Reviewerly/exHarmony huggingface dataset:
Po engineering safety monitoring and control system based on image quality analysis

Nowadays, urbanization has become a trend, and the realization of urbanization cannot be separated from the implementation of various projects. In the process of project implementation, the most critical issue is safety, so it is extremely necessary to monitor the project safety. Traditional manual monitoring cannot meet the development of today’s project, and the design of an automatic monitoring system for project safety has become a hot spot. In this paper, based on image processing and monitoring technology, and engineering safety monitoring and control system based on image quality analysis is studied, which can detect the engineering safety in real-time. Firstly, the image acquisition equipment is used to collect engineering images, and image processing is carried out to improve the image quality. Secondly, the convolutional neural network is used to realize image security analysis and detect the unsafe risk in engineering. Finally, combined with network technology, the automatic monitoring and control system of engineering safety based on image quality analysis is realized. Through simulation analysis, it is found that image processing can effectively remove noise and other interference and improve image quality. And the convolutional neural network can effectively detect the safety problems in the project, which shows that the design and implementation of the project safety monitoring and control system, it can achieve real-time safety monitoring in the implementation of the project, and has a good application effect in the project safety monitoring.
Hetero Complementary Networks with Hard-Wired Condensing Binarization for High Frame Rate and Ultra-Low Delay Dual-Hand Tracking

High frame rate, ultra-low delay yet accurate hand tracking system provides a seamless and intuitive interface for Human Computer Interaction (HCI). Tracking multi-person's dual-hand from monocular RGB camera is challenging for hand's variant image feature. Although many CNN based trackers have been proposed on general hardware, they cannot address this challenge with ultra-high speed. This paper proposes: (A) Hetero complementary networks for ultra-high speed dual-hand tracking, where the quick primary result from an FPGA network is intermittently combined with delayed accurate result from a GPU network. (B) Hard-wired condensing binarization for ultrahigh speed network implementation on FPGA. The network is able to be directly mapped as hardware resource because complex computation is condensed into binary layers. The proposed method achieves 69.8% accuracy on test sequences, which is only 4.7% lower compared with the general method. Meanwhile, the estimated FPGA resource utilization is tremendously reduced to 54.7% on the target platform. This work shows the potential to track multi-person's dual-hand at millisecond-level speed.
Training Systems for Control of Mobile Manipulator Robots in Augmented Reality

In this article, an augmented reality application interface was developed, using a technological tool oriented towards teaching-learning that allows a mobile manipulator robot training system to be created. Focusing on the simulation of autonomous control algorithms and the implementation of animations made in the Unity 3D software, which will allow the user to interact with the real world through a mobile device where the rear camera will used to capture the image and the touch screen for visualization. It is verified that the application is intuitive and understandable for the user to operate, in order to provide the necessary knowledge of the operation and structure of the robot.
Neural relational inference for disaster multimedia retrieval

Events around the world are increasingly documented on social media, especially by the people experiencing them, as these platforms become more popular over time. As a consequence, social media turns into a valuable source of data for understanding those events. Due to their destructive potential, natural disasters are among events of particular interest to response operations and environmental monitoring agencies. However, this amount of information also makes it challenging to identify relevant content pertaining to those events. In this paper, we use a relational neural network model for identifying this type of content. The model is particularly suitable for unstructured text, that is, text with no particular arrangement of words, such as tags, which is commonplace in social media data. In addition, our method can be combined with a CNN for handling multimodal data where text and visual data are available. We perform experiments in three different scenarios, where different modalities are evaluated: visual, textual, and both. Our method achieves competitive performance in both modalities by themselves, while significantly outperforms the baseline on the multimodal scenario. We also demonstrate the behavior of the proposed method in different applications by performing additional experiments in the CUB-200-2011 multimodal dataset.
The Learning Context Analysis System for Digital Textbook Service on Learning Cloud

Abstract On previous e-learning environment, learners should learn by him/herself, decide the learning sequences and take examination, since there were no way for teachers to know what the learners do and how the learners do for their learn. That means that after teachers giving the learning contents, teachers could not interact with learners, as like off-line classes. From those reason, e-learning environment has many disadvantages compared with off-line learning environment from the view of education or learning efficiency.But, on digital textbook service environment developed by this research, basic learning devices are smartphone that supports interaction between learners, teachers and learning contents. With various kinds of sensors and environment of digital textbook service, digital textbook system could track and gather learner’s learning activity and learner’s personal learning information(learning history, learning assessment results, e-portfolio, etc.). In this paper, the learning analytic system is designed for extracting, classifying and accumulating various learning data of learners from the learner’s learning activities and from learner’s personal learning information(learning history, learning assessment results, e-portfolio, etc.). And analysis learning data can be used to decide learner states and can be useful to construct learning strategy of learning direction and learning assessment results. In this research, proposed learning analytic system collects learning data from previously proposed digital textbook service and virtual practice learning cloud, and extracts and analyses learners’ learning activity. With the proposed learning analytic system, personalized learning sequence and personalized learning contents could be delivered to learners by intelligent tutoring engine and the learning analytic system and learners’ learning interests and learning efficiency would increase.
Block Level Data-Deduplication and Security Using Convergent Encryption to Offer Proof of Verification

