of generation of cloud in a plastic bottle, students increased the interest in the global atmospheric problem and were able to discuss and find solutions about this problem positively from various viewpoints of the science technology, the politics, the economy, the diplomacy and the relations among nations. The results of their questionnaires and free descriptions of this class indicate that they improve the scientific literacy and motivations of other classroom lectures to acquire knowledge. It is thus suggested that the science experiment is strong tool to improve their intellectual curiosity rapidly and the connections that link the impression of science experiment and their interest of the social problem is very important to enhance their learning effect in this education.

Keywords: active learning, scientific literacy, simple scientific experiment, university education

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Authors: Saurav Kumar Suman, P. Karthigayani

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In this paper the height of the tree is estimated and forest types is classified from the multi temporal RISAT-1 Horizontal-Horizontal (HH) and Horizontal-Vertical (HV) Polarised Satellite Aperture Radar (SAR) data. The novelty of the proposed project is combined use of the Back-scattering Coefficients (Sigma Naught) and the Coherence. It uses Water Cloud Model (WCM). The approaches use two main steps. (a) Extraction of the different forest parameter data from the Product.xml, BAND-META file and from Grid-xxx.txt file come with the HH & HV polarized data from the ISRO (Indian Space Research Centre). These file contains the required parameter during height estimation. (b) Calculation of the Vegetation and Ground Backscattering, Coherence and other Forest Parameters. (c) Classification of Forest Types using the ENVI 5.0 Tool and ROI (Region of Interest) calculation.

Keywords: RISAT-1, classification, forest, SAR data

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Authors: Jossitt Williams Vargas Cruz, Aura Jazmín Pérez Ríos

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The variations of solar dynamics have become a relevant topic of study due to the effects of climate changes generated on the earth. One of the most disconcerting aspects is the variability that the sun has on the climate is the role played by sunspots (extra-atmospheric variable) in the modulation of the Cosmic Rays CR (extra-atmospheric variable). CRs influence the earth's climate by affecting cloud formation (atmospheric variable), and solar cycle influence is associated with the presence of solar storms, and the magnetic activity is greater, resulting in less CR entering the earth's atmosphere. The different methods of climate prediction in Colombia do not take into account the extra-atmospheric variables. Therefore, correlations between atmospheric and extra-atmospheric variables were studied in order to implement a Python code based on neural networks to make the prediction of the extra-atmospheric variable with the highest correlation.

Keywords: correlations, cosmic rays, sun, sunspots and variations.

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Authors: Shaik Mohammad Samiullah Shariff, Siva Sreenath, Sai Haripriya, Prathyusha, M. Padma Lalitha

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The Internet of Things (IoT) is the emerging technology that has been utilized to extend the possibilities for smart dairy farming (SDF). Milk consumption is continually increasing due to the world's growing population. As a result, some providers are prone to using dishonest measures to close the supply-demand imbalance, such as adding adulterants to milk. To identify the presence of adulterants in milk, traditional testing methods necessitate the use of particular chemicals and equipment. While efficient, this method has the disadvantage of yielding difficult and time-consuming qualitative results. Furthermore, same milk sample cannot be tested for other adulterants later. As a result, this study proposes an IoT-based approach for identifying adulterants in milk by measuring electrical conductivity (EC) or Total Dissolved Solids (TDS) and PH. In order to achieve this, an Arduino UNO microcontroller is used to assess the contaminants. When there is no adulteration, the pH and TDS values of milk range from 6.45 to 6.67 and 750 to 780ppm, respectively, according to this study. Finally, the data is uploaded to the cloud via an IoT device attached to the Ubidot web platform.

Keywords: internet of things (IoT), pH sensor, TDS sensor, EC sensor, industry 4.0

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Authors: Ahmed Abdullah Ahmed

