Reviewerly/exHarmony huggingface dataset:
Penalty Based Backend Path Management

Ever since the technologies like big data and data mining have been introduced, keeping the data that is required to get the job done continuously available has become a requirement for almost all customer segments ranging from small business to datacenters. In contrast to connection failures in message networks—which generally could cope up by re-establishing the connection in storage networks, a network resource failure is more likely to cause a system crash. Achieving a very high level of availability can be arduous and very expensive, and it also requires that redundancy be established at multiple levels: Redundancy of storage, regular backups to help in server recovery, clustering similar servers, and redundancy of the physical path components. Load balancing, which is the distribution of reading/write (I/O) requests and fault tolerance, to surcharge the performance between the server and the storage device(s), is very important in a high load environment or settings where having consistent service level is very critical. Therefore, a system needs to have a good MPIO (multipath I/O) policy. Without an MPIO policy, a server sending I/O requests in a multiple path setup may operate such that there is a very heavy workload on some paths while other paths may be underutilized, without considering the performance of paths. The current approaches use scheduling algorithms like Round-Robin, first come first serve (FCFS), shortest seek time first (SSTF), etc., for scheduling I/O requests on paths. In such an environment, where the load on paths is not consistent or changes very frequently, Round-Robin scheduling does not work well []. The existing algorithms serve the I/O requests faster but not efficiently. This motivated us to study and analyze a method that can help us serve the I/O requests faster and efficiently. Our purpose is to introduce the user to a penalty based I/O path selection policy which is inspired by ant colony optimization (ACO). The ant colony optimization (ACO) is a very powerful approach for solving computational problems. Here, we will be considering improving the I/O servicing efficiency with the help of the path selection policy. This policy will allow us to distinguish between available paths based on their past performance hence, improving the overall system performance.
Design of Distributed Plant Temperature and Humidity Monitoring System

In recent years, the technology of industrial and agricultural generation driven by information technology is developing vigorously. The design and research of distributed temperature and humidity monitoring system is immeasurable for the impact and promotion of tertiary industry, agriculture and industry. It is the general trend of modern prosperity and future development. It is self-evident that it is of great significance to occupy the supreme prestige and level of international advanced science and technology strategy, especially for the development of economy and the improvement of industrial automation.
Fiber-Based Stations for Quantum Key Distribution Field Trials over Installed Fiber Links

In-field demonstrations in real-world scenarios boost the development of quantum key distribution (QKD) towards its integration in existing infrastructures. Although QKD devices are already adopted outside the laboratories, current field implementations still suffer from high costs and low performances, preventing this technology from a large-scale deployment in telecommunication networks. Here, we present the developing of a cost-effective setup for QKD field trials, implemented for testing QKD performances in two different scenarios: (i) using an installed fiber link the metropolitan area of Florence (Italy); (ii) using a submarine optical fiber channel between Malta and Sicily.
Generating Real-Time Strategy Heightmaps using Cellular Automata

This paper presents a new approach of heightmap generation for Real-Time Strategy games (RTS) based on Cellular Automata (CA) in the context of various established techniques. The proposed approach uses different CA rulesets to generate and modify maps for the RTS game Supreme Commander. To evaluate the quality of the generated maps, a survey was conducted asking 30 participants about map quality compared to user-generated maps. The participants rated the maps more balanced and novel but less aesthetically pleasing. The paper concludes with according future work propositions to improve the presented approach.
Tactical Intention Recognition Based on Fuzzy Multi-Entity Bayesian Network

In this paper, the representation and reasoning of uncertainty in the field of tactical intention recognition are analyzed and studied. An intelligent command and control decision support process based on deep reinforcement learning and fuzzy multi-entity Bayesian network is proposed. On this basis, a method of tactical intention recognition based on fuzzy multi-entity Bayesian network is also proposed and probability ontology is constructed. The experimental results show that the method proposed in this paper is feasible and provides a new solution for the representation and reasoning of uncertain information in the field of battlefield situation estimation.
How to Generate Reasonable Texts with Controlled Attributes

