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The hyperledger fabric is a modular blockchain framework used by private companies to develop blockchain-based products, solutions, and applications using plug-and-play components. The smart contracts operating in this framework is created by implementing a chaincode. When implementing a chaincode, there may be a security weakness inside the code, which is the root cause of the security vulnerability. However, when the contract is completed and the block is created, the chaincode cannot be arbitrarily modified, so the security weakness must be analyzed before execution. This paper conducted a study on chaincode intermediate code generation for security weakness analysis of chaincode operating in hyperledger fabric blockchain framework. Analysis of security weaknesses at the source code level is not easy because the code logic is not clear and the complexity is high. On the other hand, security weakness analysis at the intermediate code level is easy to analyze because the code logic of the source code is clearly represented and the complexity is lower than that of the source code.

Korean education-related evaluation agencies utilize a centralized system that directly manages learner data. This leaves the intellectual property of the organizations and the personal information of the students vulnerable to leakage should the central server be attacked. In this study, the researchers propose a multilateral personal portfolio authentication system that guarantees the reliability, integrity, and transparency of the data such as learner's schooling history. The system uses the features of blockchain in a distributed network wherein a learner submits schooling data to a peer in the network. The data is then verified through an agreement among the peers and recorded in a chronologically encrypted ledger. The proposed system is implemented based on Hyperledger Fabric and the analysis thereof conducted by evaluating its processing speed, capacity, and security. The analysis indicates that the system is able to successfully intercept the transactions of unvalidated users, thereby preventing the recording of incorrect data by an unauthorized user. Furthermore, it provides a record of previously made changes to a learner's profile, thus improving the integrity and reliability of the data. This system provides a platform to share learner information safely and promptly among schools, certification authorities, and higher learning institutions.

Tuberculosis (TB) is one of the top causes of death in the world. Though TB is known as the world’s most infectious killer, it can be treated with a combination of TB drugs. Some of these drugs can be active against other infective agents, in addition to TB. We propose a framework called TREASURE (Text mining algoRithm basEd on Affinity analysis and Set intersection to find the action of tUberculosis dRugs against other pathogEns), which particularly focuses on the extraction of various drug–pathogen relationships in eight different TB drugs, namely pyrazinamide, moxifloxacin, ethambutol, isoniazid, rifampicin, linezolid, streptomycin and amikacin. More than 1500 research papers from PubMed are collected for each drug. The data collected for this purpose are first preprocessed, and various relation records are generated for each drug using affinity analysis. These records are then filtered based on the maximum co-occurrence value and set intersection property to obtain the required inferences. The inferences produced by this framework can help the medical researchers in finding cures for other bacterial diseases. Additionally, the analysis presented in this model can be utilized by the medical experts in their disease and drug experiments.

This paper presents an internet-of-things (IoT)-cloud converged virtual machine (VM) system for IoT devices with restricted computing resources. The VM is a software processor that has many advantages in terms of software development, release, maintenance, etc., owing to its platform independence. However, in low-performance devices it has a significant disadvantage because its use is restricted by high execution overheads at the software interpretation level. This paper proposes a VM system for IoT devices that solves this problem while retaining the advantages of VM technology using a lightweight interpreter model and cloud-based computation offloading. The proposed interpreter solves the limited memory/performance problem when running VMs on low-performance devices using two-level instruction-to-native function matching techniques. Furthermore, by solving the low-performance issues of IoT devices using cloud-based offloading, the proposed IoT-cloud VM can run applications that require high-performance computing even when the target hardware system is a low-power IoT device.

