Biblio

Populations move across regions in search of better living possibilities, better life outcomes or going away from problems that affected their lives in the previous region they lived in. In the United States of America, this problem has been happening over decades. Intelligent Conversational Text-based Agents, also called Chatbots, and Artificial Intelligence are increasingly present in our lives and over recent years, their presence has increased considerably, due to the usability cases and the familiarity they are wining constantly. Using NLP algorithms for law in accessible platforms allows scaling of users to access a certain level of law expert who could assist users in need. This paper describes the motivation and circumstances of this problem as well as the description of the development of an Intelligent Conversational Agent system that was used by immigrants in the USA so they could get answers to questions and get suggestions about better legal options they could have access to. This system has helped thousands of people, especially in California

Next Word Prediction involves guessing the next word which is most likely to come after the current word. The system suggests a few words. A user can choose a word according to their choice from a list of suggested word by system. It increases typing speed and reduces keystrokes of the user. It is also useful for disabled people to enter text slowly and for those who are not good with spellings. Previous studies focused on prediction of the next word in different languages. Some of them are Bangla, Assamese, Ukraine, Kurdish, English, and Hindi. According to Census 2011, 43.63% of the Indian population uses Hindi, the national language of India. In this work, deep learning techniques are proposed to predict the next word in Hindi language. The paper uses Long Short Term Memory and Bidirectional Long Short Term Memory as the base neural network architecture. The model proposed in this work outperformed the existing approaches and achieved the best accuracy among neural network based approaches on IITB English-Hindi parallel corpus.

This paper presents a program-code mutation technique that is applied in-field to embedded systems in order to create diversity in a population of systems that are identical at the time of their deployment. With this diversity, it becomes more difficult for attackers to carry out the very popular Return-Oriented-Programming (ROP) attack in a large scale, since the gadgets in different systems are located at different program addresses after code permutation. In order to prevent the system from a system crash after a failed ROP attack, we further propose the combination of the code mutation with a return address checking. We will report the overhead in time and memory along with a security analysis.

As a core problem of multi-agent systems, multiagent pathfinding has an important impact on the efficiency of multi-agent systems. Because of this, many novel multi-agent pathfinding methods have been proposed over the years. However, these methods have focused on different agents with different goals for research, and less research has been done on scenarios where different agents have the same goal. We propose a multiagent pathfinding method incorporating a multi-objective gray wolf optimization algorithm to solve the multi-agent pathfinding problem with the same objective. First, constrained optimization modeling is performed to obtain objective functions about agent wholeness and security. Then, the multi-objective gray wolf optimization algorithm is improved for solving the constrained optimization problem and further optimized for scenarios with insufficient computational resources. To verify the effectiveness of the multi-objective gray wolf optimization algorithm, we conduct experiments in a series of simulation environments and compare the improved multi-objective grey wolf optimization algorithm with some classical swarm intelligence optimization algorithms. The results show that the multi-agent pathfinding method incorporating the multi-objective gray wolf optimization algorithm is more efficient in handling multi-agent pathfinding problems with the same objective.

Differential evolution algorithm is an efficient computational method that uses population crossover and variation to achieve high-quality solutions. The algorithm is simple in principle and fast in solving global solutions, so it has been widely used in complex optimization problems. In this paper, we applied the differential evolution algorithm to a time-delay dynamic system for microbial fermentation of 1,3-propanediol and obtained an average error of 22.67% comparing to baseline error of 48.53%.

Crypto-ransomware is a malware that uses the system’s cryptography functions to encrypt user data. The irreversible effect of crypto-ransomware makes it challenging to survive the attack compared to other malware categories. When a crypto-ransomware attack encrypts user files, it becomes difficult to access these files without having the decryption key. Due to the availability of ransomware development tool kits like Ransomware as a Service (RaaS), many ransomware variants are being developed. This contributes to the rise of ransomware attacks witnessed nowadays. However, the conventional approaches employed by malware detection solutions are not suitable to detect ransomware. This is because ransomware needs to be detected as early as before the encryption takes place. These attacks can effectively be handled only if detected during the pre-encryption phase. Early detection of ransomware attacks is challenging due to the limited amount of data available before encryption. An adaptive pre-encryption model is proposed in this paper which is expected to deal with the population concept drift of crypto-ransomware given the limited amount of data collected during the pre-encryption phase of the attack lifecycle. With such adaptability, the model can maintain up-to-date knowledge about the attack behavior and identify the polymorphic ransomware that continuously changes its behavior.

