Reviewerly/exHarmony huggingface dataset:
Blockchain-based Information Sharing between Smart Vehicles for Safe Driving

Smart vehicles determine and take various actions with state information with little or no human intervention. More information can be gathered when they can connect to and communicate with other vehicles and their environments, improving safe self-driving. This paper proposes a blockchain-based information sharing that verifies the shared data and the sharing process using a public blockchain, Ethereum. To protect the privacy of the shared information, cryptography and secure protocols are additionally applied to the blockchain technology.
Evoking emotions in virtual reality: schema activation via a freeze-frame stimulus

Abstract Virtual reality can be used for educational purposes, particularly in demanding professions such as firefighting. Such virtual training may be useful for preparing trainees for distress, fear, or frustration experienced during real rescue operations. Evoking cognitive schemas, especially of other people, during training appears to be crucial as well, as the greatest stressors in the firefighting profession are social. Based on interviews with firefighters, two types of people (children and young women) were chosen as stimuli in the research design. In an experimental study with three iterations, the stimuli designed to evoke the schema of a significant other were implemented in a virtual reality simulator to evoke a cognitive schema in firefighters through emotions (positive and negative) and several dimensions of stress. The first iteration of the study did not yield expected results, as the stimulus (a child’s toy) was not as suggestive and vivid as it was expected. In the second attempt, the stimulus was improved and evoked feelings of challenge, harm, and loss in the participants. In the third iteration, the stimulus was changed once more (to a white dress) and this time it evoked negative emotions of fear, anger, guilt, and sadness. However, after correcting for multiple comparisons, only results regarding emotional response remained statistically significant. The results are discussed in light of cognitive schemas’ activation, and perspectives for further research in this scope are proposed. Due to research outcomes, the issues of manipulation checks in experimental psychology and limitations of the VR technology are taken into consideration.
Improving the Linear Programming Technique in the Search for Lower Bounds in Secret Sharing

We present a new improvement in the linear programming technique to derive lower bounds on the information ratio of secret sharing schemes. We obtain non-Shannon-type bounds without using information inequalities explicitly. Our new technique makes it possible to determine the optimal information ratio of linear secret sharing schemes for all access structures on 5 participants and all graph-based access structures on 6 participants. In addition, new lower bounds are presented also for some small matroid ports and, in particular, the optimal information ratios of the linear secret sharing schemes for the ports of the Vamos matroid are determined.
Edge-Based Traffic Flow Data Collection Method Using Onboard Monocular Camera

Traffic data collection is the fundamental step in most applications of intelligent transportation systems (ITS). Recently, traffic data collection methods have become more robust and diversified, yet still have some limitations in their flexibility and coverage. Onboard monocular cameras have considerable potential to be turned into cost-effective moving traffic sensors combining the low cost and ego-vehicles’ high mobility. Existing studies have explored the feasibility of onboard cameras for scene understanding, etc. However, few studies have been conducted to utilize onboard monocular cameras for traffic flow data collection. To this end, this paper puts forward a method using the onboard monocular camera to collect traffic data. The basic structure is composed of a you-only-look-once (YOLO) model and spatial transformer network (STN) to detect vehicles in real-time. Then the traffic flow parameters are computed via fundamental optic and traffic flow theories. The experiment results show its reliability and similar sensing accuracy with inductive loop detectors on the road segment detection. In addition, the STN-YOLO model has a higher vehicle detection accuracy than the original YOLO model under complicated conditions.
Face Liveness Detection Algorithm based on Livenesslight Network

Face liveness detection is the first stage of the whole face detection technology, and it is of great significance to the system security using face recognition technology. In this paper we use convolutional neural networks to extract facial features. Compared to ResNet, SqueezeNet and VGG Face networks, the network structure is lighter and the model training takes less time. After testing, it achieved an accuracy of 99.5% on the Nanjing University of Aeronautics and Astronautics (NUAA) face detection data set of China Southern Airlines. In order to improve the robustness of the model, we built our own dataset through collecting pictures from internet and camera shooting, and horizontally compared the accuracy of the above several models on the self-built dataset. Finally the model proposed in this paper achieved the highest 99.02% accuracy and achieved the best results in actual testing.
Ubiquitous Computing and Distributed Machine Learning in Smart Cities

