Reviewerly/exHarmony huggingface dataset:
Energy-aware Goal Selection and Path Planning of UAV Systems via Reinforcement Learning

Visual exploration and smart data collection via autonomous vehicles is an attractive topic in various disciplines. Disturbances like wind significantly influence both the power consumption of the flying robots and the performance of the camera. We propose a reinforcement learning approach which combines the effects of the power consumption and the object detection modules to develop a policy for object detection in large areas with limited battery life. The learning model enables dynamic learning of the negative rewards of each action based on the drag forces that is resulted by the motion of the flying robot with respect to the wind field. The algorithm is implemented in a near-real world simulation environment both for the planar motion and flight in different altitudes. The trained agent often performed a trade-off between detecting the objects with high accuracy and increasing the area coverage within its battery life. The developed exploration policy outperformed the complete coverage algorithm by minimizing the traveled path while finding the target objects. The performance of the algorithms under various wind fields was evaluated in planar and 3D motion. During an exploration task with sparsely distributed goals and within a UAV's battery life, the proposed architecture could detect more than twice the amount of goal objects compared to the coverage path planning algorithm in moderate wind field. In high wind intensities, the energy-aware algorithm could detect 4 times the amount of goal objects when compared to its complete coverage counterpart.
Target Searching for Multiple Robots Using Hybrid Particle Swarm and Bacterial Foraging Optimization

Abstract Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO) are both important algorithms in target searching tasks. A hybrid optimization algorithm with the advantages of PSO and BFO is proposed to overcome local minimal and slow global convergence in target searching for multiple robots. Simulation on an example of target searching is conducted to show the effectiveness of the proposed approach.
An Approach to the On-site Visualization in Early Phase of Design based on Augmented Reality(AR) - Focused on an Example of a University Library Renovation Planning -

This paper describes an approach to the on-site visualization of design alternatives in early phase of design for enhancing visual communication by using Augmented Reality(AR) technique. Early design visualization and appropriate communication with clients is one of the keys in design project to alleviate some unnecessary time-consuming tasks such as often design changes caused by miscommunication. Conventional design communication is usually based on 2-dimensional drawings and rendering images at off-site. because they are fundamental and de-facto standard way of delivering an idea of designers. However, it is sometimes hard to fully deliver designers’ intents especially complex shapes of design or on-site realism. As one of realistic design visualization media, this paper deals with an application of on-site interior design visualization using AR technique based on an actual design project. It overlaps virtual objects and real world to provide a composite view to users. By utilizing AR, the designer can represent their design ideas to the client more intuitively by providing realistically experience about design plan. This paper proposes an approach to visualizing design alternatives using AR and presents an implementation example of a on-site AR visualization especially for the interior design project using an actual demo. This implementation aims to confirm that AR could be applied as a visualization medium, especially for interior design communication.
A Survey on Analysis of User Behavior on Digital Market by Mining Clickstream Data

Data stream mining has emerged as one of the most prominent areas with its applications in various areas like network sensors, stock exchange, meteorological research and e-commerce. Stream mining is potentially an active area in which the data is continuously generated in large amounts which are dynamic, non-stationary, unstoppable, and infinite in nature. One of such streaming data generated with the user browsing tendency is Clickstream data. Analyzing the user online behavior on e-commerce Web sites is helpful in drawing certain conclusions and making specific recommendations for both the users and the electronic commerce companies to improve their marking strategies and increase the transaction rates effectively leading to enhance the revenue. This paper aims at presenting a survey of different methodologies and parameters used in analyzing the behavior of a user through Clickstream data. Little deeper, this article also outlines the methods used so far for clustering the users based on mining their interests.
Mechanisms for the origin of hidden synchronization of dynamic systems

One of the varieties of radio engineering systems is considered in the work, namely, a frequency-phase locked loop system FPLL.The mathematical model of such a system is described by a system of differential equations with a cylindrical phase space.For the FPLL system, the conditions for the formation of latent synchronization modes are defined.Despite the numerous works devoted to FPLL systems, the questions of finding hidden synchronization, determining the mechanisms of its occurrence, finding the conditions of bifurcation of cycles and
Am I Asking It Properly?