This paper proposed an MAC (Media Access Control) operation method based on the CCH (Control Channel) and SCH (Service Channel) control mode of the WAVE (Wireless Access for Vehicle Environment) network in order to secure service performance sensitive to certain delays with no restriction to services pursued in the present WAVE network. The proposed method configures a new superframe for CCH and SCH frames in the WAVE network defined by IEEE Std. 1609.4 and applies TDM (Time Division Multiplexing) to the CCH frame in order to secure traffic stability and latency minimization in each terminal. CSMA/CA (Carrier Sense Multiple Access/Collision Avoidance) is applied to the SCH frame in order to secure autonomy in service perspectives. Additionally, delay control is secured despite congestion among multiple terminals in V2V communication without a station RSU (Road Side Unit) since the proposed MAC operation method adopts a new CCH/SCH coordination mode for the WAVE network. The proposed method’s applicability is verified as the simulation analysis results showed satisfactory low latency performance.

Today, the Internet of Things (IoT) is used for convenience in everyday life in many areas. Owing to the fact that the data collected from the IoT are generated in large quantities, cloud computing is inevitably used to store and analyze the data. However, cloud storage is not owned by the user, so it is unreliable. Verifying the integrity of data collected in an IoT environment and stored in a cloud has two problems: a large amount of data needs to be verified, and the data verification should be done directly on the IoT device. Many methods for data integrity verification use trusted third parties and devices that provide sufficient resources. However, it is difficult to directly apply existing research to the IoT devices that have limited resources. This paper proposes a secure cloud storage service for an IoT environment that is based on a provable data possession model and uses Bloom filters. The experimental results showed that the proposed method saves time and has no significant differences in the verification rate with existing methods, even though the Bloom filter causes false positives. Therefore, the proposed service can effectively process a large amount of data generated in an IoT environment.

Electricity is one of the key role players to build an economy. Electricity consumption and generation can affect the overall policy of the country. This opens an area for some intelligent system that can provide future insights. Intelligent management for electric power consumption requires future electricity power consumption prediction with less error. These predictions provide insights for making further decisions to smooth line the policy and help to grow economy of the country. Future prediction can be categorized into three categories namely (1) long-term, (2) short-term, and (3) mid-term predictions. For our study, we consider mid-term electricity consumption prediction. Dataset is provided by Korea Electric power supply to get insights for metropolitan city like Seoul. Dataset is in time series so we require to analyze dataset with statistical and machine learning models that can support time series dataset. This study provides experimental results from the proposed models. Our proposed models for provided dataset are ARIMA and LSTM, which look promising as RMSE for training is 0.14 and 0.20 RMSE for testing.

Recently, with the rapid change to an aging society and the increased interest in healthcare, disease prediction and management through various healthcare devices and services is attracting much attention. In particular, stroke, represented by cerebrovascular disease, is a very dangerous disease, in which death or mental and physical aftereffects are very large in adults and the elderly. The sequelae of such stroke diseases are very dangerous, because they make social and economic activities difficult. In this paper, we propose a new system to prediction and in-depth analysis stroke severity of elderly over 65 years based on the National Institutes of Health Stroke Scale (NIHSS). In addition, we use the algorithm of decision tree of C4.5, which is a methodology of prediction and analysis of machine learning techniques. The C4.5 decision trees are machine learning algorithms that provide additional in-depth rules of the execution mechanism and semantic interpretation analysis. Finally, in this paper, it is verified that the C4.5 decision tree algorithm can be used to classify and predict stroke severity, and to obtain additional NIHSS features reduction effects. Therefore, during the operation of an actual system, the proposed model uses only 13 features out of the 18 stroke scale features, including age, so that it can provide faster and more accurate service support. Experimental results show that the system enables this by reducing the patient NIH stroke scale measurement time and making the operation more efficient, with an overall accuracy, using the C4.5 decision tree algorithm, of 91.11%.

Nowadays, social inequality is an important social problem. Donations are one of the many ways to improve social inequality. Donation is largely divided into sponsorship by individuals such as corporations and public administration. In the individual sponsorship, it is common to donate to a donation organization and to support the aid recipients by donation organization. Many people are reluctant to support to this donation because of the lack of transparency. In addition, many donation organizations lack transparent and formal administration due to lack of working capital. Therefore, this paper proposes a method to enhance personal transparency by enhancing the transparency of donation organizations and protecting the privacy of sponsors using blockchain that is a Hyperledger fabric. And We implement a blockchain network for the proposed system.