The article is devoted to the analysis of the use of ubiquitous computing and distributed machine learning in smart cities. Smart city is characterized by the introduction of high-tech infrastructure, digital services, integrated information monitoring systems that allow to optimize the environment and processes of urban management. The most promising direction of smart cities development is the implementation of ubiquitous computing systems. Ubiquitous computing involves the introduction of a significant number of technologies, including sensors, artificial intelligence, Internet of Things, network robots. Since ubiquitous computing is based on the processing of data generated by different devices, the new solutions are needed to structure and ensure data compatibility. Such solutions are the distributed machine learning methods: stochastic gradient descent and K-means method. The work separately considers the use of federated training, which has advantages in data privacy and mobile computing. The article deals with the main provisions of the concept of smart city, technologies of ubiquitous computing, features of methods of distributed machine learning and their introduction into urban systems management.
Analysis Effect of Tournament Selection on Genetic Algorithm Performance in Traveling Salesman Problem (TSP)

Abstract This study discusses effect of tournament selection on the way individuals compete on the performance of Genetic Algorithms so which one tournament selection is most suitable for the Traveling Salesman Problem (TSP). One algorithm in solving TSP is Genetic Algorithm, which has 3 (three) main operators, namely selection, crossover, and mutation. Selection is one of the main operators in the Genetic Algorithm, where select the best individuals who can survive (the shortest travel route). Tournament selection compares a number of individuals through a match to choose the best individual based on each fitness value, so that the winning individual (the individual going to the next generation) will be chosen. There is two way to compete in an individual in tournament selection is by tournament selection with replacement (TSWR) and without replacement (TSWOR). The final results of the study conducted TSWR gets the best fitness, even though the generation that gets the best fitness is reaching the maximum generation (takes longer to get the best fitness).
Constructing efficient multigrid solvers with genetic programming

For many linear and nonlinear systems that arise from the discretization of partial differential equations the construction of an efficient multigrid solver is a challenging task. Here we present a novel approach for the optimization of geometric multigrid methods that is based on evolutionary computation, a generic program optimization technique inspired by the principle of natural evolution. A multigrid solver is represented as a tree of mathematical expressions which we generate based on a tailored grammar. The quality of each solver is evaluated in terms of convergence and compute performance using automated local Fourier analysis (LFA) and roofline performance modeling, respectively. Based on these objectives a multi-objective optimization is performed using grammar-guided genetic programming with a non-dominated sorting based selection. To evaluate the model-based prediction and to target concrete applications, scalable implementations of an evolved solver can be automatically generated with the ExaStencils framework. We demonstrate the effectiveness of our approach by constructing multigrid solvers for a linear elastic boundary value problem that are competitive with common V- and W-cycles.
Co-Residence Data Theft Attacks on <i>N</i>-Version Programming-Based Cloud Services With Task Cancelation

Powered by virtualization, the cloud computing has brought good merits of cost effective and on-demand resource sharing among many users. On the other hand, cloud users face security risks from co-residence attacks when using this virtualized platform. Particularly, a malicious attacker may create side channels to steal data from a target user’s virtual machine (VM) that co-resides with the attacker’s VM on the same physical server. This article models a cloud service undergoing the co-residence data theft attacks. The threshold-voting-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> -version programming (NVP) is implemented to improve the service reliability, where multiple service component versions (SCVs) are activated in parallel to perform the requested service. The final output is determined upon receiving a threshold number of identical outputs from the SCVs, immediately followed by canceling all outstanding SCVs to reduce expenses. Probabilistic models are first introduced to evaluate performance metrics of the considered service, including the data theft probability, service success probability, expected service operation time, and expected utility. Optimization problems are further solved to find the optimal number of SCVs maximizing the expected utility. Interactions among different model parameters and VM allocation policies, as well as their effects on the considered performance metrics and on the optimization solutions are studied through examples.
Designing the Expressivity of Multiple Smart Things for Intuitive and Unobtrusive Interactions

Connected products and systems are becoming popular, but they seldom provide direct and intuitive communication to the users. In this study, we applied Disney's animation principles to design the expressivity with LED lights and speakers commonly embedded in electronic devices. We explored the subtle transitions of brightness and controlled the timing to compose individual and system-level behaviors with multiple devices. The designs were evaluated and improved through three iterations. In the main study, we recruited 16 designer participants to investigate whether lights and sounds could be intuitively interpreted as what the system wanted to convey. The result shows that group light behaviors could evoke meanings that are highly similar to the intents of the system. When the acoustic accompaniments were provided, participants could better perceive the presence of devices. We concluded with six sets of light behaviors that are capable of expressing smart devices and systems' intents intuitively and unobtrusively.
Comparative Study of Time Series-based Human Activity Recognition using Convolutional Neural Networks

