Storage Usage Research Articles

Ewolucja środków płatności (zapłaty): od pieniądza gotówkowego do pieniądza elektronicznego

Money has existed in civilization for thousands years. The development of money has passed through various stages in accordance with time, place and circumstances. Primitive money took many forms, from cowrie shells to cattle and later transformed into metallic money (like gold, silver, copper, bronze, etc.) The continued development of money was very much linked to the growth in world trade and commerce. Next to cash came up non-cash payment transactions, mainly with credit or debit card. Money serves three purposes. First, it serves as a medium of exchange. Second, as a standard of value, it serves as a measure for the value of a good or service and thus provides a standard for making comparisons between different goods and services. Third, it must be widely accepted. The larger the community of users who trust and accept money, the more that its value as a medium of exchange is increased. Finally, when it is exchanged, there is anonymity. Rapid advances in technology, including Internet have made a lot of changes in payment methods. Technological innovations convert money into something more virtual than physical. Electronic money (e-money) is the digital equivalent of cash. It can be used for making payments without involving bank accounts in the transactions and always acts as a prepaid bearer instrument. E-money can be software or hardware based. One common type of hardware-based e-money is the ‘electronic purse’, where users store relatively small amounts of money on the chip or magnetic stripe of their payment card, other smart card or on mobile phones, to make small payments. Another option is to store e-money in a payment account on the Internet (software based). In general, e-money should be characterized as a substitute for currency (cash) The development of innovative e-money raises numerous legal and regulatory issues that must be addressed. These include finding acceptable methods for authentication and protection of information, accommodating the special needs of law enforcement, and creating the requisite means of settling disputes. The progressive development of technology and communication, harmonization of currency systems and becoming more common use of electronic instruments may create the uniform (European) system of electronic money. That would made e-money the legal tender with the force of redemption obligations.

Monitoring Older Adult Blood Pressure Trends at Home as a Proxy for Brain Health.

Even when older adults monitor hypertension at home, it is difficult to understand trends and share them with their providers. MyHealthNetwork is a dashboard designed for patients and providers to monitor blood pressure readings to detect hypertension and ultimately warning signs of changes in brain health. A multidisciplinary group in a Digital Health course at Tufts University School of Medicine used Design Thinking to formulate a digital solution to promote brain health among older adults in the United States (US). Older adults (aged 65 and over) are a growing population in the US, with many having one or more chronic health conditions including hypertension. Nearly half of all American adults ages 50-64 worry about memory loss as they age and almost all (90%) wish to maintain independence and age in their homes. Given the well-studied association between hypertension and dementia, we designed a solution that would ultimately promote brain health among older adults by allowing them to measure and record their blood pressure readings at home on a regular basis. Going through each step in the Design Thinking process, we devised MyHealthNetwork, an application which connects to a smart blood pressure cuff and stores users' blood pressure readings in a digital dashboard which will alert users if readings are outside of the normal range. The dashboard also has a physician view where users' data can be reviewed by the physician and allow for shared treatment decisions. The authors developed a novel algorithm to visually display the blood pressure categories in the dashboard in a way straightforward enough that users with low health literacy could track and understand their blood pressure over time. Additional features of the dashboard include educational content about brain health and hypertension, a digital navigator to support users with application use and technical questions. Phase 1 in the development of our application includes a pilot study involving recruitment of Primary Care Providers with patients who are at risk of dementia to collect and monitor BP data with our prototype. Subsequent phases of development involve partnerships to provide primary users with a rewards program to promote continued use, additional connections to secondary users such as family members and expansion to capture other health metrics.

