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Cryptosystems are commonly deployed to secure data transmission over an insecure line of communication. To provide confusion in the data over insecure networks, substitution boxes are the solitary components for delivering a nonlinear mapping between inputs and outputs. A confusion component of a block cipher with high nonlinearity and low differential and linear approximation probabilities is considered secure against cryptanalysis. This study aims to design a highly nonlinear substitution-permutation network using the blotch symmetry of quantum spin states on the Galois field GF (28). To observe the efficiency of the proposed methodology, some common and advanced measures were evaluated for performance, randomness, and cryptanalytics. The outcomes of these analyses validate that the generated nonlinear confusion components are effective for block ciphers and attain better cryptographic strength with a high signal-to-noise ratio in comparison to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Although the use of personal data from different contexts is essential to curbing the spread of COVID-19 in epidemic-handling systems (EHSs), it increases the chances of privacy breaches and personal data misuse. This article analyses the data lifecycle and proposes various technical requirements for privacy preservation in EHSs. [ABSTRACT FROM AUTHOR]

Many people have used public keys in various areas based on public key infrastructure (PKI). PKI provides a method to publicize public keys securely. However, existing PKI methods have a problem that they assume trusted third parties. Therefore, the existing PKIs cannot be used when users cannot trust certificate issuers. To solve this problem, we propose a new trust model and describe its implementation based on blockchain. Users can trust the certificate issued by full nodes even if they do not trust the full nodes themselves. We analyze the security of our model and show that its security can be achieved higher than existing models. This new model can be particularly useful in an environment where a third party cannot be easily trusted. [ABSTRACT FROM AUTHOR]

Blockchain was introduced as an incorruptible distributed data structure for financial transactions. Its contents are intrinsically verifiable, it sustains its state over multiple nodes, and its property of consistent replication can be used to build an automated structure to control computational resources. [ABSTRACT FROM AUTHOR]

In the system security technology, the information hiding field is developed as technologies for embedding information into digital content as media. The proposed technique is a technical steganography technique which uses a technique of information hiding through physical / statistical change of signal of contents. In the digital watermarking or steganography technology, the embedding process is frequently performed in the frequency domain in order to increase the latent capacity and to improve the image quality. However, these methods have a disadvantage that the computation complexity is high and the constraint condition becomes complicated. In the contrast, there have been various studies based on histogram shifting technology in reversible information hiding. Assume it selects multiple peaks on the histogram shifting, the capacity of data concealment gradually increased by applying multiple peak histogram method. The steganography technique using histogram shifting doesn't show any significant improvement for the hiding capacity and image quality in the early methods. In this paper, we analyze the effect of information hiding technology in terms of adopting the histogram shift method including skipping. In addition, we propose 2n divergence data hiding as a complimentary technique to improve hiding capacity. The proposed method has been proven through an example using mathematical expressions, and further improvement measures could be derived. [ABSTRACT FROM AUTHOR]

As office workers spend most of their time in front of computer monitors, they are at the risk of suffering from forward head posture syndrome. Repeated head forward postures may cause cervical discs. To address a precautionary approach for the syndrome, we propose a pervasive system. The purpose of this article is to identify design issues in pervasive computing when applied to forward head syndrome, which can give designers insights into how people interacts with the pervasive service and help shape a future research direction to cover those findings. [ABSTRACT FROM AUTHOR]

With the development of intelligent vehicles, in-vehicle security and privacy receive considerable attention from researchers and drivers. The data stored in a vehicle control unit (VCU) may contain privacy-sensitive content, which may be exposed to unauthorized devices or attackers due to the structural problems of the in-vehicle network. To overcome this problem, data can be encrypted with the target module's key before being transmitted to the in-vehicle network. However, this may lead to another efficiency problem. In this study, we propose a model that adopts the proxy re-encryption (PRE) scheme to the in-vehicle network. This is called privacy-enhancing in-vehicle network key management (PINK). In the proposed model, the data stored in the VCU is encrypted with a master key. In addition, when the stored data is requested by a node, it is transformed into a ciphertext that can only be decrypted with the node's key using the PRE scheme. Moreover, for the case in which a special group of nodes request the data several times, simultaneously, a group-based key management scheme i provided. Further it is demonstrated using simulations that the proposed PINK scheme can be efficiently applied to the in-vehicle network. [ABSTRACT FROM AUTHOR]