Due to increasing use of third-party libraries because of the increasing complexity of software development, the lack of management of legacy code and the nature of embedded software, the use of third-party libraries which have no source code is increasing. Without the source code, it is difficult to analyze these libraries for vulnerabilities. Therefore, to analyze weaknesses inherent in binary code, various studies have been conducted to perform static analysis using intermediate code. The conversion from binary code to intermediate language differs depending on the execution environment. In this paper, we propose a deep learning-based analysis method to reconstruct missing data types during the compilation process from binary code to intermediate language, and propose a method to generate supervised learning data for deep learning.

The objective of an emergency call is to provide the operator with as much pertinent information about the emergency as possible, so that the best course of action can be taken toward its resolution. In this paper, we propose a dialogue planner to support operators who take emergency calls. The proposed system helps the operators to respond appropriately to a caller’s report by planning potential follow-up dialogue. The decision-making process used by the planner to select from among the proposed actions is implemented as a finite-state machine. The planner assigns a weight to each path between the states, evaluates the current dialogue model, and then adjusts the weights accordingly. Therefore, the weights assigned to potential actions vary dynamically as the dialogue progresses. The proposed model guides the operators’ decisions throughout each conversation by informing them regarding the action that has the highest weighting.

One of the most significant disadvantages of the Internet of Things (IoT) is the overload of information. More information makes it harder to find valuable information. Recommendation systems identify the most suitable items for a given user. The recommended result is only valid if the system users know what they want, and clearly and explicitly convey their needs to the system. Because the role of a recommendation system is to calculate the similarity between the given request and each item, and to rank the similarity, the requests and identity of items should be clear to obtain correct results. However, in most situations in which recommendations are made, requests are implicit and ambiguous. A good recommendation system should make a reliable list of items, even with ambiguous requests. This paper proposes a model of generating recommendations for implicit requests. The model employs two methods that reveals the desire of the requestor and uses content curation with a customized layout to display the recommendations. The first method for revealing the requestor’s desire is to specify the implicit request by combining the user’s customized preference with the collective intelligence. The second method for employing content curation is to arrange the recommendation for users to accept spontaneously. To persuade users, the recommendations are transformed into a layout based on a personalized cognitive bias. Through these processes, reliable and beneficial recommendations can be provided to any user even if their requests are implicit or unclear.

Recent years have seen the development of computing environments for IoT (Internet of Things) services, which exchange large amounts of information using various heterogeneous devices that are always connected to networks. Since the data communication and services occur on a variety of devices, which not only include traditional computing environments and mobile devices such as smartphones, but also household appliances, embedded devices, and sensor nodes, the security requirements are becoming increasingly important at this point in time. Already, in the case of mobile applications, security has emerged as a new issue, as the dissemination and use of mobile applications have been rapidly expanding. This software, including IoT services and mobile applications, is continuously exposed to malicious attacks by hackers, because it exchanges data in the open Internet environment. The security weaknesses of this software are the direct cause of software breaches causing serious economic loss. In recent years, the awareness that developing secure software is intrinsically the most effective way to eliminate the software vulnerability, rather than strengthening the security system of the external environment, has increased. Therefore, methodology based on the use of secure coding rules and checking tools is attracting attention to prevent software breaches in the coding stage to eliminate the above vulnerabilities. This paper proposes a compiler and a virtual machine with secure software concepts for developing secure and trustworthy services for IoT environments. By using a compiler and virtual machine, we approach the problem in two stages: a prevention stage, in which the secure compiler removes the security weaknesses from the source code during the application development phase, and a monitoring stage, in which the secure virtual machine monitors abnormal behavior such as buffer overflow attacks or untrusted input data handling while applications are running.