The Image of the Other in the (post-)Covid Period: Analysis of Russian-language Internet Queries

The digital age has greatly changed the way information is stored and accessed. The Internet allows us to retrieve an unlimited amount of data from anywhere, at any time of the day or night. The search for new information consistently takes place via search engines, which process and store user query statistics. The analysis of these queries allows us to trace various social trends. At the same time, the personality of the researcher does not affect the "query" of the user, who is fully "sincere and independent" in finding the information he or she needs. Our hypothesis for this study is that by analysing the queries of Internet users we can identify the attitude of the contemporary Russian society to the Other and determine the criteria by which the image of the Other is formed. Considering the nature of the COVID-19 pandemic, the researchers assumed that periods of lockdown may have had a particular effect, increasing interest in certain markers of otherness and decreasing interest in other markers. As a result, we identified 10 models of otherness during the (post)lockdown period, in which food and sexual marker groups are the dominant ones. In particular, the Other-Chinese model, as in previous years, remains worrying. The focus has shifted from the appearance to the sexual and food aspects. The COVID-19 pandemic has played a part in this. The Other-Japanese/Korean model also remains ambiguous, but there is a downward trend in alertness. As for the other models, for the most part they are allert-neutral.

Energy management system for optimal cost and storage utilization of renewable hybrid energy microgrid

This paper proposes an improved energy management system to reduce the energy cost and the storage utilization of a hybrid solar and wind energy microgrid with battery storage and grid-connected option. The performance improvement relies on a parameter, called the contribution factor, that provides an adequate amount of the required power from the battery based on the electricity price of the main grid. Different profiles, for that factor, are investigated to enhance the system performance. Load shifting mechanism has been integrated into the energy management to enhance the energy cost. The system uses the efficiencies of the components to accurately compute the energy cost. Simulations have been performed for different rates of the load demand and results have shown the superiority of the proposed energy management system compared to the conventional one with respect to reducing the energy cost, the battery degradation cost and increasing the battery lifespan. From the results, it has been found that using a linear profile for the contribution factor and the load shifting will result in daily costs of $68.27 and $0.81 for the energy and the degradation, respectively. These costs represent a 25.66 % decrease in energy cost and a 91.72 % decrease in battery degradation cost compared to the conventional energy management system where the grid electricity and the load shifting are not considered in the usage of the battery storage.

Conceptual Design of Python IDE with Embedded Turkish Spoken Chatbot that Analyzes and Corrects the Syntax Errors

Intelligent agents, in addition to act as a cognitive tool, they can also to be designed as learning agents. In this way, they can learn the user's behavior from users' past behaviors. Learning agents can analyze the user's / student's behavior to a task, build a database of past activities, and suggest better strategies. This study provides a detailed review of interactive guidance using intelligent agents, and then introduces a concept model for a Conversation-based Turkish Python Integrated development environment (IDE) that can analyze user syntax errors and help them to correct errors. The model proposed in this study consists of three layers. These are user interface layer, middle layer, and Python interpreter layers. User interface layer consists of code editor and chatbot components. Middle layer includes code structural control subsystem, code error manager, and intelligent agent subsystems. The code structural control module analyzes conditions, loops, branching, and other types of program flow controls in the user's code. The code error manager analyzes the error outputs of the user code which is generated by the Python interpreter. The intelligent learner, on the other hand, uses these inferences to understand the reason for the student's error and convey the necessary actions to the user by the help of chatbot and suggest possible corrections. The proposed Integrated development environment (IDE) has a well-designed UI that can be easily adapted by newcomers. The coding editor can be used as a stand-alone desktop software, or it can also connect to a cloud storage to store user codes in the cloud. A web application is also planned for our proposed IDE. The teacher will be able to assign homework to the student over the web. The student will be able to view these assignments on the web, do the assignments in the desktop editor and send them back to the teacher over the web. In addition, each user’s error characteristics will be analyzed, the success of the learning will be measured, and the deficiencies of the students will be determined using the Intelligent Agent Subsystem.

AI Based Remotely monitoring Web User Interface to capture patient temperature and medicine consumption

he Coronavirus disease 2019 (COVID-19) pandemic has impacted the world like no other pandemic in history. COVID-19 has progressed rapidly affecting health care in the global community. In this work, we look at the web user interface for elderly patients to easily keep track of their daily temperature and their pill consumption record and see if there is a need to visit a doctor. We are going to use Efficient Net, a Deep Learning Convolution Neural Network (CNN) architecture for systematical use of model scaling and balancing network depth, width, and resolution for better performance of our model. We are using angular components to develop user interface and expressJS to write backend services and MongoDB to store user records for the application which we learn in depth throughout the paper. we have trained and developed a model that could successfully detect people from the video and confirm the pill consumption by patient with high accuracy rate connected to a THT thermal camera. This study will help medical professionals, especially during the COVID-19 pandemic, in remotely monitoring the patient’s dosage and determining appropriate action in case of non-compliance.