For interactions with humans, robots need the capability of describing the events they encountered for verbal communication. In this paper, a sentential cognitive system (SCS) using sentences as a media to represent cognized events for conversational human–robot interaction (HRI) is presented. The SCS comprising multiple modules such as perception and behavior, reasoning, and memory interprets modular events to sentential form from acquired cognitive information and a cognitive grammar linking it to sentences and vice versa. The sentences are stored in a sentential memory for being retrieved by reasoning procedures with the auxiliary memories, an object descriptor and a motion descriptor, afterward for conversational HRI. In the experiment, the proposed SCS is implemented in a robot and tested with scenarios of spatiotemporal conversation about the handling of objects. The result shows the feasibility and efficiency of the proposed SCS for conversational HRI. [ABSTRACT FROM AUTHOR]

This article proposes the modified KNN (K Nearest Neighbor) algorithm which receives a string vector as its input data and is applied to the text summarization. The results from applying the string vector based algorithms to the text categorizations were successful in previous works and the text summarization is able to be viewed into a binary classification where each paragraph is classified into summary or non-summary. In the proposed system, a text which is given as the input is partitioned into a list of paragraphs, each paragraph is classified by the proposed KNN version, and the paragraphs which are classified into summary are extracted ad the output. The proposed KNN version is empirically validated as the better approach in deciding whether each paragraph is essential or not in news articles and opinions. We need to define and characterize mathematically more operations on string vectors for modifying more advanced machine learning algorithms. [ABSTRACT FROM AUTHOR]

In a recent work, Bernardini and Rinaldo generalize and attempt to improve upon Elias method to obtain unbiased random bits from a geometric distribution resulted from a Poisson process. As a response, we analyse the output rates of their method and compare with the original binary Elias method applied on a Bernoulli process resulted from the same Poisson process, which turns out to be much simpler to implement and to have a higher output rate. [ABSTRACT FROM AUTHOR]

Telco systems usually run large-scale, centralized key management systems. However, centralized approaches based on conventional public key encryption like RSA raise problems such as key escrow, secure channel to delivery key, and third-party query as well as single point of failure. To address these problems, we propose both certificate-based encryption (CBE) and hierarchical certificate-based encryption (HCBE) schemes proved secure in the standard model. Compared with other schemes, our schemes are proved IND-CCA2 (Indistinguishability under Adaptive Chosen Ciphertext Attack) secure in full model, where the number of group elements is independent of the value of security parameter. As far as we know, the proposed HCBE is the first fully IND-CCA2 secure scheme with ciphetexts of constant size. [ABSTRACT FROM AUTHOR]

Most Intrusion Detection Systems (IDS) nowadays are signature-based. They are very useful and accurate for detecting known attacks. However, they are inefficient with unknown attacks. Moreover, most of cyber attacks start with malicious URLs. Attackers try to trick users into clicking on malicious URLs. This gives attackers an easy way to launch attacks. To defend against this, companies and organizations use IDS/IPS to detect malicous URLs using blacklist of URLs. This is very efficient with known malicious URLs, but useless with unknown malicious URLs. To overcome this problem, a number of malicious Web site detection systems have been proposed. One of the most promising methods is to apply machine learning detection techniques. In this paper, we present a new lexical approach to classify URLs by using machine learning techniques which patternize the URLs. Our approach is based on natural language processing features which use word vector representation and ngram models on the blacklist word as the main features. Using this technique can help classifier distinguish benign URLs from malicious ones. Our experimentation shows that our approach can achieve a high degree of accuracy at 97.1% in the case of SVM. Moreover, it can maintain a high level of robustness with 0.97 precision and 0.93 recall scores. [ABSTRACT FROM AUTHOR]

This article proposes the modified AHC (Agglomerative Hierarchical Clustering) algorithm which considers the feature similarity and is applied to the text clustering. The words which are given as features for encoding texts into numerical vectors are semantic related entities, rather than independent ones, and the synergy effect between the word clustering and the text clustering is expected by combining both of them with each other. In this research, we define the similarity metric between numerical vectors considering the feature similarity, and modify the AHC algorithm by adopting the proposed similarity metric as the approach to the text clustering. The proposed AHC algorithm is empirically validated as the better approach in clustering texts in news articles and opinions. The significance of this research is to improve the clustering performance by utilizing the feature similarities. [ABSTRACT FROM AUTHOR]

This paper presents a distributed Load Balancing Trade Framework (LBTF) with focus on demand response and Advanced Metering Infrastructure (AMI) security in smart grid. This comprehensive energy market model LBTF framework can achieve good balance in demand response in two ways. First way is utility-grid contract, which introduced a simple reward policy using distributed proof-of-work (PoW) consensus algorithm in Blockchain to motivate consumer to use less energy in peak hours. Through this policy consensus algorithm reward electric units to the consumers in peak hours. And in second way scheme introduce Micro-grid contract, which is a peer to peer (P2P) trade in global market. Those consumer (prosumer) who can generate energy using renewable resources can sell their surplus energy to other consumer through this contract. Furthermore, hash functions, public key encryption and digital signatures have been used to provide privacy preserving to the consumers and integrity to the stored data. [ABSTRACT FROM AUTHOR]