In Particle Swarm Optimization Algorithm (PSO), the learning factors \$c\_1\$ and \$c\_2\$ are used to update the speed and location of a particle. However, the setting of those two important parameters has great effect on the performance of the PSO algorithm, which has limited its range of applications. To avoid the tedious parameter tuning, we introduce a transfer learning based adaptive parameter setting strategy to PSO in this paper. The proposed transfer learning strategy can adjust the two learning factors more effectively according to the environment change. The performance of the proposed algorithm is tested on sets of widely-used benchmark multi-objective test problems for DTLZ. The results comparing and analysis are conduced by comparing it with the state-of-art evolutionary multi-objective optimization algorithm NSGA-III to verify the effectiveness and efficiency of the proposed method.

Particle filtering is an indispensable method for non-Gaussian state estimation, but it has some problems, such as particle degradation and requiring a large number of particles to ensure accuracy. Biota intelligence algorithms led by Cuckoo (CS) and Firefly (FA) have achieved certain results after introducing particle filtering, respectively. This paper respectively in the two kinds of bionic algorithm convergence factor and adaptive step length and random mobile innovation, seized the cuckoo algorithm (CS) in the construction of the initial value and the firefly algorithm (FA) in the iteration convergence advantages, using the improved after the update mechanism of cuckoo algorithm optimizing the initial population, and will be updated after optimization way of firefly algorithm combined with particle filter. Experimental results show that this method can ensure the diversity of particles and greatly reduce the number of particles needed for prediction while improving the filtering accuracy.

Mining, the process where multiple miners compete to add blocks to Proof-of-Work (PoW) blockchains, is of great importance to maintain the tamper-resistance feature of blockchains. In current blockchain networks, miners usually form groups, called mining pools, to improve their revenues. When multiple pools exist, a fundamental mining pool selection problem arises: which pool should each miner join to maximize its revenue? In addition, the existence of mining pools also leads to another critical issue, i.e., Block WithHolding (BWH) attack, where a pool sends some of its miners as spies to another pool to gain extra revenues without contributing to the mining of the infiltrated pool. This paper therefore aims to investigate the mining pool selection issue (i.e., the stable population distribution of miners in the pools) in the presence of BWH attack from the perspective of evolutionary game theory. We first derive the expected revenue density of each pool to determine the expected payoff of miners in that pool. Based on the expected payoffs, we formulate replicator dynamics to represent the growth rates of the populations in all pools. Using the replicator dynamics, we obtain the rest points of the growth rates and discuss their stability to identify the Evolutionarily Stable States (ESSs) (i.e., stable population distributions) of the game. Simulation and numerical results are also provided to corroborate our analysis and to illustrate the theoretical findings.

Over the past few years, malicious hardware modifications, a.k.a. hardware Trojans (HT), have emerged as a major security threat because integrated circuit (IC) companies have been fabricating chips at offshore foundries due to various factors including time-to-market, cost reduction demands, and the increased complexity of ICs. Among proposed hardware Trojan detection techniques, reverse engineering appears to be the most accurate and reliable one because it works for all circuits and Trojan types without a golden example of the chip. However, because reverse engineering is an extremely expensive, time-consuming, and destructive process, it is difficult to apply this technique for a large population of ICs in a real test environment. This paper proposes a novel golden-chip-free clustering method using backscattering side-channel to divide ICs into groups of Trojan-free and Trojan-infected boards. The technique requires no golden chip or a priori knowledge of the chip circuitry, and divides a large population of ICs into clusters based on how HTs (if existed) affect their backscattered signals. This significantly reduces the size of test vectors for reverse engineering based detection techniques, thus enables deployment of reverse engineering approaches to a large population of ICs in a real testing scenario. The results are collected on 100 different FPGA boards where boards are randomly chosen to be infected or not. The results show that we can cluster the boards with 100% accuracy and demonstrate that our technique can tolerate manufacturing variations among hardware instances to cluster all the boards accurately for 9 different dormant Trojan designs on 3 different benchmark circuits from Trusthub. We have also shown that we can detect dormant Trojan designs whose trigger size has shrunk to as small as 0.19% of the original circuit with 100% accuracy as well.

