In interdependent systems, such as electric power systems, entities or components mutually depend on each other. Due to these interdependencies, a small number of initial failures can propagate throughout the system, resulting in catastrophic system failures. This paper addresses the problem of finding the set of entities whose failures will have the worst effects on the system. To this end, a two-phase algorithm is developed. In the first phase, the tight bound on failure propagation steps is computed using a Boolean Satisfiablility (SAT) solver. In the second phase, the problem is formulated as an Integer Linear Programming (ILP) problem using the obtained step bound and solved with an ILP solver. Experimental results show that the algorithm scales to large problem instances and outperforms a single-phase algorithm that uses a loose step bound.

Universities collect and process a massive amount of Personal Identifiable Information (PII) at registration and throughout interactions with individuals. However, student PII can be exposed to the public by uploading documents along with university notice without consent and awareness, which could put individuals at risk of a variety of different scams, such as identity theft, fraud, or phishing. In this paper, we perform an in-depth analysis of student PII leakage at Vietnamese universities. To the best of our knowledge, we are the first to conduct a comprehensive study on student PII leakage in higher educational institutions. We find that 52.8% of Vietnamese universities leak student PII, including one or more types of personal data, in documents on their websites. It is important to note that the compromised PII includes sensitive types of data, student medical record and religion. Also, student PII leakage is not a new phenomenon and it has happened year after year since 2005. Finally, we present a study with 23 Vietnamese university employees who have worked on student PII to get a deeper understanding of this situation and envisage concrete solutions. The results are entirely surprising: the employees are highly aware of the concept of student PII. However, student PII leakage still happens due to their working habits or the lack of a management system and regulation. Therefore, the Vietnamese university should take a more active stand to protect student data in this situation.

Incorporating a tool for administering medication, such as a syringe, is required in microneedles (MNs) for medical use. This renders it easier for non-medical personnel to administer medication. Because it is difficult to fabricate a hollow MN, we fabricated a capillary groove on an MN and its substrate to enable the administration of a higher dosage. MN grooving is difficult to accomplish via the conventional injection molding method used for polylactic acid. Therefore, biodegradable polyacid anhydride was selected as the material for the MN. Because polyacid anhydride is a low-viscosity liquid at room temperature, an MN can be grooved using a processing method similar to vacuum casting. This study investigated the performance of the capillary force of the MN and the optimum shape and size of the MN by a puncture test.

Conventional enzymatic biofuel cells (EBFCs) use glucose solution or glucose from human body. It is desirable to get glucose from a substance containing glucose because the glucose concentration can be kept at the optimum level. This work developed a biofuel cell that generates electricity from cellulose, which is the main components of plants, by using decomposing enzyme of cellulase. Cellulose nanofiber (CNF) was chosen for the ease of decomposability. It was confirmed by the cyclic voltammetry method that cellulase was effective against CNF. The maximum output of the optimized proposed method was 38.7 μW/cm2, which was 85% of the output by using the glucose solution at the optimized concentration.

This paper addresses an observer-design method only using data. Usually, the observer requires a mathematical model of a system for state prediction and observer gain calculation. As an alternative to the model-based prediction, the proposed predictor calculates the states using a linear combination of the given data. To design the observer gain, the data which represent dual systems are derived from the data which represent the original system. Linear matrix inequalities that depend on data of the dual system provides the observer gains.

Crowdedness of buses is playing an increasingly important role in the disease control of COVID-19. The lack of a practical approach to sensing the crowdedness of buses is a major problem. This paper proposes a bus crowdedness sensing system which exploits deep learning-based object detection to count the numbers of passengers getting on and off a bus and thus estimate the crowdedness of buses in real time. In our prototype system, we combine YOLOv5s object detection model with Kalman Filter object tracking algorithm to implement a sensing algorithm running on a Jetson nano-based vehicular device mounted on a bus. By using the driving recorder video data taken from real bus, we experimentally evaluate the performance of the proposed sensing system to verify that our proposed system system improves counting accuracy and achieves real-time processing at the Jetson Nano platform.

In this letter, we investigate tight analytical and asymptotic upper bounds for bit error rate (BER) of constitutional codes over exponentially correlated Nakagami-m fading channels. Specifically, we derive the BER expression depending on an exact closed-form formula for pairwise error event probabilities (PEEP). Moreover, the corresponding asymptotic analysis in high signal-to-noise ratio (SNR) regime is also explored, which is verified via numerical results. This allows us to have explicit insights on the achievable coding gain and diversity order.

