The green communication which requires the maximum reduction of energy consumption has become a strong need for the new wireless system designs because of the massive increasing number of mobile subscriptions. Thus, in this paper, energy efficiency (EE) maximization problem is considered for multiple-input single-output orthogonal frequency division multiple access (MISO-OFDMA) cellular networks. The

UAVs are increasingly incorporated in a wide range of domains such as disaster management and rescue missions. UAV path planning deals with finding the most optimal or shortest path for UAVs such that minimum energy and resources are utilized. This paper examines the path planning algorithms for UAVs through a literature survey conducted on 139 systematically retrieved articles published in the last

Energy-efficient data transmission is a key challenge in Wireless Sensor Networks (WSNs). Cluster heads (CHs) are often used to relay aggregated data from sensor nodes to a central base station (BS). However, the transmission range of such relay nodes is limited, necessitating multihop communication for forwarding data from edge relays to BS. Further, links may not be created due to the unavailability

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes that works

Ball bonding inspections with human vision are essential in manufacturing processes of semiconductors devices and integrated circuits (ICs). The inspections are an intensive task which involves human labours to detect poor bonds. Prolonged visual inspections cause poor inspection integrity due to eye-fatigue. However, inspections nowadays are mostly conducted manually by humans which cannot satisfy

Echo state network (ESN) is a reservoir computing structure consisting randomly generated hidden layer which enables a rapid learning and extrapolation process. On the other hand, the determination of inputs is still an outstanding question similar to other neural networks. Instead of utilizing the original historical data, wavelet transformations can be used to extract and reflect features with predictability

We consider approximability of two optimization problems called Minimum Open k-Monopoly (Min-Open-k-Monopoly) and Minimum Partial Open k-Monopoly (Min-P-Open-k-Monopoly), where k is a fixed positive integer. The objective, in Min-Open-k-Monopoly, is to find a minimum cardinality vertex set \(S \subseteq V\) in a given graph G = (V,E) such that \(|N(v) \cap S| \geq \frac {1}{2} |N(v)| + k\), for every

In the era of big data, dimensionality reduction plays an extremely important role in many fields driven by machine learning and data mining techniques. The existing information-theoretic feature selection algorithms generally reduce the dimension by selecting the features with maximum class-relevance and minimum redundancy, while relatively overlook the complementary correlation among features and

With the rapid development of intelligent hardware and the Internet of Things, the bandwidth and resource utilization efficiency of related networks need to be improved. At the same time, the corresponding communication spectrum is becoming more and more scarce. At present, cognitive D2D communication technology in 5G network can establish communication link directly through communication equipment

“Age of Information” is a newly introduced metric, getting vivid attention for measuring the freshness of information in real-time networks. This parameter has evolved to guarantee the timely reception of information from the latest status update received by a user from any real-time application. In this paper, we study a centralized, closed-loop, networked controlled industrial wireless sensor-actuator

Simultaneous Wireless Information and Power Transfer (SWIPT) is an emerging field to transmit information and power in IoT network through the same RF signal. Time switching (TS) protocol is more favorable in free space communication than Power Splitting (PS) protocol when the transmitted RF signal is already weak. This is because, transmitted signal loses its power due to attenuation in free space

Popularity and openness have made the Android platform a potential target of malware attacks. The hackers continuously evolve and improve attacking strategies to identify vulnerabilities in different Android versions. Detection and analysis of malware attacks in Android platform pose unique challenges due to the security features as well as resource limitations present in the devices. This paper proposes

In human-machine interfaces (HMI), deep learning (DL) techniques such as convolutional neural networks (CNN), long-short term memory networks (LSTM) and the hybrid CNN-LSTM framework have been exploited for hand kinematics estimation using surface electromyography (sEMG). However, these DL techniques only capture the relationship between sEMG and hand kinematics, but ignores the prior knowledge of

Soft biometrics, although not discriminant enough for person recognition provides additional information that aids traditional person recognition. Initially, attempts were made to integrate appearance-based facial soft biometrics, such as facial marks, skin color, and hair color/style, but more recently behavior-based facial soft biometrics, such as head dynamics, visual speech, and facial expressions

Cornea is the outmost layer of an eye helps to focuses the light rays towards the retinal layer of the eye. The irregular thickness of the corneal layer results in poor focus of light rays over the retinal layer and hence it results in blur vision. Lasik is a surgical procedure made for correcting the irregular thickness of the cornea to certain extent for making the light rays to fall exactly on the