The controllable text generation (CTG) task is crucial for text-related applications, such as goal-oriented dialogue systems and text style-transfer applications, etc. However, existing CTG methods commonly ignore the co-occurrence dependencies between multiple controlled attributes, which are implicit in domain knowledge. As a result, rarely co-occurring controlled values are highly likely to be given by users, which finally leads to non-committal generated texts that are out of control. To address this problem, we initially propose the Dependency-aware Controllable Text Generation (DCTG) model that reduces trivial generations by automatically learning the co-occurrence dependencies and adjusting rarely co-occurring controlled values. Our DCTG highlights in (1) modeling the co-occurrence dependencies between controlled attributes with neural networks, (2) integrating dependency losses to guide each component of our model to collaboratively work for generating reasonable texts based on the learned dependencies, and (3) proposing a novel Reasonableness metric measuring to which degree generations comply with real co-occurrence dependencies. Experiments prove that DCTG outperforms state-of-the-art baselines on three datasets in multiple aspects.
Analysis of Single Demodulation of Composite Continuous Wave Signal Modulated by Pseudorandom Code Family

In this paper, a new kind of composite continuous wave signal modulated by pseudorandom code family is designed aiming at solving the problem of group targets resolution with high dynamic: more quantity, larger speed variation, larger distribution and higher resolution. With a thumbtacked ambiguity, the new composite signal retained the advantage of pseudo code signal with high 2 dimensional resolution of speed and range. Furthermore, a new single demodulation method is proposed for this modulated signal. This method brings the characteristic of multi-resolution for the composite signal. Single demodulation method has been proved to be feasible through the correlation analysis and fuzzy characteristics analysis of sub-signal from the composite signal. Meanwhile, the realization process of signal multiresolution is illustrated by an example. At last, recommendations pertaining to signal selection, number option and usage are put forward for this signal modulated by pseudo-code family and this single demodulation method. The study of this paper is helpful for group targets resolution and is also of great importance to improve complex waveform design and performance.
A closely spaced target track maintenance algorithm based on Gaussian mixture probability hypothesis density

As one suboptimal but tractable alternative to the Bayes filter within the random finite set (RFS) framework, the probability hypothesis density (PHD) filter has attracted considerable attention because of its low computational load and ease of implementation. However, the standard PHD filter is unable to estimate the tracks of targets in clutter circumstances. Although some PHD-based trackers are proposed to estimate the tracks of targets, the track maintenance performances of these trackers suffer from significant declines in closely spaced target environments. In this paper, with the help of the track label and an association-update factor matrix, a closely spaced target track maintenance algorithm based on the Gaussian mixture PHD filter is proposed to identify and provide the trajectories of close targets over times. Numerical simulations demonstrate that the proposed algorithm is capable of correctly estimating the tracks of closely spaced targets in clutter environments, and is superior to the related multi-target trackers in terms of target states, computational load, and association accuracy of tracks.
Cross-Graph Representation Learning for Unsupervised Graph Alignment

As a crucial prerequisite for graph mining, graph alignment aims to find node correspondences across multiple correlated graphs. The main difficulty of graph alignment lies in how to seamlessly bridge multiple graphs with distinct topology structures and attribute distributions. A vast majority of earlier efforts tackle this problem based on alignment consistency, which directly measures the attribute and structure similarity of nodes. However, alignment consistency is prone to be violated due to the radically different patterns owned by different graphs. Another group of methods tackle the problem in a supervised manner by learning a mapping function that maps the node representations of both the source and target graphs into the same feature space. However, these methods heavily rely on observed anchor links between different graphs while these anchor links are usually limited or even absent in many real-world applications. To address these issues, we propose an unsupervised cross-graph representation learning framework to jointly learn the node representations of different graphs in a unified deep model. Specifically, we employ an auto-encoder model to learn the cross-graph node representations based on both attribute and structure reconstruction, where source and target graphs share the same encoder but are decoded by their respective decoders. To step further, we also introduce a discriminator to better align the learned representations for different graphs via adversarial training. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed approach.
PrivGMM: Probability Density Estimation with Local Differential Privacy

Probability density estimation is a fundamental task in data analysis that can estimate the unobservable underlying probability density function from the observed data. However, the data used for density estimation may contain sensitive information, and the public of original data will compromise individuals' privacy. To address this problem, we in this paper propose a private parametric probability density estimation mechanism, called PrivGMM. It provides strong privacy guarantees locally (e.g., on personal computers or mobile phones) and efficiently (i.e., computation cost is small) for users. Meanwhile, it provides an accurate estimation of parameters of the probability density model for data collectors. Specifically, in a local setting, each user adds noise to his/her original data, given the constraint of local differential privacy. On the server side, we employ the Gaussian Mixture Model, which is a popular model to approximate distributions. To reduce the effect of noise, we formulate the parametric estimation problem with a multi-layer latent variables structure, and utilize Expectation-Maximization algorithm to solve the Gaussian Mixture Model. Experiments in real datasets validate that our mechanism outperforms the state-of-the-art methods.
A Methond of Capturing GPS Signal Loss Based on QAR Data