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The task of recognizing the writer of a handwritten text has been an attractive research problem in the document analysis and recognition community with applications in handwriting forensics, paleography, document examination and handwriting recognition. This research presents an automatic method for writer recognition from digitized images of unconstrained writings. Although a great effort has been made by previous studies to come out with various methods, their performances, especially in terms of accuracy, are fallen short, and room for improvements is still wide open. The proposed technique employs optimal codebook based writer characterization where each writing sample is represented by a set of features computed from two codebooks, beginning and ending. Unlike most of the classical codebook based approaches which segment the writing into graphemes, this study is based on fragmenting a particular area of writing which are beginning and ending strokes. The proposed method starting with contour detection to extract significant information from the handwriting and the curve fragmentation is then employed to categorize the handwriting into Beginning and Ending zones into small fragments. The similar fragments of beginning strokes are grouped together to create Beginning cluster, and similarly, the ending strokes are grouped to create the ending cluster. These two clusters lead to the development of two codebooks (beginning and ending) by choosing the center of every similar fragments group. Writings under study are then represented by computing the probability of occurrence of codebook patterns. The probability distribution is used to characterize each writer. Two writings are then compared by computing distances between their respective probability distribution. The evaluations carried out on ICFHR standard dataset of 206 writers using Beginning and Ending codebooks separately. Finally, the Ending codebook achieved the highest identification rate of 98.23%, which is the best result so far on ICFHR dataset.

Keywords: off-line text-independent writer identification, feature extraction, codebook, fragments

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Authors: Mahdi Daghmhehci Firoozjaei, Minchang Kim, Dima Alhadidi

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In this paper, we present a power profiling model for smart grid consumers based on real time load data acquired smart meters. It profiles consumers’ power consumption behaviour using the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features be extracted. Two load types are defined and the related load patterns are extracted for classifying consumption behaviour by DTW. The classification methodology is discussed in detail. To evaluate the performance of the method, we analyze the time-series load data measured by a smart meter in a real case. The results verify the effectiveness of the proposed profiling method with 90.91% true positive rate for load type clustering in the best case.

Keywords: power profiling, user privacy, dynamic time warping, smart grid

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Authors: Meshari Aljohani, Stephan Olariu, Ravi Mukkamala

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Data is essential for enhancing the quality of life. Its value creates chances for users to profit from data sales and purchases. Users in data marketplaces, however, must share and trade data in a secure and trusted environment while maintaining their privacy. The first main contribution of this paper is to identify enabling technologies and challenges facing the development of decentralized data marketplaces. The second main contribution is to propose a decentralized data marketplace framework based on blockchain technology. The proposed framework enables sellers and buyers to transact with more confidence. Using a security deposit, the system implements a unique approach for enforcing honesty in data exchange among anonymous individuals. Before the transaction is considered complete, the system has a time frame. As a result, users can submit disputes to the arbitrators which will review them and respond with their decision. Use cases are presented to demonstrate how these technologies help data marketplaces handle issues and challenges.

Keywords: blockchain, data, data marketplace, smart contract, reputation system

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Authors: Omar Alshehri, Vic Lally

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This paper examined the attitudes of using social media tools to support learning at a university in Saudi Arabia. Moreover, it investigated the students’ current usage of these tools and examined the barriers they could face during the use of social media tools in the education process. Participants in this study were 42 university students. A web-based survey was used to collect data for this study. The results indicate that all of the students were familiar with social media and had used at least one type of social media for learning. It was found out that all students had very positive attitudes towards the use of social media and welcomed using these tools as a supplementary to the curriculum. However, the results indicated that the major barriers to using these tools in learning were distraction, opposing Islamic religious teachings, privacy issues, and cyberbullying. The study recommended that this study could be replicated at other Saudi universities to investigate factors and barriers that might affect Saudi students’ attitudes toward using social media to support learning.

Keywords: barriers to social media use, benefits of social media use, higher education, Saudi Arabia, social media

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Authors: Amirhossein Safi

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Internet of things (IOT) is a kind of advanced information technology which has drawn societies’ attention. Sensors and stimulators are usually recognized as smart devices of our environment. Simultaneously, IOT security brings up new issues. Internet connection and possibility of interaction with smart devices cause those devices to involve more in human life. Therefore, safety is a fundamental requirement in designing IOT. IOT has three remarkable features: overall perception, reliable transmission, and intelligent processing. Because of IOT span, security of conveying data is an essential factor for system security. Hybrid encryption technique is a new model that can be used in IOT. This type of encryption generates strong security and low computation. In this paper, we have proposed a hybrid encryption algorithm which has been conducted in order to reduce safety risks and enhancing encryption's speed and less computational complexity. The purpose of this hybrid algorithm is information integrity, confidentiality, non-repudiation in data exchange for IOT. Eventually, the suggested encryption algorithm has been simulated by MATLAB software, and its speed and safety efficiency were evaluated in comparison with conventional encryption algorithm.