Human Activity Recognition (HAR) is an emerging active research area which applies both digital signal processing methods and machine learning algorithms to predict motion activity based on ubiquitously available sensor measurements. Inertial sensors are commonly used in HAR systems. Deep learning (DL) techniques, especially using Convolutional Neural Network (CNN), have been widely used due to their structure flexibility and high classification accuracy. In this paper, we study different CNN structures and test their generalization ability under various conditions. The aim is to find the best CNN model for HAR systems in obtaining higher prediction rates using short time segment of inertial measurements data (1 s) compared to longer time segments (2.5 s) that are used in current literature. The results from this study show that using a simple 1-D shallow CNN fused with standard global statistical features of the input time signal provide the highest precision and recall. The study also shows that enhancing the architecture to multimodal CNN improves the recognition rate of some activities. The system was tested on two publicly available datasets, namely UCI HAR and m-Health. The proposed architecture achieved recognition accuracy of 97.12% using subject-based cross validation of UCI HAR dataset. Moreover, the generalization ability of the UCI HAR based system was verified by testing on m-Health dataset. This cross-dataset evaluation showed an overall accuracy of 93.09%.
Paraphrase thought: Sentence embedding module imitating human language recognition

Sentence embedding is an important research topic in natural language processing. It is essential to generate a good embedding vector that fully reflects the semantic meaning of a sentence in order to achieve an enhanced performance for various natural language processing tasks, such as machine translation and document classification. Thus far, various sentence embedding models have been proposed, and their feasibility has been demonstrated through good performances on tasks following embedding, such as sentiment analysis and sentence classification. However, because the performances of sentence classification and sentiment analysis can be enhanced by using a simple sentence representation method, it is not sufficient to claim that these models fully reflect the meanings of sentences based on good performances for such tasks. In this paper, inspired by human language recognition, we propose the following concept of semantic coherence, which should be satisfied for a good sentence embedding method: similar sentences should be located close to each other in the embedding space. Then, we propose the Paraphrase-Thought (P-thought) model to pursue semantic coherence as much as possible. Experimental results on three paraphrase identification datasets (MS COCO, STS benchmark, SICK) show that the P-thought models outperform the benchmarked sentence embedding methods.
An Analysis of Phishing Emails and How the Human Vulnerabilities are Exploited

Humans continue to be considered as the weakest link in securing systems. While there are a variety of sophisticated system attacks, phishing emails continues to be successful in gaining users attention and leading to disastrous security consequences. In designing strategies to protect users from fraudulent phishing emails, system designers need to know which attack approaches and type of content seems to exploit human limitations and vulnerabilities. In this study, we are focusing on the attackers’ footprints (emails) and examining the phishing email content and characteristics utilizing publicly available phishing attack repository databases. We analyzed several variables to gain a better understanding of the techniques and language used in these emails to capture users’ attention. Our findings reveal that the words primarily used in these emails are targeting users’ emotional tendencies and triggers to apply their attacks. In addition, attackers employ user-targeted words and subjects that exploits certain emotional triggers such as fear and anticipation. We believe our human centered study and findings is a critical step forward towards improving detection and training programs to decrease phishing attacks and to promote the inclusion of human factors in securing systems.
Addressing the train–test gap on traffic classification combined subflow model with ensemble learning

Previous machine learning-based network traffic classification approaches hold the assumption that training and testing network environment are of the same. This assumption is invalid in most real cases due to the changes in traffic features and leads to the train–test gap issue: the model trained in the training environment performs poorly in the testing environment. In this paper, to address the gap, we propose CSA: a traffic classification approach based on packet-wise segmentation and aggregation. Firstly, we observe that some specific fragments of network flows – subflows – are robust against the gap. Therefore, we are motivated to segment the traffic flows into different subflows. Afterward, with the justification of our feature selection, 26 statistical features are extracted from each subflow and input into its corresponding sub-classifier. Secondly, with the results from sub-classifiers, we develop an aggregation method based on their classification accuracy to increase the overall classification performance. We experiment on five real datasets, including three collected from the Northwest Center of CERNET (China Education and Research Network) and two from public traces. By comparing with state-of-the-art baselines, the experiment results demonstrate the effectiveness of our CSA against the gap.
What Is the Optimal Annealing Schedule in Quantum Annealing