The GPS signal is interfered unintentionally or manually, which leads to the GPS failure. It will cause the position error reported by the aircraft, making the pilots lose situational awareness. As a result, it will lay a great hidden peril for flight safety. In this study, the GPS signal loss analysis was carried out by using the QAR data to accurately capture loss location and period information of the signal. By displaying the analysis data on the electronic map, keeping consistent with the flight route of the aircraft, and using the longitude and latitude coordinate information of the map, it can effectively improve the efficiency and timeliness of manual patrol signal interference detection and reduce the potential safety hazards of civil aviation flight.
The Local Navigation and Positioning System of Unmanned Ground Vehicles

Navigation and positioning system plays an important role in autonomous driving vehicles. In view of the existing problems, this paper mainly introduces the design and the implementation of local navigation and positioning system of UGVs. At first, this paper introduces all kinds of dead-reckoning algorithms for local navigation and positioning system of UGVs, including DR algorithm based on odometer, IMU, and the combination of IMU and odometer. Then, it analyzes the advantages, disadvantages and existing problems of these algorithms, and demonstrates the rationality of DR algorithm based on IMU and odometer. Finally, the navigation and positioning results of different algorithms are compared by experiments. Experiments show that the algorithm based on IMU and odometer is better than other algorithms. It is feasible to use to complete local navigation and positioning system of UGVs.
Efficient and Secure Two-Party Distributed Signing Protocol for the GOST Signature Algorithm

Mobile devices, such as Android and iOS devices, are often used for electronic/mobile commerce (e.g. payments using WeChat Pay and Bitcoin wallet). Hence, ensuring the security of a user’s private key stored on the device is crucial. To reduce the risk of private key leakage, a number of (t, n) threshold secret sharing protocols have been proposed in the literature. However, (t, n) threshold secret sharing protocols are generally not designed to mitigate key reconstruction attacks. Hence, in this paper we propose an efficient and secure two-party distributed signing protocol for the GOST signature algorithm, a Russian cryptographic standard algorithm. This allows us to separate a single private key to two mobile devices and generate a valid signature without reconstructing the entire private key. We then prove that our protocol is secure under non-standard assumption. Moreover, we implement our protocol using MIRACL Cryptographic SDK and evaluate its performance on two Android devices and two personal computers.
Scene detection and recognition by analysing deep features using PlacesCNN

Scene recognition is employed for recognizing images along with some other visual features to collect information from it. As a field, it has turned out to be useful for digital marketers. Digital marketers can identify a consumer's favorite hangout spot like a cafe or bar based on his/her social media posts or uploads. Other applications include using the information from the pictures by the tour guide. CNNs help to identify whether the images belong to a specific class or not like a playground, classroom, dining room depending on the dataset. Different types of CNNs have been used to perform the classification task ranging from PlacesCNN, ImageNetCNN, HybridCNN and much more. PlacesCNN has been implemented using architectures namely AlexNet, GoogleNet and VGG. The objective of the paper is to study and analyze the performance for PlacesCNN based on VGG architecture to classify images into their correct classes along with determining the accuracy for the same. Using the pretrained model for PlacesCNN and the concept of transfer learning, we have been able to perform the task of scene recognition and achieve an accuracy of 98.25% for the same.
67‐3: ColorNet: A Neural Network‐Based System for Consistent Display of Brand Colors for Video

When watching sports on screen, fans recognize inconsistencies in their team colors. ColorNet is an artificial intelligence (AI) technology that ingests live video, adjusts each frame pixel‐by‐pixel, and outputs color corrected video in real‐time ensuring accurate brand colors. In this study, ColorNet is demonstrated using Clemson University's orange, Pantone 165.
Realization of “Animated Image Processing” Open Teaching Platform

The animation image processing course mainly contains machine operation as the prime teaching content. Compared with other majors, this major requires higher level of image processing ability and software operation skills for students who are in this major. Therefore, the course task is more suitable for conducting the information environment. This research pays attention on the “animated image processing” open teaching platform as the research content. The implementation of this research ensures the implementation of professional education mode and the continuous improvement of the curriculum system on the basis of open teaching; they studied and designed an open teaching platform based on “animated image processing,” which enables students to integrate information teaching and intelligent management into the learning process of “animated image processing”; the implementation of this study will realize the student-centered education thought as the leading direction and cultivate students’ ability as the core and the intensify students’ professional quality, theoretical application ability and innovative development as the focus. It effectively improves the practical operation level of students and impels students to develop further orientation during the process of interest and problem.
Medical CT Images Classification Model Based on BBO-HS Algorithm Optimized SVM