This paper presents graphkit-learn, the first Python library for efficient computation of graph kernels based on linear patterns, able to address various types of graphs. Graph kernels based on linear patterns are thoroughly implemented, each with specific computing methods, as well as two well-known graph kernels based on non-linear patterns for comparative analysis. Since computational complexity

Considerable interest has been recently devoted to the exploitation of brain activity as biometric identifier in automatic recognition systems, with a major focus on data acquired through electroencephalography (EEG). Several researches have in fact confirmed the presence of discriminative characteristics within brain signals recorded while performing specific mental tasks. Yet, to make EEG-based recognition

Signature forgery is at the centre of several fraudulent activities and legal battles. The introduction of 3D signatures, the virtual signing of ones name in the air, has the potential to restrict forgers due to the absence of visual cues that can be easily copied. Existing 3D signature recognition approaches, however, have not leveraged the inherent spatial and temporal information, making it difficult

This paper explores content-based image registration as a means of dealing with and understanding better Electronic Image Stabilization (EIS) in the context of Photo Response Non-Uniformity (PRNU) alignment. A novel and robust solution to extrapolate the transformation relating the different image output formats for a given device model is proposed. This general approach can be adapted to specifically

Automatic lesion detection from computed tomography (CT) scans is an important task in medical diagnosis. However, three frequent properties of medical data make CT lesion detection a challenging task: (1) Scale variance: Large scale variation is across lesion instances. Especially, it is extremely difficult to detect small lesions; (2) Imbalanced data: The data distributions are highly imbalanced

A real-time architecture of medical image semantic segmentation called Fully Convolution dense Dilated Network, is proposed to improve the segmentation efficiency while ensuring high accuracy. Considering low resolution and contrast, interferences of shadows, as well as differences in nodules’ position and size, accurate ultrasound images’ segmentation cannot be obtained easily. Therefore, a novel

The non-overlapping indexing problem is defined as follows: pre-process a given text T[1,n] of length n into a data structure such that whenever a pattern P[1,m] comes as an input, we can efficiently report the largest set of non-overlapping occurrences of P in T. The best-known solution is by Cohen and Porat [ISAAC 2009]. The size of their structure is O(n) words and the query time is optimal O(m+nocc)

An equitable tree-k-coloring of a graph is a vertex k-coloring such that each color class induces a forest and the size of any two color classes differs by at most one. In this work, we show that every interval graph G has an equitable tree-k-coloring for any integer k≥⌈(Δ(G)+1)/2⌉, solving a conjecture of Wu, Zhang and Li (2013) for interval graphs, and furthermore, give a linear-time algorithm for

Supply energy to battery-powered sensor devices by deploying wireless chargers is a promising way to prolong the operation time of wireless sensor networks, and has attracted much attention recently. Existing works focus on maximizing the total received charging power of the network. However, this may face the unbalanced energy allocation problem, which is not beneficial to prolong the operation time

This paper proposes a dynamic approach of specification mining for Propositional Projection Temporal Logic (PPTL). To this end, a pattern library is built to collect some common temporal relation among events. Further, several algorithms of specification mining for PPTL are designed. With our approach, PPTL specifications are mined from a trace set of a target program by using patterns in the library

A well-designed task recommendation framework aims to protect the data quality as well as increase the task execution results. However, current crowdsourcing systems ignore the fact that there are few duplicate task expectations because of the budget limitation in realistic conditions. Besides, a practical crowdsourcing system needs to recommend new tasks without previous knowledge about the concrete

In this paper we propose an algorithm for the approximate k-Nearest-Neighbors problem. According to the existing researches, there are two kinds of approximation criteria. One is the distance criterion, and the other is the recall criterion. All former algorithms suffer the problem that there are no theoretical guarantees for the two approximation criteria. The algorithm proposed in this paper unifies

Influence maximization (IM) is a widely studied problem in social networks, which aims at finding a seed set with limited size that can maximize the expected number of influenced users. However, existing studies haven't considered the matching relationship, which refers to such scenarios that influenced users seek matched partners among the influenced users, such as time matching with friends to watch

DS decomposition plays an important role in set function optimization problem, because there is DS decomposition for any set function. How to design an efficient and effective algorithm to solve maximizing DS decomposition is a heated problem. In this work, we propose a framework called Parameter Conditioned Greedy Algorithm which has a deterministic version and two random versions. In more detail