Efficient Medical Image Segmentation Based on Knowledge Distillation.

Recent advances have been made in applying convolutional neural networks to achieve more precise prediction results for medical image segmentation problems. However, the success of existing methods has highly relied on huge computational complexity and massive storage, which is impractical in the real-world scenario. To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network. This architecture empowers the lightweight network to get a significant improvement on segmentation capability while retaining its runtime efficiency. We further devise a novel distillation module tailored for medical image segmentation to transfer semantic region information from teacher to student network. It forces the student network to mimic the extent of difference of representations calculated from different tissue regions. This module avoids the ambiguous boundary problem encountered when dealing with medical imaging but instead encodes the internal information of each semantic region for transferring. Benefited from our module, the lightweight network could receive an improvement of up to 32.6% in our experiment while maintaining its portability in the inference phase. The entire structure has been verified on two widely accepted public CT datasets LiTS17 and KiTS19. We demonstrate that a lightweight network distilled by our method has non-negligible value in the scenario which requires relatively high operating speed and low storage usage.

AI Based Remotely monitoring Web User Interface to capture patient temperature and medicine consumption

The Coronavirus disease 2019 (COVID-19) pandemic has impacted the world like no other pandemic in history. COVID-19 has progressed rapidly affecting health care in the global community. In this work, we look at the web user interface for elderly patients to easily keep track of their daily temperature and their pill consumption record and see if there is a need to visit a doctor. We are going to use Efficient Net, a Deep Learning Convolution Neural Network (CNN) architecture for systematical use of model scaling and balancing network depth, width, and resolution for better performance of our model. We are using angular components to develop user interface and expressJS to write backend services and MongoDB to store user records for the application which we learn in depth throughout the paper. we have trained and developed a model that could successfully detect people from the video and confirm the pill consumption by patient with high accuracy rate connected to a THT thermal camera. This study will help medical professionals, especially during the COVID-19 pandemic, in remotely monitoring the patient’s dosage and determining appropriate action in case of non-compliance.

Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware

Ransomware is a special malware designed to extort money in return for unlocking the device and personal data files. Smartphone users store their personal as well as official data on these devices. Ransomware attackers found it bewitching for their financial benefits. The financial losses due to ransomware attacks are increasing rapidly. Recent studies witness that out of 87% reported cyber-attacks, 41% are due to ransomware attacks. The inability of application-signature-based solutions to detect unknown malware has inspired many researchers to build automated classification models using machine learning algorithms. Advanced malware is capable of delaying malicious actions on sensing the emulated environment and hence posing a challenge to dynamic monitoring of applications also. Existing hybrid approaches utilize a variety of features combination for detection and analysis. The rapidly changing nature and distribution strategies are possible reasons behind the deteriorated performance of primitive ransomware detection techniques. The limitations of existing studies include ambiguity in selecting the features set. Increasing the feature set may lead to freedom of adept attackers against learning algorithms. In this work, we intend to propose a hybrid approach to identify and mitigate Android ransomware. This study employs a novel dominant feature selection algorithm to extract the dominant feature set. The experimental results show that our proposed model can differentiate between clean and ransomware with improved precision. Our proposed hybrid solution confirms an accuracy of 99.85% with zero false positives while considering 60 prominent features. Further, it also justifies the feature selection algorithm used. The comparison of the proposed method with the existing frameworks indicates its better performance.

