Smart systems and services aim to facilitate growing urban populations and their prospects of virtual-real social
behaviors, gig economies, factory automation, knowledge-based workforce, integrated societies, modern living,
among many more. To satisfy these objectives, smart systems and services must comprises of a complex set of
features such as security, ease of use and user friendliness, manageability, scalability, adaptivity, intelligent
behavior, and personalization. Recently, artificial intelligence (AI) is realized as a data-driven technology to
provide an efficient knowledge representation, semantic modeling, and can support a cognitive behavior aspect
of the system. In this paper, an integration of AI with the smart systems and services is presented to mitigate
the existing challenges. Several novel researches work in terms of frameworks, architectures, paradigms, and
algorithms are discussed to provide possible solutions against the existing challenges in the AI-based smart
systems and services. Such novel research works involve efficient shape image retrieval, speech signal
processing, dynamic thermal rating, advanced persistent threat tactics, user authentication, and so on.
Dynamic thermal rating technology can effectively improve the thermal load capacity of transmission lines.
However, its availability is limited by the quantity and high cost of the hardware facilities. This paper proposes
a new dynamic thermal rating technology based on global/regional assimilation and prediction system
(GRAPES) and geographic information system (GIS). The paper will also explore the method of obtaining any
point meteorological data along the transmission line by using GRAPES and GIS, and provide the strategy of
extracting and decoding meteorological data. In this paper, the accuracy of numerical weather prediction was
verified from the perspective of time and space. Also, the 750-kV transmission line in Shaanxi Province is
considered as an example to analyze. The results of the study indicate that dynamic thermal rating based on
GRAPES and GIS can fully excavate the line power potential without additional cost on hardware, which saves
a lot of investment.
Shape description is an important and fundamental issue in content-based image retrieval (CBIR), and a
number of shape description methods have been reported in the literature. For shape description, both global
information and local contour variations play important roles. In this paper a new included-angular ternary
pattern (IATP) based shape descriptor is proposed for shape image retrieval. For each point on the shape
contour, IATP is derived from its neighbor points, and IATP has good properties for shape description. IATP
is intrinsically invariant to rotation, translation and scaling. To enhance the description capability, multiscale
IATP histogram is presented to describe both local and global information of shape. Then multiscale IATP
histogram is combined with included-angular histogram for efficient shape retrieval. In the matching stage,
cosine distance is used to measure shape features’ similarity. Image retrieval experiments are conducted on the
standard MPEG-7 shape database and Swedish leaf database. And the shape image retrieval performance of the
proposed method is compared with other shape descriptors using the standard evaluation method. The
experimental results of shape retrieval indicate that the proposed method reaches higher precision at the same
recall value compared with other description method.
In order to deal with the filtering delay problem of least mean square adaptive filter noise reduction algorithm
and music noise problem of spectral subtraction algorithm during the speech signal processing, we combine
these two algorithms and propose one novel noise reduction method, showing a strong performance on par or
even better than state of the art methods. We first use the least mean square algorithm to reduce the average
intensity of noise, and then add spectral subtraction algorithm to reduce remaining noise again. Experiments
prove that using the spectral subtraction again after the least mean square adaptive filter algorithm overcomes
shortcomings which come from the former two algorithms. Also the novel method increases the signal-to-noise
ratio of original speech data and improves the final noise reduction performance.
The smart city is one of the most promising, prominent, and challenging applications of the Internet of Things
(IoT). Smart cities rely on everything connected to each other. This in turn depends heavily on technology.
Technology literacy is essential to transform a city into a smart, connected, sustainable, and resilient city where
information is not only available but can also be found. The smart city vision combines emerging technologies
such as edge computing, blockchain, artificial intelligence, etc. to create a sustainable ecosystem by dramatically
reducing latency, bandwidth usage, and power consumption of smart devices running various applications. In
this research, we present a comprehensive survey of emerging technologies for a sustainable smart city network.