The stable set associated to a given set S of nonerasing endomorphisms or substitutions is the set of all right infinite words that can be indefinitely desubstituted over S. This notion generalizes the notion of sets of fixed points of morphisms. It is linked to S-adicity and to property preserving morphisms. Two main questions are considered. Which known sets of infinite words are stable sets? Which

An injective k-coloring of a graph G is a mapping f:V(G)→{1,2,…,k} such that for any two vertices v1,v2∈V(G), f(v1)≠f(v2) if N(v1)∩N(v2)≠∅. The injective chromatic number of a graph G, denoted by χi(G), is the smallest integer k such that G has an injective k-coloring. In this paper, we prove that for a Halin graph G, χi(G)≤Δ(G)+2. Moreover, χi(G)≤Δ(G)+1 if Δ(G)≥6. Also, we show that for a triangle-free

The Burrows-Wheeler-Transform (BWT) is a reversible string transformation which plays a central role in text compression and is fundamental in many modern bioinformatics applications. The BWT is a permutation of the characters, which is in general better compressible and allows to answer several different query types more efficiently than the original string. It is easy to see that not every string

Whereas Perl-compatible regular expression matchers typically exhibit some variation of leftmost-greedy semantics, those conforming to the posix standard are prescribed leftmost-longest semantics. However, the posix standard leaves some room for interpretation, and Fowler and Kuklewicz have done experimental work to confirm differences between various posix matchers. The Boost library has an interesting

The game of cops and robbers is a well-known game played on graphs. In this paper we consider the straight-ahead orientations of 4-regular quadrangulations of the torus and the Klein bottle and we prove that their cop number is bounded by a constant. We also show that the cop number of every k-regularly oriented toroidal grid is at most 13.

Range partitioning is a typical and mostly used data partitioning method and has became a core operation in most of big data computing platforms. Given an input L of N data items admitting a total order, the goal of range partitioning is to divide the whole input into k ranges containing the same number of data items. There is a trivial lower bound Ω(N) for the exact partitioning algorithms, since

We consider the Budgeted version of the classical Connected Dominating Set problem (BCDS). Given a graph G and a budget k, we seek a connected subset of at most k vertices maximizing the number of dominated vertices in G. We improve over the previous (1−e−1)/13 state of the art [Khuller, Purohit, and Sarpatwar, SODA 2014] by introducing a new method for performing tree decompositions in the analysis

We investigate the computational complexity of the graph primality testing problem with respect to the direct product (also known as Kronecker, cardinal or tensor product). In [1] Imrich proves that both primality testing and a unique prime factorization can be determined in polynomial time for (finite) connected and nonbipartite graphs. The author states as an open problem how results on the direct

We study the K-Facility Reallocation problem on the real line, where we maintain K facility locations over T stages, based on the stage-dependent locations of n agents. Each agent is connected to the nearest facility at each stage, and the facilities may move from one stage to another, to accommodate different agent locations. The objective is to minimize the connection cost of the agents plus the

We consider 2-players minimization games with very few cost values. Players are risk-averse and play mixed strategies. The players care about minimizing some function other than expectation or minimizing expectation with additional properties: Expectation plus Variance (E+Var), or Extended Sharpe Ratio (ESR), or Expectation (E) with the additional property that Variance is zero (Var=0). These give

We study deterministic mechanism design without money for k-facility location games with envy ratio on a real line segment, where a set of strategic agents report their locations and a social planner locates k facilities for minimizing the envy ratio. The objective of envy ratio, which is defined as the maximum over the ratios between any two agents' utilities, is derived from fair division to measure

Leakage of private information, e.g. the secret keys, has become a serious threat to the security of computing systems. It has become a common requirement that real-world security applications should withstand various leakage attacks, such as side-channel attacks, cold-boot attacks, etc. For example, the above leakage attacks are very common in cloud computing nowadays. Hence, we need a novel method

Production lines in pharmaceutical manufacturing generate numerous heterogeneous data sets from various embedded systems which control the multiple processes of medicine production. Such data sets should arguably ensure end-to-end traceability and data integrity in order to release a medicine batch, which is uniquely identified and tracked by its batch number/code. Consequently, auditable computerised