Precise: Predictive Content Dissemination Scheme exploiting realistic mobility patterns

Device-to-Device (D2D) communications have expanded the way of managing available network resources to efficiently distribute data between users. D2D exploits communication alternatives, in Opportunistic Networks, based on short range wireless radio technologies such as Bluetooth and WiFi-Direct. Besides, nowadays in most urban areas, realistic human mobility is characterized by often repeated patterns that can be used to accurately predict the next visited regions—we call these regions hotspots (or Replication Zones (RZs)). In this work, we present Predictive Content Dissemination Scheme (Precise), to explore and combine the D2D paradigm along with real mobility and predictions focused on the dissemination of content among hotspots. To analyze the viability of such scheme, we show simulation results and evaluate the average content availability, lifetime and delivery delay, storage usage and network utilization metrics. We compare the performance of Precise with state-of-the-art approaches, such as Epidemic, restricted Epidemic, and Proximity-Interest-Social (PIS) routing protocols. Our results underline the need for smart usage of communication opportunities and storage. We demonstrate that Precise allows for a neat reduction in network activity by decreasing the number of data exchanges by up to 92%, requiring the use of up to 50% less of on-device storage. This comes at negligible costs. In particular, the delivery delay with Precise shows an increase with respect to epidemic dissemination schemes that varies from 0.03 s in the most dynamic case to at most 1.91 s for the least dynamic case, and which however does not hinder the possibility to use Precise for real-time applications. Regarding how contents are spread, we observe that Precise requires 2% to 20% less mobile users to carry them within a target hotspot, especially under slow dynamics. This however does not impact on the probability that mobile users entering the hotspots obtain contents, and barely shortens the lifetime of contents in our experiments from 100 min down to about 95, in the worst case. This demonstrates that the reduction of content availability among mobile users with Precise is either negligible or not impactful, thus guaranteeing the dissemination of contents as with legacy epidemic dissemination protocols.

FluidSMR: Adaptive Management for Hybrid SMR Drives

Hybrid Shingled Magnetic Recording (H-SMR) drives are the most recently developed SMR drives, which allow dynamic conversion of the recording format between Conventional Magnetic Recording (CMR) and SMR on a single disk drive. We identify the unique opportunities of H-SMR drives to manage the tradeoffs between performance and capacity, including the possibility of adjusting the SMR area capacity based on storage usage and the flexibility of dynamic data swapping between the CMR area and SMR area. We design and implement FluidSMR, an adaptive management scheme for hybrid SMR Drives, to fully utilize H-SMR drives under different workloads and capacity usages. FluidSMR has a two-phase allocation scheme to support a growing usage of the H-SMR drive. The scheme can intelligently determine the sizes of the CMR and the SMR space in an H-SMR drive based on the dynamic changing of workloads. Moreover, FluidSMR uses a cache in the CMR region, managed by a proposed loop-back log policy, to reduce the overhead of updates to the SMR region. Evaluations using enterprise traces demonstrate that FluidSMR outperforms baseline schemes in various workloads by decreasing the average I/O latency and effectively reducing/controlling the performance impact of the format conversion between CMR and SMR.

Techno-economic and environmental assessment of flexible operation for decarbonized super-critical power plants using reactive gas–liquid absorption

Reactive gas–liquid absorption is currently one of the most technological and commercial mature decarbonization solution for energy-intensive industrial applications. This decarbonization system is based on chemical absorption - desorption cycle using alkanolamine solutions. In this work, the reactive gas–liquid absorption was assessed from techno-economic and environmental perspective for decarbonization of super-critical power plants as well as to improve the plant time flexibility. The assessed coal and lignite plant concepts generate 1000 MW net output with 90% carbon capture rate. As benchmark cases, the plant designs without CO2 capture were also assessed to quantify the decarbonization energy and cost penalties. Detailed evaluations cover the following aspects: decarbonized power generation by chemical scrubbing with / without solvent storage facilities; set-up of an integrated assessment methodology; evaluation of main techno-economic and environmental performance indicators. A key novelty element of this work represents the usage of solvent storage to improve the flexibility of decarbonized power plant. Base-load operation and flexible part-load operation with lean & rich solvent storage were both evaluated to assess the flexibility impact on overall performance indicators. The time flexible solution based on solvent storage reduces the specific capital cost by about 11% and the levelized cost of electricity (LCOE) by about 7–9%.