Malware detection have long become a challenge in research. The existing methods rely on malware signature which are proved not to be effective nowadays. The recent researches focus on using probabilistic model such as machine learning to detect the existence of malware. They, however, do not achieve such a good performance. Particularly, machine learning techniques still have an issue of high feature engineering overhead. In this paper, we propose a deep learning method to detect malware based on their malicious behavior. Through experimentation, we show that our method can achieve a very high accuracy rate of 98.75 in F1 measure, compared to state of the art methods. [ABSTRACT FROM AUTHOR]

Predicate encryption is a public-key encryption that supports attribute-hiding as well as payload-hiding and achieves high flexibility in terms of access control. Since Katz, Sahai, and Waters first proposed the predicate encryption scheme in 2008, several predicate encryption schemes have been published. Unfortunately these are impractical as they require O(n) pairing computations for decryption with considerably large sized public parameters, secret key, and ciphertext, where n is the dimension of the attribute/predicate vectors. In this paper, we propose a very efficient predicate encryption scheme that requires only n exponentiation plus three pairing computations for decryption with shorter sized public parameters, secret key, and ciphertext. The proposed scheme is proven selective attribute-secure against chosen-plaintext attacks in the standard model under the Asymmetric Decisional Bilinear Diffie-Hellman assumptions. [ABSTRACT FROM AUTHOR]

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area. [ABSTRACT FROM AUTHOR]

The article discusses digital rights management (DRM). As currently implemented DRM prevents users from achieving interoperability across devices with the content they have purchased. After briefly reviewing early DRM technology and its current architecture, the author examines several varieties of interoperability including full-format, building-block-based, and transformation-driven. A discussion of the challenges that must be met before interoperability can be realized concludes with his recommendation that a new use model for DRM be adopted. INSET: FROM THE CONSUMER VIEW.

Recently, National Institute of Standards and Technology (NIST) in the U.S. had initiated a global-scale competition to standardize the lightweight authenticated encryption with associated data (AEAD) and hash function. Gimli is one of the Round 2 candidates that is designed to be efficiently implemented across various platforms, including hardware (VLSI and FPGA), microprocessors, and microcontrollers. However, the performance of Gimli in massively parallel architectures like Graphics Processing Units (GPU) is still unknown. A high performance Gimli implementation on GPU can be especially useful to Internet of Things (IoT) applications, wherein the gateway devices and cloud servers need to handle a massive number of communications protected by AEAD. In this paper, we show that with careful optimization, Gimli can be efficiently implemented in desktop and embedded GPU to achieve extremely high throughput. Our experiments show that the proposed Gimli implementation can achieve 661.44 KB/s (encryption), 892.24 KB/s (decryption), and 4344.46 KB/s (hashing) in state-of-the-art GPUs. [ABSTRACT FROM AUTHOR]

As many services have changed from offline to online, a lot of personal information including user private data has been collected by and exchanged with various service providers. An issue raised in this process is that personal information can be exploited by multiple unwanted entities without the data owner's knowledge. To solve this problem, functional encryption was proposed. It is suitable for data protection because even if a third-party uses the owner's secret key for a function f, it cannot retrieve the original message x from the ciphertext. This means that information about x cannot be published, but is exposed only as f(x), the result of the function f. However, previous pairing-based public key functional encryption schemes for inner product evaluations (FE-IPE) cannot be practical solutions yet because they require too much computation, communication and storage overheads. In this paper, we propose an efficient pairing-based public key FE-IPE that requires only n (i.e., the dimension of vectors for function and message) exponentiation plus two pairing computations for decryption with smaller sized public parameters, secret keys and ciphertexts. And this scheme supports fully collusion resistance. The proposed scheme is proven selectively secure against chosen-plaintext attacks in the standard model under the external Diffie–Hellman assumption. [ABSTRACT FROM AUTHOR]