Let R=𝔽q+u𝔽q, where q is a power of a prime number p and u2=0. A triple cyclic code of length (r,s,t) over R is a set that can be partitioned into three parts that any cyclic shift of the coordinates of the three parts leaves the code invariant. These codes can be viewed as R[x]-submodules of R[x]/〈xr−1〉×R[x]/〈xs−1〉×R[x]/〈xt−1〉. In this paper, we study the generator polynomials and the minimum generating

In the age of increasing mobile and smart connectivity, malware poses an ever evolving threat to individuals, societies and nations. Anti-malware companies are often the first and only line of defense for mobile users. Driven by economic benefits, quantity and complexity of Android malware are increasing, thus making them difficult to detect. Malware authors employ multiple techniques (e.g. code obfuscation

Routing protocols as one of the key communication techniques that intend to enable and maintain robust communication routes in the network, play a significant part in boosting the performance of Wireless Sensor Networks (WSNs). During the last 18 years, researchers proposed several diverse modifications of chain-based routing protocols in WSN as they are effective in balancing the WSN power-consumption

Efficient distribution of tasks in an Internet of Things (IoT) network ensures the fulfillment for all objects to dynamically cooperate with their limited energy, processing and memory capabilities. The main contribution of this paper is threefold. Firstly, we address the task allocation in the IoT as an optimization problem with a new formulation derived from the context of set cover problem. To the

WiFi-based human activity recognition (HAR) aims to recognize human activities in an off-the-shelf manner that only relies on the commercial Wi-Fi devices already installed in environments. The recent trend in HAR research is to train classifiers on top of statistical or deep neural features extracted from channel state information (CSI) data. Unfortunately, existing methods only take into account

The structure of a sentence conveys rich linguistic knowledge and has proven useful for natural language understanding. In this paper, we aim to incorporate syntactical constraints and long-range word dependencies into the sentence encoding procedure using the widely applied Graph Convolutional Network (GCN) and word dependency trees. Existing syntax-aware GCN methods construct the adjacency matrix

Maintenance planning and execution are challenging tasks for every system with complex structure. Interdependent nature of the components that builds up the system may have significant effect on system integrity. While preventive maintenance actions can be carried out in a more planned fashion, corrective actions are more time sensitive as they directly affect the availability of the system. This study

Fuzzy rough set theory is sensitive to noisy samples as the fuzzy approximations are proposed based on sensitive statistics, i.e. minimum and maximum. Here, we develop a robust fuzzy rough set model called probability granular distance-based fuzzy rough sets (PGDFRS), in which the similarity between samples is substituted by that between granules to reduce the impact of noise on the statistical minimum

In multi-view and multi-label learning, each example can be represented by multiple data view features and annotated with a set of discrete non-exclusive labels. Missing label learning is an important branch of multi-label learning, which can handle incomplete labels with annotations. Previous work on multi-label learning with missing labels mainly considered data in a single view representation. Based

Polyadenylation is responsible for regulating mRNA function and translation efficiency. The accurate polyadenylation signal (PAS) recognition is essential for understanding the genome annotation and function. Currently, computational methods mainly rely on human-designed features and do not extract complex discriminative patterns. In this work, we first adopt a shallow neural network to automatically

In this paper, a novel comprehensive learning particle swarm optimization algorithm, which is based on the Bayesian iteration method and named as Bayesian comprehensive learning particle swarm optimization (BCLPSO), is proposed. In the original PSO, the flying direction of each particle is based on its own historical best position and global optimum. This updating mechanism, however, easily falls into

Optimal scheduling of workflows in cloud computing environments is an essential element to maximize the utilization of Virtual Machines (VMs). In practice, scheduling of dependent tasks in a workflow requires distributing the tasks to the available VMs on the cloud. This paper introduces a discrete variation of the Distributed Grey Wolf Optimizer (DGWO) for scheduling dependent tasks to VMs. The scheduling

The management of end-of-life vehicles (ELVs) is currently one of the most important ecological topics. The recycling process has essential importance for the environmental and economic sustainability of the ELV management. Istanbul has the highest rate of car ownership population in Turkey as well as an old vehicle fleet. There is a strong motivation to open an additional ELV recycling facility in

Kernel method is an effective way to solve the problem of nonlinear mode analysis, and its key is the selection or construction of kernel function. This paper firstly induced entropy-like divergence by combining Jensen-Shannon/Bregman divergence with convex function, its mercer kernel function called entropy-like divergence kernel is also constructed. Secondly, an adaptive noise distance based on entropy-like