Easy, Quality of Service, and Timeliness of Delivery to Customer Satisfaction

The Internet makes it easy for users to trade with people. This is what causes many companies to compete to create quality online services or e-commerce services that consumers can trust to simplify and add value to the products they offer so that they can be cheaper and more efficient. The purpose of this study was to determine the effect of easy, service quality, and timeliness of delivery on customer satisfaction in online shopping store users of the Surakarta UNIBA student application. This research is quantitative descriptive. The population in this study were the users of the online shopping store, Surakarta, UNIBA students, totaling 911 students. The sample uses 10% of the total population and the results are 91.1 students and then rounded up to 100 students. Easy, Quality of Service and Timeliness of Delivery have a simultaneous and significant effect on Customer Satisfaction in the online shopping application Tokopedia. Based on the t-test, Easy has a positive and significant effect on Customer Satisfaction in the Tokopedia online shopping application. Service Quality has a positive and significant effect on Customer Satisfaction in the online shopping application Tokopedia. Timeliness of Delivery has a positive and significant effect on Customer Satisfaction in the online shopping application Tokopedia.

Cloud-Based Enhanced Storage System Using Android Technology

Cloud storage is a secure-oriented aspect for users. It provides on-demand service to the user, where users can connect to a cloud through network to store user’s data in the cloud. In a frequently connected world, users obtain personal or shared data stored in the cloud such as Dropbox, Google Drive and Microsoft One Drive with the various gadget. This shows the popularity of cloud storage being used among users. This project aims to integrate enhance features in cloud storage which can be more adaptability for users. This proposed system contains the mobile application and web application that implemented using Android Studio for mobile app, PHP for a web app and it will be stored in the cloud via Google Cloud. This system also provides basic characteristics such as delete file, attach documents and file recovery. The enhancement characteristics are QR Code and chatbot where, QR code used to scan into a web for website and chatbot allow the users to communicate within the same application. The author has chosen the questionnaire and case study as fact-finding method for proposed system.

A Scrutiny on Cloud Computing Security Issues

Cloud computing is one of the widely used technology in the 20th century. Cloud has become an essential part in our day-to-day life, because the data are stored in the cloud in any one form such as Gmail, Google Drive etc. As the users store sensitive information in the cloud, the Cloud Service Providers need to provide proper data security for data stored in the cloud. There are many issues in Cloud Computing Security. This paper discussed about various issues in the Cloud Computing environment and the future work directions have identified for the Cloud Computing Security.

Key Protected Deputy Signature Scheme against the Deputy Signing Key Exposure

Key exposure is very harmful to a cryptographic system. To decrease the loss from the deputy signing key vulnerability in identity-based proxy signature systems, we propose the method of key protected deputy signature (IBKPDS) using the method of parallel key insulation. The proposed IBKPDS is based on identities and is shown to be secure with the cryptographic proof. In the proof, there is no random oracle. In an IBKPPS crypto-system, a user stores his short-lived deputy signing key by himself and saves two long-lived keys in two heavily guarded boxes respectively. The derived IBKPDS cryptographic system is heavily key-separated. A thief who wants to obtain crucial information can not corrupt the IBKPDS when he get only one long-lived key. In addition, the user can change the short-lived deputy signing keys frequently at low risk.

PSCPAC: Post-quantum secure certificateless public auditing scheme in cloud storage

To reduce the local storage burden and enjoy the conveniently ubiquitous access, an increasing number of individuals and enterprises prefer to outsource their data to the cloud server. Due to the loss of physical control over data, how to guarantee the cloud server stores user’s data honestly becomes an important security challenge. Meanwhile, most of existing public auditing schemes based on traditional public key cryptosystem either suffer from bearing the heavy burden of certificate management or possess an inherent key escrow drawback. Even worse, with the advent of powerful quantum computers, most of them could be cracked quickly by a large-scale quantum computer in polynomial time in the near future. To this end, we propose a novel post-quantum secure certificateless public auditing scheme in cloud storage (PSCPAC) from lattice assumptions. Compared with the existing schemes, our novel scheme enjoys the following promising merits: (1) removing the requirement for complicated certificate management in traditional PKI-based public auditing schemes; (2) eliminating the inherent key escrow problem in ID-based public auditing schemes; (3) providing resistance against quantum computers attacks. Correctness, security and performance analysis demonstrate that PSCPAC is provably secure, highly efficient and more practical for post-quantum security in cloud storage public auditing.

Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors

The advent of autonomous vehicles is indeed a potential field of research in today's situation. Connected Vehicles (CV) have received a lot of attention in the last decade, which has resulted in CV as a Service (CVaaS). With the advent of taxi services, there is a need for or demand for robust, seamless, and secure information transmission between the vehicles connected to a vehicular network. Thus, the concept of vehicular networking is transformed into novel concept of autonomous and connected vehicles. These autonomous vehicles will serve as a better experience by providing instant information from the vehicles via congestion reduction. The significant drawback faced by the invention of autonomous vehicles is the malicious floor of intruders, who tend to mislead the communication between the vehicles resulting in the compromised smart devices. To address these concerns, the best methodology that will protect and secure the control system of the autonomous vehicle in real time is blockchain. This research work proposes a blockchain framework in order to address the security challenges in autonomous vehicles. This research work enhances the security of smart vehicles thereby preventing intruders from accessing the vehicular network. To validate the suggested technique, money security criteria such as changing stored user ratings, probabilistic authentication scenarios, smart device compromise, and bogus user requests were employed. The observed findings have been documented and analysed, revealing an 82% success rate.

A Predictive Control Strategy for Energy Management in Micro-Grid Systems

The integration of renewable energy sources (RES) was amplified, during the past decades, in order to tackle the challenges related to energy demands and CO2 increases. Recently, many initiatives have been taken by promoting the deployment and the usage of micro-grids (MG) in buildings, as decentralized systems, for energy production. However, the variable nature of RESs and the limited size of energy storage systems require the deployment of adaptive control strategies for efficient energy balance. In this paper, a generalized predictive control (GPC) strategy is introduced for energy management (EM) in MG systems. Its main objective is to efficiently connect the electricity generators and consumers in order to predict the most suitable actions for energy flow management. In fact, based on energy production and consumption profiles as well as the availability of energy storage systems, the proposed EM will be able to select the best suitable energy source for supplying the building’s loads. It will efficiently manage the usage of energy storage and the utility grid while maximizing RESs power generation. Simulations have been conducted, using real-sitting scenarios, and results are presented to validate the proposed predictive control approach by showing its effectiveness for MG systems control.

Overview and Exploitation of Haptic Tele-Weight Device in Virtual Shopping Stores

In view of the problem of e-commerce scams and the absence of haptic interaction, this research aims to introduce and create a tele-weight device for e-commerce shopping in smart cities. The objective is to use the proposed prototype to provide a brief overview of the possible technological advancements. When the tele-weight device is affixed over the head-mounted display, it allows the user to feel the item’s weight while shopping in the virtual store. Addressing the problem of having no physical interaction between the user (player) and a series game scene in virtual reality (VR) headsets, this research approach focuses on creating a prototype device that has two parts, a sending part and a receiving part. The sending part measures the weight of the object and transmits it over the cellular network to the receiver side. The virtual store user at the receiving side can thus realize the weight of the ordered object. The findings from this work include a visual display of the item’s weight to the virtual store e-commerce user. By introducing sustainability, this haptic technology-assisted technique can help the customer realize the weight of an object and thus have a better immersive experience. In the device, the load cell measures the weight of the object and amplifies it using the HX711 amplifier. However, some delay in the demonstration of the weight was observed during experimentation, and this indirectly altered the performance of the system. One set of the device is sited at the virtual store user premises while the sending end of the device is positioned at the warehouse. The sending end hardware includes an Arduino Uno device, an HX711 amplifier chip to amplify the weight from the load cell, and a cellular module (Sim900A chip-based) to transmit the weight in the form of an encoded message. The receiving end hardware includes a cellular module and an actuator involving a motor gear arrangement to demonstrate the weight of the object. Combining the fields of e-commerce, embedded systems, VR, and haptic sensing, this research can help create a more secure marketplace to attain a higher level of customer satisfaction.