We discuss the requirements and challenges for a sustainable network and the role of heterogeneous integrated
technologies in providing smart city solutions. We also discuss different network architectures from a security
perspective to create an ecosystem. Finally, we discuss the open issues and challenges of the smart city network
and provide suitable recommendations to resolve them.
Due to the view point, illumination, personal gait and other background situation, person re-identification
across cameras has been a challenging task in video surveillance area. In order to address the problem, a novel
method called Joint Bayesian across different cameras for person re-identification (JBR) is proposed. Motivated
by the superior measurement ability of Joint Bayesian, a set of Joint Bayesian matrices is obtained by learning
with different camera pairs. With the global Joint Bayesian matrix, the proposed method combines the
characteristics of multi-camera shooting and person re-identification. Then this method can improve the
calculation precision of the similarity between two individuals by learning the transition between two cameras.
For investigating the proposed method, it is implemented on two compare large-scale re-ID datasets, the
Market-1501 and DukeMTMC-reID. The RANK-1 accuracy significantly increases about 3% and 4%, and the
maximum a posterior (MAP) improves about 1% and 4%, respectively.
Internet of Things (IoT) is the paradigm of network of Internet-connected things as objects that constantly
sense the physical world and share the data for further processing. At the core of IoT lies the early technology
of radio frequency identification (RFID), which provides accurate location tracking of real-world objects. With
its small size and convenience, RFID tags can be attached to everyday items such as books, clothes, furniture
and the like as well as to animals, plants, and even humans. This phenomenon is the beginning of new
applications and services for the industry and consumer market. IoT is regarded as a fourth industrial
revolution because of its massive coverage of services around the world from smart homes to artificial
intelligence-enabled smart driving cars, Internet-enabled medical equipment, etc. It is estimated that there will
be several dozens of billions of IoT devices ready and operating until 2020 around the world. Despite the
growing statistics, however, IoT has security vulnerabilities that must be addressed appropriately to avoid
causing damage in the future. As such, we mention some fields of study as a future topic at the end of the survey.
Consequently, in this comprehensive survey of IoT, we will cover the architecture of IoT with various layered
models, security characteristics, potential applications, and related supporting technologies of IoT such as 5G,
MEC, cloud, WSN, etc., including the economic perspective of IoT and its future directions.
Estimation of accurate blood volume flow in ultrasound Doppler blood flow spectrograms is extremely
important for clinical diagnostic purposes. Blood volume flow measurements require the assessment of both
the velocity distribution and the cross-sectional area of the vessel. Unfortunately, the existing volume flow
estimation algorithms by ultrasound lack the velocity space distribution information in cross-sections of a
vessel and have the problems of low accuracy and poor stability. In this paper, a new robust ultrasound volume
flow estimation method based on multigate (RMG) is proposed and the multigate technology provides detail
information on the local velocity distribution. In this method, an accurate double iterative flow velocity
estimation algorithm (DIV) is used to estimate the mean velocity and it has been tested on in vivo data from
carotid. The results from experiments indicate a mean standard deviation of less than 6% in flow velocities
when estimated for a range of SNR levels. The RMG method is validated in a custom-designed experimental
setup, Doppler phantom and imitation blood flow control system. In vitro experimental results show that the
mean error of the RMG algorithm is 4.81%. Low errors in blood volume flow estimation make the prospect of
using the RMG algorithm for real-time blood volume flow estimation possible.
In real time applications, due to their effective cost and small size, wireless networks play an important role in
receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed.
Due to various internal and external factors, networks can change dynamically, which impacts the localisation
of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault
detection, and quality of service, among others. Conventional methods were programmed, for static networks
which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied
for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less
human intervention and reprogramming. In this paper, we present a wireless networks survey based on
different machine learning algorithms and network lifetime parameters, and include the advantages and
drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion,
synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation
of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief
discussion on the challenges inherent in this area of research.