mHealth apps have a vital role in facilitation of human health management. Users have to enter sensitive health related information in these apps to fully utilize their functionality. Unauthorized sharing of sensitive health information is undesirable by the users. mHealth apps also collect data other than that required for their functionality like surfing behavior of a user or hardware details of devices used. mHealth software and their developers also share such data with third parties for reasons other than medical support provision to the user, like advertisements of medicine and health insurance plans. Existence of a comprehensive and easy to understand data privacy policy, on user data acquisition, sharing and management is a salient requirement of modern user privacy protection demands. Readability is one parameter by which ease of understanding of privacy policy is determined. In this research, privacy policies of 27 free Android, medical apps are analyzed. Apps having user rating of 4.0 and downloads of 1 Million or more are included in data set of this research.RGL, Flesch-Kincaid Reading Grade Level, SMOG, Gunning Fox, Word Count, and Flesch Reading Ease of privacy policies are calculated. Average Reading Grade Level of privacy policies is 8.5. It is slightly greater than average adult RGL in the US. Free mHealth apps have a large number of users in other, less educated parts of the World. Privacy policies with an average RGL of 8.5 may be difficult to comprehend in less educated populations.

With the concept of “wireless city”, 3G, WIFI and other wireless network coverages have become more extensive. Data transmission rate has achieved a qualitative leap, providing feasibility for the implementation of mobile video surveillance solutions. The mobile video monitoring system based on the bee population optimization algorithm proposed in this paper makes up for the defects of traditional network video surveillance, and according to the video surveillance system monitoring command, the optimal visual effect of the current state of the observed object can be rendered quickly and steadily through the optimization of the camera linkage model and simulation analysis.

Pushed by market forces, software development has become fast-paced. As a consequence, modern development projects are assembled from 3rd-party components. Security & privacy assurance techniques once designed for large, controlled updates over months or years, must now cope with small, continuous changes taking place within a week, and happening in sub-components that are controlled by third-party developers one might not even know they existed. In this paper, we aim to provide an overview of the current software security approaches and evaluate their appropriateness in the face of the changed nature in software development. Software security assurance could benefit by switching from a process-based to an artefact-based approach. Further, security evaluation might need to be more incremental, automated and decentralized. We believe this can be achieved by supporting mechanisms for lightweight and scalable screenings that are applicable to the entire population of software components albeit there might be a price to pay.

This study focuses on identifying the factors contributing to a sense of personal responsibility that could improve understanding of insecure cybersecurity behavior and guide research toward more effective messaging targeting non-adopting populations. Towards that, we ran a 2(account type) x2(usage scenario) x2(message type) between-group study with 237 United States adult participants on Amazon MTurk, and investigated how the non-adopting population allocates blame, and under what circumstances they blame the end user among the parties who hold responsibility: the software companies holding data, the attackers exposing data, and others. We find users primarily hold service providers accountable for breaches but they feel the same companies should not enforce stronger security policies on users. Results indicate that people do hold end users accountable for their behavior in the event of a breach, especially when the users' behavior affects others. Implications of our findings in risk communication is discussed in the paper.