The increasingly prevalent use of chatbots provides an innovative way to conduct conversation interviews. However, the lack of comprehensive design guidance and robust evaluation methodologies challenge designers in developing interview chatbots with good elicitation and ethics. This proposal presents our progress on investigating interview chatbots' performances in eliciting high-quality response without breaking any ethical rules, based on which an automatic evaluation framework will be developed. We also present our plan to propose design implications and prototypes for more robust interview chatbot development cycle.
Effects of misalignments on the modulation transfer function measurement of camera lenses analyzed in optomechanical simulations

The Physikalisch-Technische Bundesanstalt has developed a reference setup for measuring the modulation transfer function (MTF) of camera lenses with the goal of reaching an expanded measurement uncertainty of 0.01 (coverage probability of 95%) for various measurement configurations. We present optomechanical simulations of the setup behavior, which are used to investigate the influence of the combined mechanical misalignments on the MTF depending on the lens under test. The investigations are carried out as part of Monte Carlo studies for different sample lenses and field heights considering the correlations between the effects of different positioning errors on the MTF measured. The results of the sensitivity analyses have allowed appropriate alignment strategies to be identified that significantly reduce the uncertainty contribution of the positioning errors. By implementing these strategies, the target measurement uncertainty can be achieved for most of the desired measurement configurations. In addition, the comparison of three different sample lenses shows that the MTF sensitivity to misalignments strongly depends on the characteristics of the lens to be measured.
Deep Rating Elicitation for New Users in Collaborative Filtering

Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations. The key challenge of the rating elicitation is to choose the seed items which can best infer the new users’ preference. This paper proposes a novel end-to-end Deep learning framework for Rating Elicitation (DRE), that chooses all the seed items at a time with consideration of the non-linear interactions. To this end, it first defines categorical distributions to sample seed items from the entire itemset, then it trains both the categorical distributions and a neural reconstruction network to infer users’ preferences on the remaining items from CF information of the sampled seed items. Through the end-to-end training, the categorical distributions are learned to select the most representative seed items while reflecting the complex non-linear interactions. Experimental results show that DRE outperforms the state-of-the-art approaches in the recommendation quality by accurately inferring the new users’ preferences and its seed itemset better represents the latent space than the seed itemset obtained by the other methods.
IOT based Coal Mine Safety Monitoring and Controlling

At this moment are shifting through an IoT (Internet of Things) screen, prosperity tries for excavators which is commonly fundamental in underground mining spaces.At this moment, system is making using explicit sensors sort out subject to MEMS used to screen the ecological variables parameters of underground mine place and drives each and every perceived parameter/ascribes to/characteristics to ARDUINO based ATmega2560 Microcontroller Unit (MCU).The MCU unit is used to make a totally robotized investigating structure with high exactness, smooth control and consistency.Decisively when a fundamental condition is seen alert is given by the structure and comparative estimations is given to webserver by beginning ESP8266 module subject to Wi-Fi correspondence.The apparent assortments in the characteristics are appeared on webserver page that makes less referencing for the underground control network to screen and to make critical quick move to hinder genuine mischief.At the same time using NRF24L01 handset module to transmit data from the mine fragment which can be used to screen and brief move to be made.
INT-RUP Secure Lightweight Parallel AE Modes

Owing to the growing demand for lightweight cryptographic solutions, NIST has initiated a standardization process for lightweight cryptographic algorithms. Specific to authenticated encryption (AE), the NIST draft demands that the scheme should have one primary member that has key length of 128 bits, and it should be secure for at least 250 − 1 byte queries and 2112 computations. Popular (lightweight) modes, such as OCB, OTR, CLOC, SILC, JAMBU, COFB, SAEB, Beetle, SUNDAE etc., require at least 128-bit primitives to meet the NIST criteria, as all of them are just birthday bound secure. Furthermore, most of them are sequential, and they either use a two pass mode or they do not offer any security when the adversary has access to unverified plaintext (RUP model). In this paper, we propose two new designs for lightweight AE modes, called LOCUS and LOTUS, structurally similar to OCB and OTR, respectively. These modes achieve notably higher AE security bounds with lighter primitives (only a 64-bit tweakable block cipher). Especially, they satisfy the NIST requirements: secure as long as the data complexity is less than 264 bytes and time complexity is less than 2128, even when instantiated with a primitive with 64-bit block and 128-bit key. Both these modes are fully parallelizable and provide full integrity security under the RUP model. We use TweGIFT-64[4,16,16,4] (also referred as TweGIFT-64), a tweakable variant of the GIFT block cipher, to instantiate our AE modes. TweGIFT-64-LOCUS and TweGIFT-64-LOTUS are significantly light in hardware implementation. To justify, we provide our FPGA based implementation results, which demonstrate that TweGIFT-64-LOCUS consumes only 257 slices and 690 LUTs, while TweGIFT-64-LOTUS consumes only 255 slices and 664 LUTs.
A Sensitive Information Detection Method Based on Network Traffic Restore