Microblogging services (such as Twitter) are the representative information communication networks during
the Web 2.0 era, which have gained remarkable popularity. Weibo has become a popular platform for
information dissemination in online social networks due to its large number of users. In this study, a microblog
information dissemination model is presented. Related concepts are introduced and analyzed based on the
dynamic model of infectious disease, and new influencing factors are proposed to improve the susceptibleinfective-
removal (SIR) information dissemination model. Correlation analysis is conducted on the existing
information dissemination risk and the rumor dissemination model of microblog. In this study, web hyper is
used to model rumor dissemination. Finally, the experimental results illustrate the effectiveness of the method
in reducing the rumor dissemination of microblogs.
The need for cyber resilience is increasingly important in our technology-dependent society where computing
devices and data have been, and will continue to be, the target of cyber-attackers, particularly advanced
persistent threat (APT) and nation-state/sponsored actors. APT and nation-state/sponsored actors tend to be
more sophisticated, having access to significantly more resources and time to facilitate their attacks, which in
most cases are not financially driven (unlike typical cyber-criminals). For example, such threat actors often
utilize a broad range of attack vectors, cyber and/or physical, and constantly evolve their attack tactics. Thus,
having up-to-date and detailed information of APT’s tactics, techniques, and procedures (TTPs) facilitates the
design of effective defense strategies as the focus of this paper. Specifically, we posit the importance of
taxonomies in categorizing cyber-attacks. Note, however, that existing information about APT attack
campaigns is fragmented across practitioner, government (including intelligence/classified), and academic
publications, and existing taxonomies generally have a narrow scope (e.g., to a limited number of APT
campaigns). Therefore, in this paper, we leverage the Cyber Kill Chain (CKC) model to “decompose” any
complex attack and identify the relevant characteristics of such attacks. We then comprehensively analyze more
than 40 APT campaigns disclosed before 2018 to build our taxonomy. Such taxonomy can facilitate incident
response and cyber threat hunting by aiding in understanding of the potential attacks to organizations as well
as which attacks may surface. In addition, the taxonomy can allow national security and intelligence agencies
and businesses to share their analysis of ongoing, sensitive APT campaigns without the need to disclose detailed
information about the campaigns. It can also notify future security policies and mitigation strategy formulation.
The single carrier frequency domain equalization (SC-FDE) technology is an important part of the broadband
wireless access communication system, which can effectively combat the frequency selective fading in the
wireless channel. In SC-FDE communication system, the accuracy of timing synchronization directly affects
the performance of the SC-FDE system. In this paper, on the basis of Schmidl timing synchronization
algorithm a timing synchronization algorithm suitable for FPGA (field programmable gate array) implementation
is proposed. In the FPGA implementation of the timing synchronization algorithm, the sliding window
accumulation, quantization processing and amplitude reduction techniques are adopted to reduce the
complexity in the implementation of FPGA. The simulation results show that the algorithm can effectively
realize the timing synchronization function under the condition of reducing computational complexity and
Many security systems rely solely on solutions based on Artificial Intelligence, which are weak in nature. These
security solutions can be easily manipulated by malicious users who can gain unlawful access. Some security
systems suggest using fingerprint-based solutions, but they can be easily deceived by copying fingerprints with
clay. Image-based security is undoubtedly easy to manipulate, but it is also a solution that does not require any
special training on the part of the user. In this paper, we propose a multi-factor security framework that operates
in a three-step process to authenticate the user. The motivation of the research lies in utilizing commonly
available and inexpensive devices such as onsite CCTV cameras and smartphone camera and providing fully
secure user authentication. We have used technologies such as Argon2 for hashing image features and physically
unclonable identification for secure device-server communication. We also discuss the methodological workflow
of the proposed multi-factor authentication framework. In addition, we present the service scenario of the
proposed model. Finally, we analyze qualitatively the proposed model and compare it with state-of-the-art
methods to evaluate the usability of the model in real-world applications.