Personal content creation has become a significant part of the media industry, and sales are not necessarily the main objective of such creators. Individual creators have a greater need for privacy protection; therefore, the novel active content (AC) approach is proposed to address this need. The Intention Markup Language (InML) markup language is also introduced to express the content owner's intentions systematically and precisely, and then to implement these as an executable code. To overcome the incompatibility and inconvenience of the existing digital rights management (DRM) systems, the novel AC format is proposed. This is based on the Portable Executable (PE) format and consists of a PE header and sections, followed by an AC header, the content, and the intention code. The proposed content virtualization technique protects the content against a variety of attacks by treating content in main memory as if it were stored on the user's storage. Since external players, beyond the AC control boundary, may potentially expose the content, these are controlled by hooking code included in the intention engine while the AC is in use. Finally, example use cases are presented to show how intentions can be expressed in InML documents and the creation and use of an AC file on Windows is demonstrated. [ABSTRACT FROM AUTHOR]

As the mobile communication evolves toward next generation, the 5th G, it was required that the number of base stations should be increased and the size of the base station should be reduced. In order for the base station to be designed in small size, we need to minimize the filters in the base station. There are couple of ways to reduce the size of the filters in the base station, one of which is to change the materials for the resonators of filter from the metal to ceramic. The ceramic resonator makes it possible to reduce the size of the filter at the same time it would be difficult to get the performance as the metal resonator. In order to use ceramic resonator with the same characteristics as the metal resonator, we have to consider the sintering and the fabrication condition of ceramic resonator. This paper introduces the sintering and fabrication condition of ceramic resonator for the filter miniaturization. [ABSTRACT FROM AUTHOR]

Recent research in the computer security sector has primarily been qualitative, focusing on detection aspects where malware has already infected the target. Challenges for security vendors include developing better techniques for early detection of malware attacks before they can do malicious damage. Malware prediction models are needed that can describe and predict malware generation processes. In this study, we address the feasibility of quantitative characterization of malware in security assessment, we propose a model with aim to explain the mechanism behind malware generation which contributes to estimating malware discovery. Although several malware modeling systems have been proposed, such models have shortcomings in related to historical data and do not consider malware data as a time series. Using time series analysis, we provide predictive neural network models for five datasets from Symantec and Malwr. The models explore the structures of malware data along with leveraging non-linear and linear properties to predict the number of future malware. Our examination also reveals that it is possible to model the malware discovery process using a neural network based non-linear model. In addition, our analysis provides insights into understanding the mechanisms that generate malware data series. This information can be useful for intelligence services and vital to threat assessment. [ABSTRACT FROM AUTHOR]

Recently, drone technology has developed rapidly for various purposes. A drone is very useful for aerial surveillance due to its remote sensing capability. Multiple target detection and tracking are essential to recognize any harmful threats in advance, however the image captured at a distance is easily degraded due to blurring and noise as well as low resolution. This paper addresses the detection and tracking of moving vehicles with drone imaging. A drone captures video sequences of multiple moving vehicles from a distance. Cars and buses are the objects of interests driving on urban roads. The detection step consists of frame difference followed by thresholding and morphological operation considering the size of region of interest (ROI). The centroids of the ROI's are considered measurements for tracking. Tracking is performed with interacting multiple model (IMM) filtering, which estimate the state of vectors and covariance matrices using multiple modes of Kalman filtering. The measurements in the validation region are associated with established tracks by the nearest neighbor rule. In the experiment, total seven moving cars and buses are captured at a long distance by a drone. It will be shown that the proposed method well detects the moving vehicles and achieves a good accuracy in estimating their locations. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a human-vehicle classification scheme using a Doppler spectrum distribution based on 2D Range-Doppler FMCW (Frequency Modulated Continuous Wave). Typically, because humans have non-rigid motion, multiple reflection points can appear on the Doppler spectrum. However, in the actual field, the Doppler spectrum distribution of a walking human is highly variable over time. Thus method using only this characteristic of the extended Doppler spectrum is limited with regard to human-vehicle classification. In order to improve the target classification performance, we designed two feature. The first is the Doppler spectrum extension features, which is expressed as the number of Doppler reflection points with magnitudes exceeding reference threshold. Next, we defined the Doppler spectrum variance feature, which is extracted as the difference the reflection points between two successive frames. We can determine how the Doppler spectrum expands with the first feature, and how the Doppler spectra change based on the second feature. To verify the proposed target classification scheme, we measured real data using a 24 GHz FMCW transceiver on an actual road with various scenarios of walking humans and moving vehicles. From an analysis of the results, we confirmed that the thresholds effectively classify humans and vehicles based on the two proposed features. Finally, we verified that the results of the proposed classification scheme using the two features were much better than those using the first feature alone. [ABSTRACT FROM AUTHOR]