With the rapid development of smart grids and the continuous deepening of informatization, while realizing remote telemetry and remote control of massive data-based grid operation, electricity information network security problems have become more serious and prominent. A method for electricity information network security situation prediction method based on improved deep belief network is proposed in this paper. Firstly, the affinity propagation clustering algorithm is used to determine the depth of the deep belief network and the number of hidden layer nodes based on sample parameters. Secondly, continuously adjust the scaling factor and crossover probability in the differential evolution algorithm according to the population similarity. Finally, a chaotic search method is used to perform a second search for the best individuals and similarity centers of each generation of the population. Simulation experiments show that the proposed algorithm not only enhances the generalization ability of electricity information network security situation prediction, but also has higher prediction accuracy.

In this work, novel metaheuristic algorithms are proposed to address the network coding (NC)-based routing and spectrum allocation (RSA) problem in elastic optical networks, aiming to increase the level of security against eavesdropping attacks for the network's confidential connections. A modified simulated annealing, a genetic algorithm, as well as a combination of the two techniques are examined in terms of confidentiality and spectrum utilization. Performance results demonstrate that using metaheuristic techniques can improve the performance of NC-based RSA algorithms and thus can be utilized in real-world network scenarios.

In this work, we propose a new concept of multiple support thresholds to sanitize the database for specific sensitive itemsets. The proposed method assigns a stricter threshold to the sensitive itemset for data sanitization. Furthermore, a genetic-algorithm (GA)-based model is involved in the designed algorithm to minimize side effects. In our experimental results, the GA-based PPDM approach is compared with traditional compact GA-based model and results clearly showed that our proposed method can obtain better performance with less computational cost.

Security services and share information is provided by the computer network. The computer network is by default there is not security. The Attackers can use this provision to hack and steal private information. Confidentiality, creation, changes, and truthful of data is will be big problems in the network. Many types of research have given many methods regarding this, from these methods Generating Initial Chromosome Key called Generating Dynamic Chromosome Key (GDCK), which is a novel approach. With the help of the RSA (Rivest Shamir Adleman) algorithm, GICK and GDCK have created an initial key. The proposed method has produced new techniques using genetic fitness function for the sender and receiver. The outcome of GICK and GDCK has been verified by NIST (National Institute of Standards Technology) tools and analyzes randomness of auto-generated keys with various methods. The proposed system has involved three examines; it has been yield better P-Values 6.44, 7.05, and 8.05 while comparing existing methods.

These days the digitization process is everywhere, spreading also across central governments and local authorities. It is hoped that, using open government data for scientific research purposes, the public good and social justice might be enhanced. Taking into account the European General Data Protection Regulation recently adopted, the big challenge in Portugal and other European countries, is how to provide the right balance between personal data privacy and data value for research. This work presents a sensitivity study of data anonymization procedure applied to a real open government data available from the Brazilian higher education evaluation system. The ARX k-anonymization algorithm, with and without generalization of some research value variables, was performed. The analysis of the amount of data / information lost and the risk of re-identification suggest that the anonymization process may lead to the under-representation of minorities and sociodemographic disadvantaged groups. It will enable scientists to improve the balance among risk, data usability, and contributions for the public good policies and practices.

For solving multi-objective optimization problems, this paper firstly combines a multi-objective evolutionary algorithm based on decomposition (MOEA/D) with good convergence and non-dominated sorting genetic algorithm II (NSGA-II) with good distribution to construct. Thus we propose a hybrid multi-objective optimization solving algorithm. Then, we consider that the population diversity needs to be improved while applying multi-objective particle swarm optimization (MOPSO) to solve the multi-objective optimization problems and an improved MOPSO algorithm is proposed. We give the distance function between the individual and the population, and the individual with the largest distance is selected as the global optimal individual to maintain population diversity. Finally, the simulation experiments are performed on the ZDT\textbackslashtextbackslashDTLZ test functions and track planning problems. The results indicate the better performance of the improved algorithms.

Particle swarm optimization (PSO), as a kind of swarm intelligence algorithm, has the advantages of simple algorithm principle, less programmable parameters and easy programming. Many scholars have applied particle swarm optimization (PSO) to various fields through learning it, and successfully solved linear problems, nonlinear problems, multiobjective optimization and other problems. However, the algorithm also has obvious problems in solving problems, such as slow convergence speed, too early maturity, falling into local optimization in advance, etc., which makes the convergence speed slow, search the optimal value accuracy is not high, and the optimization effect is not ideal. Therefore, many scholars have improved the particle swarm optimization algorithm. Taking into account the improvement ideas proposed by scholars in the early stage and the shortcomings still existing in the improvement, this paper puts forward the idea of improving particle swarm optimization algorithm in the future.