To figure out the impact of debt financing on the profits of industrial enterprises, it starts with calculating the
first differences against the logarithms of the cost profit ratios and the debt asset ratios of Chinese industrial
enterprises during 179 months from 2002 to 2016; next, it runs the cointegration test and afterwards the
regression test to analyze the obtained first differences, and still next uses the Simulink software to get the
regularity of those changes. It finds out that there is not only a long-term stable relationship between the
enterprises’ profits and debts, but also a steady time series trend within a short term. The profit rate positively
correlates to the debt asset ratio, and profit for the current term positively correlates to the profit for the
previous term. It indicates that properly raised debts can help increase the profit rate of the industrial
enterprises, and a higher previous profit level can help improve the current profit level.
Documents contain information that can be used for various applications, such as question answering (QA)
system, information retrieval (IR) system, and recommendation system. To use the information, it is necessary
to develop a method of extracting such information from the documents written in a form of natural language.
There are several kinds of the information (e.g., temporal information, spatial information, semantic role
information), where different kinds of information will be extracted with different methods. In this paper, the
existing studies about the methods of extracting the temporal information are reported and several related
issues are discussed. The issues are about the task boundary of the temporal information extraction, the history
of the annotation languages and shared tasks, the research issues, the applications using the temporal
information, and evaluation metrics. Although the history of the tasks of temporal information extraction is
not long, there have been many studies that tried various methods. This paper gives which approach is known
to be the better way of extracting a particular part of the temporal information, and also provides a future
This paper studies a novel approach to natural gait cycles based gait recognition via kernel Fisher discriminant
analysis (KFDA), which can effectively calculate the features from gait sequences and accelerate the recognition
process. The proposed approach firstly extracts the gait silhouettes through moving object detection and
segmentation from each gait videos. Secondly, gait energy images (GEIs) are calculated for each gait videos, and
used as gait features. Thirdly, KFDA method is used to refine the extracted gait features, and low-dimensional
feature vectors for each gait videos can be got. The last is the nearest neighbor classifier is applied to classify.
The proposed method is evaluated on the CASIA and USF gait databases, and the results show that our
proposed algorithm can get better recognition effect than other existing algorithms.
Bug report processing is a key element of bug fixing in modern software maintenance. Bug reports are not
processed immediately after submission and involve several processes such as bug report deduplication and
bug report triage before bug fixing is initiated; however, this method of bug fixing is very inefficient because all
these processes are performed manually. Software engineers have persistently highlighted the need to automate
these processes, and as a result, many automation techniques have been proposed for bug report processing;
however, the accuracy of the existing methods is not satisfactory. Therefore, this study focuses on surveying to
improve the accuracy of existing techniques for bug report processing. Reviews of each method proposed in
this study consist of a description, used techniques, experiments, and comparison results. The results of this
study indicate that research in the field of bug deduplication still lacks and therefore requires numerous studies
that integrate clustering and natural language processing. This study further indicates that although all studies
in the field of triage are based on machine learning, results of studies on deep learning are still insufficient.
Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge.
Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems.
To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern
data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to
cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by
utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO
algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an
improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population
search capability and accelerate the convergence speed. Experimental results show the effectiveness and
superiority of the proposed clustering method.
Pedestrian tracking is a particular object tracking problem and an important component in various visionbased
applications, such as autonomous cars and surveillance systems. Following several years of development,
pedestrian tracking in videos remains challenging, owing to the diversity of object appearances and surrounding
environments. In this research, we proposed a tracking-by-detection system for pedestrian tracking, which
incorporates a convolutional neural network (CNN) and color information. Pedestrians in video frames are
localized using a CNN-based algorithm, and then detected pedestrians are assigned to their corresponding
tracklets based on similarities between color distributions. The experimental results show that our system is
able to overcome various difficulties to produce highly accurate tracking results.
The 2nd Journal of Information Processing Systems Awards
"Block-VN: A Distributed Blockchain Based Vehicular Network Architecture in Smart City"
Pradip Kumar Sharma, Seo Yeon Moon and Jong Hyuk Park (Seoul National University of Science and Technology, Korea)
Publication (Corresponding Author)
Chengyou Wang (Shangdong University, China)