Software Defined Networking (SDN) is an emerging paradigm, which brings the network innovation and attracts both the industries and researchers. SDN is a programmable network model that separates the control logic from the forwarding plane. The centralize control plane takes care of all networking resources. The attackers target the SDN controller, to paralyze the logic plane that is considered as the brain of the network which provides a lot of benefits. However, due to the characteristics the control plane becomes the attractive target of security attacks for the adversaries. One of the most known threats is Distributed Denial-of-Service (DDoS) attacks with the goal to exhausting network resources by sending heavy traffic to them, causing network congestion. Since SDN was proposed, DDoS attack has become a popular research field in SDN security and many researchers have been presented DDoS attacks detection, prevention and mitigation solutions in SDN environment using different methods. This paper surveys the previous researches in DDoS attacks detection and mitigation based methods available in SDN environment. [ABSTRACT FROM AUTHOR]

In a Personal Area Network (PAN) populated with wearable devices, the security of stored files is an important concern. Secret sharing has received considerable attention as an effective approach for handling security as it guarantees confidentiality and integrity. A high level of data privacy is still assured when devices are compromised, lost, or stolen, and even in cases where the total count of these devices is less than the minimum threshold. However, because traditional secret sharing requires tremendous computational overhead and consumes large storage space, it is suboptimal for battery-powered wearable devices. To overcome these limitations, combinatorial-based file sharing is proposed. However, this approach is also hindered by an efficiency problem, because the preparation of file storage and retrieval involves computational costs. Additionally, this file sharing scheme was designed without taking into consideration an environment with heterogeneous wearable devices. To address the aforementioned problems, we first propose a new fog network model that delegates the calculation for the preparation of file storage and retrieval to a fog node, such as a smartphone or tablet PC. A fog node has comparatively higher computational speed, and a larger battery capacity; hence, the resources of the devices in the PAN can be managed efficiently. Additionally, we propose a new algorithm that facilitates file shares by considering the heterogeneous characteristics of wearable devices. In particular, we consider the factors of storage capacity and network speed to determine the size of the file shares. We use mathematical analysis and simulations to demonstrate that our proposed model efficiently manages stored files. [ABSTRACT FROM AUTHOR]

TCP SYN flood attack has been one of representative DDoS attack in computer security history. To cope with this, a number of researches have been done, but they have a high false detection rate and are hard to be applicable in network address translation environment which is very common in the real world. To address these problems, we propose an efficient scheme to cope with SYN flood attacks with spoofed IP addresses. Compared to the existing approaches, it achieves the lowest false positive rate of 0.0003% at maximum and detects false IP packets at an earlier point of time, which serve to reduce the impacts of DDoS attacks significantly. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent noise prediction system using pattern analysis and deep learning method. The proposed system is designed to analyze an interlayer noise information from noise and vibration sensors in order to provide a noise warning signal to the noise generator. This scheme is based on both a pattern analysis and a predictive model at that time when the threshold is exceeded. The analyzing process of noise information is performed on Arduino board and noise data is collected from multiple sensors with Bluetooth transmission technology. The Firebase cloud server allows users to access the collected data easily. In addition, a large amount of stored data is analyzed systematically using both R program and a predictive model according to a noise pattern. Performance analysis is carried out to evaluate the excellent performance of the proposed system by showing the accurate predictive results. [ABSTRACT FROM AUTHOR]

Wireless sensor network becomes crucial with the appearance of the Internet of Things (IoT). IoT roots in the sensors' data and the technology to transmit the data to the sink. As the importance of IoT arises, the MAC protocols to transmit the data are proposed in many papers. Initially, the sensor devices and the sink remain stationary. As the technology has been advanced, the sensor devices start to move. Accordingly, novel protocols have been proposed for the changing topology. In this paper, we propose Improved-GM-MAC (I-GM-MAC), which means that new algorithms to reconfigure the group number and prevent the endless increasing of group number of the nodes in an isolated area are added to the 3-dimensional group management MAC (3D-GM-MAC). [ABSTRACT FROM AUTHOR]

The study presents a framework to analyze and detect meddling in real-time network data and identify numerous meddling patterns that may be harmful to various communication means, academic institutes, and other industries. The major challenge was to develop a non-faulty framework to detect meddling (to overcome the traditional ways). With the development of machine learning technology, detecting and stopping the meddling process in the early stages is much easier. In this study, the proposed framework uses numerous data collection and processing techniques and machine learning techniques to train the meddling data and detect anomalies. The proposed framework uses support vector machine (SVM) and K-nearest neighbor (KNN) machine learning algorithms to detect the meddling in a network entangled with blockchain technology to ensure the privacy and protection of models as well as communication data. SVM achieves the highest training detection accuracy (DA) and misclassification rate (MCR) of 99.59% and 0.41%, respectively, and SVM achieves the highest-testing DA and MCR of 99.05% and 0.95%, respectively. The presented framework portrays the best meddling detection results, which are very helpful for various communication and transaction processes. [ABSTRACT FROM AUTHOR]