Ontology describes concepts and relations in a specific domain-knowledge that are important for knowledge representation and knowledge sharing. In the past few years, several tools have been introduced for ontology modeling and editing. To design and develop an ontology is one of the challenge tasks and its challenges are quite similar to software development as it requires many collaborative activities from many stakeholders (e.g. domain experts, knowledge engineers, application users, etc.) through the development cycle. Most of the existing tools do not provide collaborative feature to support stakeholders to collaborate work more effectively. In addition, there are lacking of standard process adoption for ontology development task. Thus, in this work, we incorporated ontology development process into Scrum process as used for process standard in software engineering. Based on Scrum, we can perform standard agile development of ontology that can reduce the development cycle as well as it can be responding to any changes better and faster. To support this idea, we proposed a Scrum Ontology Development Framework, which is an online collaborative framework for agile ontology design and development. Each ontology development process based on Scrum model will be supported by different services in our framework, aiming to promote collaborative activities among different roles of stakeholders. In addition to services such as ontology visualized modeling and editing, we also provide three more important features such as 1) concept/relation misunderstanding diagnosis, 2) cross-domain concept detection and 3) concept classification. All these features allow stakeholders to share their understanding and collaboratively discuss to improve quality of domain ontologies through a community consensus.

This study presents a new method to exploit both accent and grouping structures of music in meter estimation. The system starts by extracting autocorrelation-based features that characterize accent periodicities. Based on the local boundary detection model, we construct grouping features that serve as additional cues for inferring meter. After the feature extraction, a multi-layer cascaded classifier based on neural network is incorporated to derive the most likely meter of input melody. Experiments on 7351 folk melodies in MIDI files indicate that the proposed system achieves an accuracy of 95.76% for classification into nine categories of meters.

For the further scaling and lower voltage applications of nonvolatile ferroelectric memory, the effect of Kr/O2 sputtering for SrBi2Ta2O9 (SBT) thin film formation was investigated utilizing a SrBi2Ta2O9 target. The 80-nm-thick SBT films were deposited by radio-frequency (RF) magnetron sputtering on Pt/Ti/SiO2/Si(100). Compared with Ar/O2 sputtering, the ferroelectric properties such as larger remnant polarization (Pr) of 3.2 μC/cm2 were observed with decrease of leakage current in case of Kr/O2 sputtering. X-ray diffraction (XRD) patterns indicated that improvement of the crystallinity with suppressing pyrochlore phases and enhancing ferroelectric phases was realized by Kr/O2 sputtering.

This paper deals with the minimum and maximum value distributions based on the n-variate FGM copula with one dependence parameter. The ranges of dependence parameters are theoretically determined so that the probability density function always takes a non-negative value. However, the closed-form conditions of the ranges for the dependence parameters have not been known in the literature. In this paper, we newly provide the necessary conditions of the ranges of the dependence parameters for the minimum and maximum value distributions which are derived from FGM copula, and show the asymptotic properties of the ranges.

Locally repairable codes, which can repair erased symbols from other symbols, have attracted a good deal of attention in recent years because its local repair property is effective on distributed storage systems. (ru, δu)u∈[s]-locally repairable codes with multiple localities, which are an extension of ordinary locally repairable codes, can repair δu-1 erased symbols simultaneously from a set consisting of at most ru symbols. An upper bound on the minimum distance of these codes and a construction method of optimal codes, attaining this bound with equality, were given by Chen, Hao, and Xia. In this paper, we discuss the parameter restrictions of the existing construction, and we propose explicit constructions of optimal codes with multiple localities with relaxed restrictions based on the encoding polynomial introduced by Tamo and Barg. The proposed construction can design a code whose minimum distance is unrealizable by the existing construction.

Android app developers sometimes copy code snippets posted on a question-and-answer (Q&A) website and use them in their apps. However, if a code snippet has vulnerabilities, Android apps containing the vulnerable snippet could also have the same vulnerabilities. Despite this, the effect of such vulnerable snippets on the Android apps has not been investigated in depth. In this paper, we investigate the correspondence between the vulnerable code snippets and vulnerable apps. we collect code snippets from a Q&A website, extract possibly vulnerable snippets, and calculate similarity between those snippets and bytecode on vulnerable apps. Our experimental results show that 15.8% of all evaluated apps that have SSL implementation vulnerabilities (Improper host name verification), 31.7% that have SSL certificate verification vulnerabilities, and 3.8% that have WEBVIEW remote code execution vulnerabilities contain possibly vulnerable code snippets from Stack Overflow. In the worst case, a single problematic snippet has caused 4,844 apps to contain a vulnerability, accounting for 31.2% of all collected apps with that vulnerability.