A medical CT image classification model with hybrid BBO (Biogeography Based Optimization) and HS (Harmony Search) algorithm is proposed to improve medical CT image classification accuracy and solve SVM parameter optimization problem in the classifier. Firstly, HS algorithm has strong search capabilities for global solutions, but poor local search abilities. It can be better to search global and local solutions by integrating migration operations of BBO algorithm into HS algorithm. Finally, the medical CT image classification model is established according to the optimal parameter and meanwhile the simulation test is also carried out to verify the performance of the model. The simulation result shows that relative to the comparison model, BBO-HS-SVM can not only improve medical CT image classification accuracy, but also accelerate the classification speed, thus being more suitable for the real-time classification requirements of medical CT images.
The Analysis of Students’ Politeness In Interaction on Facebook at the Fourth Semester Students of the English Education Program of Stkip Taman Siswa Bima

The aims of this research are: (1) The politeness used by students of fourth semester at STKIP Taman Siswa Bima in interaction on social networking Facebook. (2)The factors influenced students’ politeness in interaction. This research investigates how are the politeness used by students in their interaction in Basecamp group on Facebook. This research is descriptive research; This research used the primary data taken by the scrip of the students’ interaction on Facebook that will be analyzed in this research. Also, researcher used recording of interview. The result of this research showed that there were 62 Extracts among 9 conversations used by students, the researcher found 15 were used Bald on Record, 26 used Positive Politeness, 15 used Negative Politeness, and 6 used Off-record. Positive politeness was dominated among other kinds of politeness, which is oriented by the speaker toward the positive face or the positive self-image of the hearer that the speaker claims for himself. The speaker can satisfy the addressee’s positive face wants by emphasizing that speaker wants what the hearer’s wants. The use of Bald-on record which is chosen by students also. By using this politeness, the addressee possible to easy catch the speaker mean, so misunderstanding can be avoided. Only a very small number of participants are applying off-record. The second result that there are 3 factors that influenced students’ politeness at the students fourth semester of English Education Program of STKIP Taman Siswa Bima. The factors are : (1)Social Statues, students use polite utterances when they speak to theirs lecturers; (2) Situation; students use polite utterances depend on formal and informal situation, and (3) Gender, there are different utterances of female and male students and when they talk each others. Male students use polite utterances when they talk to female students because they have sensitive feeling
A Dynamic Fuzzy Neural System for Time Series Classification

A dynamic fuzzy neural system is proposed, for time series anomaly detection. The model is entitled BFuzzTiD (Block-diagonal Fuzzy Time-series Detector) and consists of fuzzy rules whose consequent parts are three-layer small recurrent neural networks. The hidden layer of each network has blocks of neurons that feed back to each other. BFuzzTid is trained by the Dynamic Resilient Propagation algorithm. The model learns the dynamics of the time series such that it can classify them by detecting the anomaly points. A comparative analysis is conducted with a series of time series anomaly detection models, in order to investigate the capabilities of the proposed detector.
Artificial Intelligence Assists Novice Designer to Improve Problem-Solving Ability of Interior Floor Plan

The purpose of this study is to explore how the artificial intelligence system of floor plan assist novice designer to improve problem-solving ability of interior floor plan. Through two experiments of drawing floor plan before and after, we used the method of document analysis to compare the differences of two drawings. Novices drew a floor plan of bedroom in first stage, and next, novices operated the artificial system of interior floor plan to construct their knowledge and learned various possible schemes in a short period of time. Finally, novices drew second floor plan of bedroom again. The result shows that the second floor plan to be improved significantly after novices used the intelligence system of floor plan.
A Study on the Development of Robot Education in the Fourth Industrial Revolution

Abstract With the development of the Fourth Industrial Revolution, traditional education is evolving into a new education adapted for future society. In particular, with the emergence of artificial intelligence, education targeted at enhancing creativity gets increasingly important. In the future, robots will be used in almost all areas of social life. For this reason, many creative education programs that use robots will surely be developed. In this study, the development trend of robots education programs for children was centrally analyzed. Based on these results, the study further analyzed the current status of robot education. It also suggested the actual practical curriculum of robot education. The results of this study can not only suggest an educational direction for robot education, but also provide with theoretical basis to some extent for future education related to robots.