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency. [ABSTRACT FROM AUTHOR]

Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture for estimating intrusion detection empowered with a Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system model is proposed. This study investigates the methodology of RTS-DELM implemented in blockchain-based smart homes to detect any malicious activity. The approach of data fusion and the decision level fusion technique are also implemented to achieve enhanced accuracy. This study examines the numerous key components and features of the smart home network framework more extensively. The Fused RTS-DELM technique achieves a very significant level of stability with a low error rate for any intrusion activity in smart home networks. The simulation findings indicate that this suggested technique successfully optimizes smart home networks for monitoring and detecting harmful or intrusive activities. [ABSTRACT FROM AUTHOR]

Abstract: Attribute-based encryption (ABE) is a promising cryptographic primitive for fine-grained access control of distributed data. In ciphertext policy attribute-based encryption (CP-ABE), each user is associated with a set of attributes and data are encrypted with access policies on attributes. A user is able to decrypt a ciphertext if and only if his attributes satisfy the access policy embedded in the ciphertext. However, key escrow is inherent in ABE systems. A curious key generation center in that construction has the power to decrypt every ciphertext. We found that most of the existing ABE schemes depending on a single key authority suffer from the key escrow problem. In this study, we propose a novel CP-ABE key issuing architecture that solves the key escrow problem. The proposed scheme separates the power of issuing user keys into two parties: the key generation center and the attribute authority. In the proposed construction, the key generation center and the attribute authority issue different parts of secret key components to users through a secure two-party computation protocol such that none of them can determine the whole set of keys of users individually. We demonstrate how the proposed key issuing protocol can be applied in the existing CP-ABE scheme and resolve the key escrow problem. [Copyright &y& Elsevier]

Abstract: Broadcast encryption enables a broadcaster to encrypt messages and transmit them to some subset S of authorized users. In identity-based broadcast encryption schemes, a broadcasting sender typically encrypts a message by combining public identities of receivers in S and system parameters. However, previous identity-based broadcast encryption schemes have not been concerned about preserving the privacy of receivers. Consequently, all of the identities of broadcast receivers in S are exposed to the public in the previous schemes, which may be subject to attacks on user privacy in lots of pragmatic applications. We propose a novel privacy-preserving identity-based broadcast encryption scheme against an active attacker. The proposed scheme protects the privacy of receivers of broadcasted messages by hiding the identities of receivers in S. Additionally, it achieves less storage and computation costs required to encrypt and decrypt the broadcast message, compared to the previous identity-based broadcast encryption schemes that do not provide user privacy. [Copyright &y& Elsevier]

Rain streaks can eclipse some information of an image taken during rainfall which can degrade the performance of a vision system. While existing rain removing methods can recover the semantic structure, they lack natural texture recovery. The aim of this work is to recover the hidden structure and texture under the rain streaks with fine details. We propose a novel generative adversarial network with two discriminators to remove rain called rain removal generative adversarial network, where a combination of reconstruction, feature and adversarial losses is used for low level, structural and natural recovery, respectively. We have found that exploiting low-level l 1 loss with high-level structural similarity loss as a reconstruction loss is quite effective in attaining visually plausible and consistent texture. Qualitative and quantitative evaluations on our synthetically created dataset and a benchmark dataset show substantial performance gain than state-of-the-art rain removing methods. [ABSTRACT FROM AUTHOR]

A distance estimation method for hopping-frequency-coding (HFC)-based continuous wave (CW) is proposed herein. It is important to frequently update the distance estimate of a near-field underwater vehicle, robust to the time delay, Doppler effect, and volume reverberation. Frequency hopping ensures a high estimation accuracy of the wideband signal without being influenced by the Doppler effect, and also allows the receiver to update the target information at each frequency-hopping instance. The proposed algorithm, which is the initial algorithm for HFC-based CW, focuses on the update period of the estimates and real-time implementation rather than the optimization of estimation accuracy. To achieve this goal, the proposed method exploits multiple Doppler correlators, a difference moving average filter, and four thresholds. The update period is inversely related to the hopping frequency, which is directly proportional to the Doppler frequency. As the target or transceiver moves at a high speed, the frequency-hopping period can be reduced, and the proposed algorithm can update the target information more frequently. [ABSTRACT FROM AUTHOR]