A new method for adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays with gain/phase uncertainties is presented. Because of the sparsity of the arriving signals, CS theory can be adopted to sample and recover receiving signals with less data. But due to the existence of the gain/phase uncertainties, the sparse representation of the signal is not optimal. In order to eliminating the influence of the gain/phase uncertainties to the sparse representation, most present study focus on calibrating the gain/phase uncertainties first. To overcome the effect of the gain/phase uncertainties, a new dictionary optimization method based on the total least squares (TLS) algorithm is proposed in this paper. We transfer the array signal receiving model with the gain/phase uncertainties into an EIV model, treating the gain/phase uncertainties effect as an additive error matrix. The method we proposed in this paper reconstructs the data by estimating the sparse coefficients using CS signal reconstruction algorithm and using TLS method toupdate error matrix with gain/phase uncertainties. Simulation results show that the sparse regularized total least squares algorithm can recover the receiving signals better with the effect of gain/phase uncertainties. Then adaptive digital beamforming algorithms are adopted to form antenna beam using the recovered data.

As the capabilities and costs of Artificial Intelligence (AI) and of sensors (IoT) continue to improve, the concept of a “control system” can evolve beyond the operation of a discrete technical system based on numerical information and enter the realm of large-scale systems with both technical and social characteristics based on both numerical and unstructured information. This evolution has particular significance for applying the principles of Autonomous Decentralised Systems (ADS) [1]. This article considers the possible roles for ADS in complex technical and social systems extending up to global scales.

Rough set theory is an important branch of data mining and granular computing, among which neighborhood rough set is presented to deal with numerical data and hybrid data. In this paper, we propose a new concept called inconsistent neighborhood, which extracts inconsistent objects from a traditional neighborhood. Firstly, a series of interesting properties are obtained for inconsistent neighborhoods. Specially, some properties generate new solutions to compute the quantities in neighborhood rough set. Then, a fast forward attribute reduction algorithm is proposed by applying the obtained properties. Experiments undertaken on twelve UCI datasets show that the proposed algorithm can get the same attribute reduction results as the existing algorithms in neighborhood rough set domain, and it runs much faster than the existing ones. This validates that employing inconsistent neighborhoods is advantageous in the applications of neighborhood rough set. The study would provide a new insight into neighborhood rough set theory.

A TM010 cavity power combiner is presented, which achieves direct interface to microstrip lines via magnetic field coupling. A prototype is fabricated and its S-matrix measured. From the S-parameters we calculate that it shows less than 0.85 dB insertion loss over 250 MHz bandwidth at X-band. The return power to the input ports is less than -15 dB over this bandwidth. We verify the insertion loss estimation using S-matrix, by measuring transmission S-parameter of a concatenated 2-port divider-combiner network. Similarly analyzed is the case of performance of power combiner when one of the input fails. We find that we can achieve graceful degradation provided we ensure some particular reflection phase at the degraded port.

Sponsored search is a mechanism that shows the appropriate advertisements (ads) according to search queries. The orders and payments of ads are determined by the auction. However, the externalities which give effects to CTR and haven't been considered in some existing works because the mechanism with externalities has high computational cost. In addition, some algorithms which can calculate the approximated solution considering the externalities within the polynomial-time are proposed, however, it assumed that one bidder can propose only a single ad. In this paper, we propose the approximation allocation algorithm that one bidder can offer many ads considering externalities. The proposed algorithm employs the concept of the combinatorial auction in order to consider the combinational bids. In addition, the proposed algorithm can find the approximated allocation by the dynamic programming. Moreover, we prove the computational complexity and the monotonicity of the proposed mechanism, and demonstrate computational costs and efficiency ratios by changing the number of ads, slots and maximum bids. The experimental results show that the proposed algorithm can calculate 0.7-approximation solution even though the full search can't find solutions in the limited times.

The definitions of the Lee complete ρ weight enumerator and the exact complete ρ weight enumerator over Mn×s(F2[u,v]/) are introduced, and the MacWilliams identities with respect to the RT metric for these two weight enumerators of linear codes over Mn×s(F2[u,v]/) are obtained. Finally, we give two examples to illustrate the results we obtained.

Recently, there has been an increase in use-after-free (UAF) vulnerabilities, which are exploited using a dangling pointer that refers to a freed memory. In particular, large-scale programs such as browsers often include many dangling pointers, and UAF vulnerabilities are frequently exploited by drive-by download attacks. Various methods to prevent UAF attacks have been proposed. However, only a few methods can effectively prevent UAF attacks during runtime with low overhead. In this paper, we propose HeapRevolver, which is a novel UAF attack-prevention method that delays and randomizes the timing of release of freed memory area by using a memory-reuse-prohibited library, which prohibits a freed memory area from being reused for a certain period. The first condition for reuse is that the total size of the freed memory area is beyond the designated size. The threshold for the conditions of reuse of the freed memory area can be randomized by HeapRevolver. Furthermore, we add a second condition for reuse in which the freed memory area is merged with an adjacent freed memory area before release. Furthermore, HeapRevolver can be applied without modifying the target programs. In this paper, we describe the design and implementation of HeapRevolver in Linux and Windows, and report its evaluation results. The results show that HeapRevolver can prevent attacks that exploit existing UAF vulnerabilities. In addition, the overhead is small.