In this thesis, an modified algorithm for grey wolf optimization in swarm intelligence optimization algorithm is proposed, which is called an adaptive grey wolf algorithm (AdGWO) based on population system and bacterial foraging optimization algorithm (BFO). In view of the disadvantages of premature convergence and local optimization in solving complex optimization problems, the AdGWO algorithm uses a three-stage nonlinear change function to simulate the decreasing change of the convergence factor, and at the same time integrates the half elimination mechanism of the BFO. These improvements are more in line with the actual situation of natural wolves. The algorithm is based on 23 famous test functions and compared with GWO. Experimental results demonstrate that this algorithm is able to avoid sinking into the local optimum, has good accuracy and stability, is a more competitive algorithm.

In this work, a new particle swarm optimization (PSO)-based optimization algorithm, and the idea of a running match is introduced and employed in a non-linear system PID controller design. This algorithm aims to modify the formula of velocity calculating of the general PSO method to increase the diversity of the searching process. In this process of designing an optimal PID controller for a non-linear system, the three gains of the PID controller form a particle, which is a parameter vector and will be updated iteratively. Many of those particles then form a population. To reach the PID gains which are optimum, using modified velocity updating formula and position updating formula, the position of all particles of the population will be moved into the optimization direction. In the meanwhile, an objective function may be minimized as the performance of the controller get improved. To corroborate the controller functioning of this method, a non-linear system known as inverted pendulum will be controlled by the designed PID controller. The results confirm that the new method can show excellent performance in the non-linear PID controller design task.

Groups often struggle to reach decisions, especially when populations are strongly divided by conflicting views. Traditional methods for collective decision-making involve polling individuals and aggregating results. In recent years, a new method called Artificial Swarm Intelligence (ASI) has been developed that enables networked human groups to deliberate in real-time systems, moderated by artificial intelligence algorithms. While traditional voting methods aggregate input provided by isolated participants, Swarm-based methods enable participants to influence each other and converge on solutions together. In this study we compare the output of traditional methods such as Majority vote and Borda count to the Swarm method on a set of divisive policy issues. We find that the rankings generated using ASI and the Borda Count methods are often rated as significantly more satisfactory than those generated by the Majority vote system (p\textbackslashtextless; 0.05). This result held for both the population that generated the rankings (the “in-group”) and the population that did not (the “out-group”): the in-group ranked the Swarm prioritizations as 9.6% more satisfactory than the Majority prioritizations, while the out-group ranked the Swarm prioritizations as 6.5% more satisfactory than the Majority prioritizations. This effect also held even when the out-group was subject to a demographic sampling bias of 10% (i.e. the out-group was composed of 10% more Labour voters than the in-group). The Swarm method was the only method to be perceived as more satisfactory to the “out-group” than the voting group.

With the increasing performance of neural networks on many security-critical tasks, the security concerns of machine learning have become increasingly prominent. Recent studies have shown that neural networks are vulnerable to adversarial examples: carefully crafted inputs with negligible perturbations on legitimate samples could mislead a neural network to produce adversary-selected outputs while humans can still correctly classify them. Therefore, we need an additional measurement on the trustworthiness of the results of a machine learning model, especially in adversarial settings. In this paper, we analyse the root cause of adversarial examples, and propose a new property, namely fidelity, of machine learning models to describe the gap between what a model learns and the ground truth learned by humans. One of its benefits is detecting adversarial attacks. We formally define fidelity, and propose a novel approach to quantify it. We evaluate the quantification of fidelity in adversarial settings on two neural networks. The study shows that involving the fidelity enables a neural network system to detect adversarial examples with true positive rate 97.7%, and false positive rate 1.67% on a studied neural network.