The Mammographic image is a tool for observing breast cancer. Analyzing difficulties include shape, size variety, nearby tissue, and noise. In this paper, we propose a method to classify mammogram abnormalities based on learning vector quantization inference classifier (LVQIC) with fuzzy co-occurrence matrix (FCOM) textural features. The system is implemented on the Mini-MIAS data set with a 5-class problem, i.e., the classification of architectural distortion (AD), spiculated mass (SPIC), calcification (CALC), well-defined/circumscribed masses (CIRC), and normal (NORM). The implementation is also on a 2-class problem consisting of AD-vs-All, SPIC-vs-All, CALC-vs-All, CIRC-vs-All, and NORM/abnormal. The best blind test result is from the 5-class problem with features from fuzzy co-occurrence matrix (FCOM) with 4 clusters, co-occurrence distance d = 2, and 16 prototypes per class. The best classification result is 100% correct classification with 0.03, 0.04, 0.06, and 0.02 false positive rate for AD, SPIC, CALC, and CIRC, respectively. [ABSTRACT FROM AUTHOR]

We present a new approach for generating thumbnail images from H.264/AVC coded bit streams. We have verified analytically that mismatch errors between the encoder and decoder prevent the direct generation of thumbnail images from H.264/AVC transform coefficients. Based on this analysis, we have devised a method that exploits both the spatial and transform domains. What distinguishes our algorithm from previous works is that it determines the thumbnail image pixels by summing the residual and estimate block averages. The residual block averages are directly acquired in the transform domain and the estimate block averages are calculated in the spatial domain. The proposed method produces thumbnail images that are indistinguishable to the ones produced by the method that decodes the H.264/AVC-I slice bit streams and then scales them down, while, for most of images, it executes almost 3 times faster than the down-scaling method at frequently used bandwidths. [ABSTRACT FROM AUTHOR]

With the emergence of mobile and pervasive computing, digital gadgets and products have become a pervasive and of our lives. Everyday, they influence us explicitly or implicitly and are changing our way of life as well as our behaviors, either intentionally or unintentionally. Throughout the past decade, research has been conducted to make persuasion interactive rather than unilateral. The outcome is known today as persuasive computing technology. There have been considerable efforts to apply persuasive technology in many domains, especially healthcare. Although these efforts to are gradually coming to fruition, several problems and challenges remain. Most currently available persuasive systems were not based on explicit theoretical foundations of persuasion theories and models when they were developed. There are two major reasons: (1) a general lack of theoretical frameworks and practical methodologies for building persuasive systems or applications, and (2) it is difficult for computer scientists and developers to understand and harness persuasion theories and models. In response, we have developed an Action-based Behavior Model (ABM) that can be utilized by computer scientists and developers. In this paper, we validate ABM through a scenario-based case, describing how it can be utilized in participatory telehealth to enable persuasion. [ABSTRACT FROM AUTHOR]

IoT devices are diverse in their characteristics and made by many vendors, hence the inter-operation among them is difficult. Especially, end users can't make their own programs by do-it-yourselves. IFTTT and Zapier platforms are designed to help end users to make them inter-operable easily and prevail in these days. Their approach is categorized into a Trigger-Action-Programming, in which trigger conditions and actions are already made by professional programmers of several IoT vendors and end users composite them into their own applications easily. But, their drawback is that the composition can be made at once in the first level, hence end users can't make more complicated applications. Our approach is based on a dataflow programming paradigm which resembles the TAP in that the internal actions are triggered when all the inputs of a node are prepared. In our approach, a composition of some atomic nodes becomes another atomic node, so the composition would continue iteratively. This feature is so generous that several visual programming languages like LabView are relied on the approach for various fields. We propose the overall architecture of our system and explain them. We also present Internet of Things examples of our approach, which shows that atomic dataflow objects can be associated to produce composite dataflow objects. And they are also composited to make more complex applications iteratively. We compare IFTTT, Zapier, and our approach qualitatively and show that end users can make more diverse and flexible applications in our approach. [ABSTRACT FROM AUTHOR]

This paper presents a novel binary classifier based on two best fitting hyperellipsoids in the feature space, called twin-hyperellipsoidal support vector machine (TESVM). The idea of TESVM is inspired by the minimum volume covering ellipsoid together with twin-hypersphere support vector machine (THSVM) which is a variant of the well-known support vector data description (SVDD). Following the concept of THSVM, TESVM constructs two hyperellipsoids where each hyperellipsoid is closest to one class but also as far as possible from the other class in order to form a decision boundary. The construction of hyperellipsoids in the feature space is also enabled through the use of empirical feature mapping. The experimental results on several artificial as well as standard real-world datasets are provided to demonstrate the performance of TESVM. Particularly, TESVM outperforms its spherical counterpart in term of classification accuracy. [ABSTRACT FROM AUTHOR]