In many real-life situations in engineering (and in other disciplines), we need to solve an optimization problem: we want an optimal design, we want an optimal control, etc. One of the main problems in optimization is avoiding local maxima (or minima). One of the techniques that helps with solving this problem is annealing: whenever we find ourselves in a possibly local maximum, we jump out with some probability and continue search for the true optimum. A natural way to organize such a probabilistic perturbation of the deterministic optimization is to use quantum effects. It turns out that often, quantum annealing works much better than non-quantum one. Quantum annealing is the main technique behind the only commercially available computational devices that use quantum effects-D-Wave computers. The efficiency of quantum annealing depends on the proper selection of the annealing schedule, i.e., schedule that describes how the perturbations decrease with time. Empirically, it has been found that two schedules work best: power law and exponential ones. In this paper, we provide a theoretical explanation for these empirical successes, by proving that these two schedules are indeed optimal (in some reasonable sense).
Extraction of the pulse width and pulse repetition period of linear FM radar signal using time-frequency analysis

A common technique used by military to realize low probability of intercept (LPI) is linear frequency modulation (LFM) in the field of electronic intelligence (ELINT). This paper estimates the pulse width (PW) and the pulse repetition period (PRP) of LFM signal using instantaneous powers. The instantaneous powers were obtained either using time-marginal or power maxima approximated from a modified version of the Wigner-Ville distribution (WVD). The instantaneous power was also gotten directly from the signal by multiplication with its conjugate. Measurement was then carried out when the instantaneous power is ‘ON’ (the PW) and when it is ‘OFF’ (the PRP) at carefully selected thresholds. Thereafter, the mWVD-based algorithm was tested in the presence of additive white Gaussian noise (AWGN) at various signal-to-noise ratios. Results obtained during the test showed that the time marginal method emerged the best with minimum signal-to-noise ratio (SNR) of -5dB followed closely by the direct method with minimum SNR of -1dB at different thresholds. The results show that the proposed algorithm based on this modified WVD can be deployed in the practical field to determine radar’s performance and function
Program synthesis as latent continuous optimization

In optimization and machine learning, the divide between discrete and continuous problems and methods is deep and persistent. We attempt to remove this distinction by training neural network autoencoders that embed discrete candidate solutions in continuous latent spaces. This allows us to take advantage of state-of-the-art continuous optimization methods for solving discrete optimization problems, and mitigates certain challenges in discrete optimization, such as design of bias-free search operators. In the experimental part, we consider program synthesis as the special case of combinatorial optimization. We train an autoencoder network on a large sample of programs in a problem-agnostic, unsupervised manner, and then use it with an evolutionary continuous optimization algorithm (CMA-ES) to map the points from the latent space to programs. We propose also a variant in which semantically similar programs are more likely to have similar embeddings. Assessment on a range of benchmarks in two domains indicates the viability of this approach and the usefulness of involving program semantics.
TinyEVM: Off-Chain Smart Contracts on Low-Power IoT Devices

With the rise of the Internet of Things (IoT), billions of devices ranging from simple sensors to smart-phones will participate in billions of micropayments. However, current centralized solutions are unable to handle a massive number of micropayments from untrusted devices. Blockchains are promising technologies suitable for solving some of these challenges. Particularly, permissionless blockchains such as Ethereum and Bitcoin have drawn the attention of the research community. However, the increasingly large-scale deployments of blockchain reveal some of their scalability limitations. Prominent proposals to scale the payment system include off-chain protocols such as payment channels. However, the leading proposals assume powerful nodes with an always-on connection and frequent synchronization. These assumptions require in practice significant communication, memory, and computation capacity, whereas IoT devices face substantial constraints in these areas. Existing approaches also do not capture the logic and process of IoT, where applications need to process locally collected sensor data to allow for full use of IoT micro-payments. In this paper, we present TinyEVM, a novel system to generate and execute off-chain smart contracts based on sensor data. TinyEVM's goal is to enable IoT devices to perform micro-payments and, at the same time, address the device constraints. We investigate the trade-offs of executing smart contracts on low-power IoT devices using TinyEVM. We test our system with 7,000 publicly verified smart contracts, where TinyEVM achieves to deploy 93% of them without any modification. Finally, we evaluate the execution of off-chain smart contracts in terms of run-time performance, energy, and memory requirements on IoT devices. Notably, we find that low-power devices can deploy a smart contract in 215 ms on average, and they can complete an off-chain payment in 584 ms on average.