Keywords: internet of things, security, hybrid algorithm, privacy

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Authors: Jeoffrey Oostrom, Robbert James Schlingmann, Hani Alers

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This document depicts the research into the indicators of dementia progression, the automation of quality of life assignments, and the visualization of it. To do this, the Smart Teddy project was initiated to make a smart companion that both monitors the senior citizen as well as processing the captured data into an insightful dashboard. With around 50 million diagnoses worldwide, dementia proves again and again to be a bothersome strain on the lives of many individuals, their relatives, and society as a whole. In 2015 it was estimated that dementia care cost 818 billion U.S Dollars globally. The Smart Teddy project aims to take away a portion of the burden from caregivers by automating the collection of certain data, like movement, geolocation, and sound-levels. This paper proves that the Smart Teddy has the potential to become a useful tool for caregivers but won’t pose as a solution. The Smart Teddy still faces some problems in terms of emotional privacy, but its non-intrusive nature, as well as diversity in usability, can make up for it.

Keywords: dementia care, medical data visualization, quality of life, smart companion

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Authors: Prisa Damrongsiri, Kittinan Pongpianskul, Mario Kubek, Herwig Unger

Abstract:

Apps are today the most important possibility to adapt mobile phones and computers to fulfill the special needs of their users. Location- and context-sensitive programs are hereby the key to support the interaction of the user with his/her environment and also to avoid an overload with a plenty of dispensable information. The contribution shows, how a trusted, secure and really bi-directional communication and interaction among users and their environment can be established and used, e.g. in the field of home automation.

Keywords: apps, context-sensitive, location-sensitive, self-configuration, mobile computing, smart home

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Authors: Wiseman Emmanuel Nkhomah, Chiyembekezo Kachimanga, Julia Huggins, Fabien Munyaneza

Abstract:

Background: Partners In Health – Malawi introduced one of Operational Researches called Primary Health Care (PHC) Surveys in 2020, which seeks to assess progress of delivery of care in the district. The study consists of 5 long surveys, namely; Facility assessment, General Patient, Provider, Sick Child, Antenatal Care (ANC), primarily conducted in 4 health facilities in Neno district. These facilities include Neno district hospital, Dambe health centre, Chifunga and Matope. Usually, these annual surveys are conducted from January, and the target is to present final report by June. Once data is collected and analyzed, there are a series of reviews that take place before reaching final report. In the first place, the manual process took over 9 months to present final report. Initial findings reported about 76.9% of the data that added up when cross-checked with paper-based sources. Purpose: The aim of this approach is to run away from manually pulling the data, do fresh analysis, and reporting often associated not only with delays in reporting inconsistencies but also with poor quality of data if not done carefully. This automation approach was meant to utilize features of new technologies to create visualizations, reports, and dashboards in Power BI that are directly fished from the data source – CommCare hence only require a single click of a ‘refresh’ button to have the updated information populated in visualizations, reports, and dashboards at once. Methodology: We transformed paper-based questionnaires into electronic using CommCare mobile application. We further connected CommCare Mobile App directly to Power BI using Application Program Interface (API) connection as data pipeline. This provided chance to create visualizations, reports, and dashboards in Power BI. Contrary to the process of manually collecting data in paper-based questionnaires, entering them in ordinary spreadsheets, and conducting analysis every time when preparing for reporting, the team utilized CommCare and Microsoft Power BI technologies. We utilized validations and logics in CommCare to capture data with less errors. We utilized Power BI features to host the reports online by publishing them as cloud-computing process. We switched from sharing ordinary report files to sharing the link to potential recipients hence giving them freedom to dig deep into extra findings within Power BI dashboards and also freedom to export to any formats of their choice. Results: This data automation approach reduced research timelines from the initial 9 months’ duration to 5. It also improved the quality of the data findings from the original 76.9% to 98.9%. This brought confidence to draw conclusions from the findings that help in decision-making and gave opportunities for further researches. Conclusion: These results suggest that automating the research data process has the potential of reducing overall amount of time spent and improving the quality of the data. On this basis, the concept of data automation should be taken into serious consideration when conducting operational research for efficiency and decision-making.

Keywords: reporting, decision-making, power BI, commcare, data automation, visualizations, dashboards

Procedia PDF Downloads 116