The job/career matching has been ever increasingly important social issue in every country. The career matching is to assign the right candidate to the right company. The matching is passive process by employment counselor who cannot go through all profiles of candidates and also companies' profiles. And there can be a human mistake assigning the wrong candidate to a wrong company. To minimize miss-matching cases, the paper investigates several statistical analysis methods and also proposes an artificial intelligence based method. In this paper, the method named Artificial Intelligence based Design platform (AID) has been developed for career matching between university students and companies. The efficiency of AID has been proven by comparing the results (matching rate representing perfect matching) obtained by the proposed method AID and statistical methods such as least squares, Pearson correlation, Manhattan distance. The paper shows that AID can produce zero miss-matching for student's skill and company's needs while the statistical methods produce more than 30% miss-matching. In other words, AID can assign the right student to the right company. [ABSTRACT FROM AUTHOR]

For the artificial intelligence (AI) to effectively mimic humans, understanding humans, more specifically, human emotion is important. Sentiment analysis aims to automatically uncover the underlying sentiment or emotions that humans hold towards an entity. There is high ambiguity of emotion in text data. In this paper, we consider the sentence-level sentiment classification task, and propose a novel type of convolutional neural network combined with fuzzy logic called the Fuzzy Convolutional Neural Network (FCNN) and its associated learning algorithm. The new model is an integration of modified Convolutional Neural Network (CNN) in the fuzzy logic domain. The proposed model benefits from the use of fuzzy membership degrees to produce more refined outputs, thereby reducing the ambiguities in emotional aspects of sentiment classification. Also it benefits from extracting high-level emotional features due to convolutional neural representation. We compare the performance of our proposed approach with conventional CNN for sentiment classification. The experimental results indicate that the proposed FCNN outperforms the conventional methods for sentiment classification task. [ABSTRACT FROM AUTHOR]

In this study, we classified emotional responses of a user by measuring the physiological response signals with the Emotion Collector, which is a wearable band equipped with a multi-sensor device. We collected physiological signal data to measure the delicate hand movements, heart rate, and skin tension of the user analysis with a focus on picking out fear among the user's emotions. In experiments classifying fear factors into five categories, we were able to identify the characteristics of the user's physiological responses according to the type of fear. We were also able to find the similarities and differences of physiological and psychological results for fear. The Emotion Collector is very easy to use, robust, and suitable for mobile and long-time logging of data. It can easily be integrated into other systems or applications. The system is designed for use in emotion research as well as in everyday affective applications such as user-centered service and content. [ABSTRACT FROM AUTHOR]

Anomaly-based intrusion detection system (IDS) is gaining wide attention from the research community, due to its robustness in detecting and profiling the newly discovered network attacks. Unlike signature-based IDS which solely relying on a set of pre-defined rules through some massive human efforts, anomaly-based IDS utilises the collected network traces in building its own classification model. The classification model can optimised when a large set of network traces is available. The ideal way of pooling the network traces is through database sharing. However, not many organisations are willing to release or share their network databases due to some privacy concerns, i.e. to avoid some kinds of internet traffic behaviour profiling. To address this issue, a number of anonymisation techniques was developed. The main usage of anonymisation techniques is to conceal the potentially sensitive information in the network traces. However, it is also important to ensure the anonymisation techniques are not over abusing the performances of IDS. To do so, the convention way is by using a Snort IDS to measure the number of alarms generated before-and-after an anonymisation solution is applied. However, this approach is infeasible for Anomaly-Based IDS. Thus, an alternative way of using machine learning approach is proposed and explored in this manuscript. Instead of manual evaluation through the usage of Snort IDS, a J48 decision tree (Weka package of C4.5 algorithm) is used. In this manuscript, two anonymisation techniques, (1) black-marker, and (2) bilateral classification are used to hide the value of port numbers; and their before-and-after performances are evaluated through a J48 decision tree. [ABSTRACT FROM AUTHOR]

The adoption of network flow in the domain of Network-based Intrusion Detection System (NIDS) has steadily risen in popularity. Typically, NIDS detects network intrusions by inspecting the contents of every packet. Flow-based approach, however, uses only features derived from aggregated packet headers. In this paper, all publicly accessible and labeled NIDS data sets are explored. Following the advances in deep learning techniques, the performances of Long Short-Term Memory (LSTM) are also presented and compared with various machine learning classifiers. Amongst the reviewed data sets, the models are trained and evaluated on CIDDS-001 flow-based data set. [ABSTRACT FROM AUTHOR]