With increasing importance of protecting sensitive data, it is necessary to monitoring the transmission of sensitive data. Most recent methods detect the network packet based on the analyzing IP, port, size characteristics and payload of the network traffic packets, which can not be used for detecting sensitive document transmitted via network. Hence, this paper propose a sensitive information detection method based on network traffic restore solution to fix the problem. This method firstly use TCP reassembling to restore documents from network traffic, and then use TF-IDF model to judge the similarity with those documents marked with sensitive flag, which is very simple and efficient.
Attentive Gated Graph Neural Network for Image Scene Graph Generation

Image scene graph is a semantic structural representation which can not only show what objects are in the image, but also infer the relationships and interactions among them. Despite the recent success in object detection using deep neural networks, automatically recognizing social relations of objects in images remains a challenging task due to the significant gap between the domains of visual content and social relation. In this work, we translate the scene graph into an Attentive Gated Graph Neural Network which can propagate a message by visual relationship embedding. More specifically, nodes in gated neural networks can represent objects in the image, and edges can be regarded as relationships among objects. In this network, an attention mechanism is applied to measure the strength of the relationship between objects. It can increase the accuracy of object classification and reduce the complexity of relationship classification. Extensive experiments on the widely adopted Visual Genome Dataset show the effectiveness of the proposed method.
The use of augmented reality-based learning media to develop the technology literacy of chemistry teachers in the 21st century

The 21st century chemistry learning process requires teachers to implement creative learning methods that are supported by technology. However, the teachers’ ability to use technology in chemistry learning is still low. The purpose of this study was to develop the teachers’ technology literacy using augmented reality-based learning media. This research used mix-method with sequential strategies of mixed methods. The first stage of this research was conducted by surveying the use of technology in chemistry learning. The instrument used was closed and open questionnaire. The second stage was carried out by collecting qualitative data through interview techniques. The data taken from this stage were to obtain the data of teachers’ technology literacy after they received a training treatment using Augmented Reality based learning media. The research subjects consisted of 25 Chemistry teachers spread in Central Java. The results of the study show that teachers’ literacy in chemistry learning is still low. The training of augmented reality-based learning increases the teachers’ technology literacy. In addition, augmented reality-based learning media also assist teachers to introduce abstract concepts to students. In addition, this learning media also help teachers who have limited tools and materials in the laboratory to introduce the laboratory work virtually to the students.
Optimization of Random Forests Algorithm Based on ReliefF-SA

Abstract As a widely used classification integration algorithm, the random forests based on decision tree combination is mostly used for classification and regression problems, but its deficiencies still need to be improved. In this paper, we propose simulated annealing algorithm, and its parameters is combined with the global search optimal solution and local optimal solution to optimize search. Then, we propose a random forests hybrid algorithm by combining the Relief algorithm and annealing algorithm, briefly introduce its thoughts and algorithm flow, and also carry out simulation experiments. According to the experimental results, the classification effect of the hybrid algorithm is generally the best, which greatly improves the overall generalization performance of the random forests.
Research on EVM-Based Smart Contract Runtime Self-protection Technology Framework

Smart contracts enable developers to quickly develop decentralized applications running on the blockchain, enriching blockchain applications, but at the same time, due to their immutability, smart contracts have frequent vulnerabilities in recent years. These years many smart contract audit tools have emerged; however, we are committed to solving the problem at the root, using RASP technology to design and implement the runtime self-protection framework of smart contracts, and design and implement the four common vulnerabilities in contracts.
Natural Perturbation for Robust Question Answering

While recent models have achieved human-level scores on many NLP datasets, we observe that they are considerably sensitive to small changes in input. As an alternative to the standard approach of addressing this issue by constructing training sets of completely new examples, we propose doing so via minimal perturbation of examples. Specifically, our approach involves first collecting a set of seed examples and then applying human-driven natural perturbations (as opposed to rule-based machine perturbations), which often change the gold label as well. Local perturbations have the advantage of being relatively easier (and hence cheaper) to create than writing out completely new examples. To evaluate the impact of this phenomenon, we consider a recent question-answering dataset (BoolQ) and study the benefit of our approach as a function of the perturbation cost ratio, the relative cost of perturbing an existing question vs. creating a new one from scratch. We find that when natural perturbations are moderately cheaper to create, it is more effective to train models using them: such models exhibit higher robustness and better generalization, while retaining performance on the original BoolQ dataset.
An artificial intelligence based crowdsensing solution for on-demand accident scene monitoring

Road traffic crashes have a devastating impact on societies by claiming more than 1.35 million lives each year and causing up to 50 million injuries. Improving the efficiency of emergency management systems constitutes a key measure to reduce road traffic deaths and injuries. In this work, we propose a comprehensive crowdsensing-based solution for the real-time collection and the analysis of accident scene intelligence as a means to improve the efficiency of the emergency response process and help reduce road fatalities. The solution leverages sensory, mobile, and web technologies for the real-time monitoring of accident scenes, and employs Artificial Intelligence for the automatic analysis of the accident scene data, to allow the automatic generation of accident intelligence reports. Police officers and rescue teams can use those reports for fast and accurate situational assessment and effective response to emergencies. The proposed system was fully implemented and its operation was successfully tested using a variety of scenarios. This work gives interesting insights into the possibility of leveraging crowdsensing and artificial intelligence for offering emergency situational awareness and improving the efficiency of emergency response operations.
A Research on Face Profile Surface Acquisition and Robot Trajectory Planning

This study researches a method of the facial profile surface acquisition and robot trajectory planning on the human face. A Kinect 2 is firstly used to obtain the facial contour information. Then a deformation method based on 68 feature points is proposed to improve the accuracy of the facial model. Furthermore, a refinement and optimization of facial mesh model is constructed for robot trajectory planning and posture adjustment in working process. For complex facial contours and features, a cross-section method is suggested to plan discrete points of the facial operation trajectory, in which the robot posture can be controlled to changing with the trajectory normal curvature. Simulation and experiment demonstrates that the studied method is effective in 3D face model construction and robot movement control of the facial operation.
Bitcoin price prediction using ARIMA model

Bitcoin is a highly volatile cryptocurrency with rising popularity. It is a turning point in the way currency is seen. Now the currency, rather than being physical is becoming more and more digital. Due to high variance of solo mining, the number of users joining top most famous bitcoin mining pools is increasing due to the fact that users together under a bitcoin pool will have a higher chance of generating next block in the bitcoins blockchain by reducing the variance and earning the mining reward. In this research paper we are doing a survey on the technology lying underneath bitcoin's network and the various machine learning predictive algorithms. We collected the dataset on bitcoin blockchain from 28 April 2013 to 31 July 2017 which is publicly available on https://coinmarketcap.com and applied the ARIMA model for price prediction of bitcoin.
Balancing Specialization, Generalization, and Compression for Detection and Tracking

We propose a method for specializing deep detectors and trackers to restricted settings. Our approach is designed with the following goals in mind: (a) Improving accuracy in restricted domains; (b) preventing overfitting to new domains and forgetting of generalized capabilities; (c) aggressive model compression and acceleration. To this end, we propose a novel loss that balances compression and acceleration of a deep learning model vs. loss of generalization capabilities. We apply our method to the existing tracker and detector models. We report detection results on the VIRAT and CAVIAR data sets. These results show our method to offer unprecedented compression rates along with improved detection. We apply our loss for tracker compression at test time, as it processes each video. Our tests on the OTB2015 benchmark show that applying compression during test time actually improves tracking performance.
Modeling Users’ Multifaceted Interest Correlation for Social Recommendation

Recommender systems suggest to users the items that are potentially of their interests, by mining users’ feedback data on items. Social relations provide an independent source of information about users and can be exploited for improving recommendation performance. Most of existing recommendation methods exploit social influence by refining social relations into a scalar indicator to either directly recommend friends’ visited items to users or constrain that friends’ embeddings are similar. However, a scalar indicator cannot express the multifaceted interest correlations between users, since each user’s interest is distributed across multiple dimensions. To address this issue, we propose a new embedding-based framework, which exploits users’ multifaceted interest correlation for social recommendation. We design a dimension-wise attention mechanism to learn a correlation vector to characterize the interest correlation between a pair of friends, capturing the high variation of users’ interest correlation on multiple dimensions. Moreover, we use friends’ embeddings to smooth a user’s own embedding with the correlation vector as weights, building the elaborate unstructured social influence between users. Experimental results on two real-world datasets demonstrate that modeling users’ multifaceted interest correlations can significantly improve recommendation performance.