Cloud computing is a promising cutting-edge technology because of its cost-efficiency and flexibility to store and access the data. Preserving the data in cloud has become great challenge as the end users store their privacy data in daily life. But majority of the data in the cloud storage is redundant. Cloud storage providers (CSP) are using de duplication as the technique to maintain only one of the copy of the file, by which cloud storage providers are able to minimize the management and storage overheads for data. Data de-duplication is one the mechanism which enables us to store more information with less storage space. Cloud storage system uses various data de-duplication schemes to drop redundancy. In conventional encryption, the user will initially encrypt the information along with a key he chooses and send the cipher text to cloud storage.
Bio-inspired Optical Flow-based Autonomous Obstacle Avoidance Control

In this paper, we propose a new methodology for autonomous obstacle avoidance control using a bio-inspired optical flow estimation. The main difference with other methods is that we use an image model inspired by the human vision system to define the constraints in the optical flow formulation which includes a Hermite transform (HT) and a perceptive mask. We use a physical robot platform to test the control algorithm, where due to the structure of the chassis a forward, reverse and turn movements were defined. The robot has a RBG camera to capture images of the path and then calculate optical flow estimation. To define velocity and direction robot response we propose a fuzzy controller. Finally, we made some experiments to demonstrate the performance of control navigation, and how responds algorithm using HT and a perceptive mask.
Pedestrian Tracking and Trajectory Analysis for Security Monitoring

Security awareness is becoming more and more critical in modern society. In this paper, we propose an infrared pedestrian tracking and trajectory analysis system. The system is able to monitor large area like factories and communities, and provides monitoring and suspicious alarm functions. An attention based object detector is structured to integrate features from different level. Deep-sort style tracker is used by following the principle of tracking-by-detection. Based on the tracking results, the system provides the function of suspect alarm by analyzing the trajectories. The performance of pedestrian tracking and suspect alarm satisfies the industrial needs in the experiment.
An Efficient and Accurate Object Detection Algorithm And Its Application

In order to solve the problem that the recognition accuracy and recognition speed of the current deep learning target detection algorithm are not compatible, an efficient and accurate object detection algorithm was proposed and applied to the inspection of small-sized body stamping parts of an auto parts enterprise. Firstly, image data sets of 10 small-sized automobile body stampings were produced. The transfer learning method was used to train YOLO V3, YOLO V3-tiny and the model proposed in this paper. Then, three models were used for small-sized body stamping parts detection and recognition experiment. The experimental results show that our model can detect 37 images per second in the same sample and test environment, which is 208.33% higher than the YOLO V3 model; the average detection precision is 96.50%, compared with The YOLO V3-tiny model is improved by 4.40%, and the average detection precision is improved up to 22.58% on a single smaller size object. This study can provide visual navigation support for the small-size body stamping robot automatic sorting system.
Data Anonymization: K-anonymity Sensitivity Analysis

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.
Simulation of pseudo-text synthesis for generating words with long-range dynamic correlations

In a previous study, we have regarded real written texts as time series data and have tried to investigate dynamic correlations of word occurrences by utilizing an autocorrelation function (ACF). The results showed that the obtained ACFs can be classified into two groups: One was a group of ACFs showing dynamic correlations and the other was a group of ACFs showing no correlations. A word having the former ACF was called Type-I word, and a word with the latter ACF was called Type-II word. In this study, we investigate the mechanism of generating dynamic correlations of Type-I words by conducting simulations of pseudo-text synthesis. The results of the simulation clarified that inheritance of words between subsequent paragraphs is key to generate dynamic correlations of Type-I words, indicating a important role of long-range memory with duration time from few to several tens of sentences.
Mitigating Large Response Time Fluctuations through Fast Concurrency Adapting in Clouds

Dynamically reallocating computing resources to handle bursty workloads is a common practice for web applications (e.g., e-commerce) in clouds. However, our empirical analysis on a standard n-tier benchmark application (RUBBoS) shows that simply scaling an n-tier application by reallocating hardware resources without fast adapting soft resources (e.g., server threads, connections) may lead to large response time fluctuations. This is because soft resources control the workload concurrency of component servers in the system: adding or removing hardware resources such as Virtual Machines (VMs) can implicitly change the workload concurrency of dependent servers, causing either under- or over-utilization of the critical hardware resource in the system. To quickly identify the optimal soft resource allocation of each server in the system and stabilize response time fluctuation, we propose a novel Scatter-Concurrency-Throughput (SCT) model based on the monitoring of each server's real-time concurrency and throughput. We then implement a Concurrency-aware system Scaling (ConScale) framework which integrates the SCT model to fast adapt the soft resource allocations of key servers during the system scaling process. Our experiments using six realistic bursty workload traces show that ConScale can effectively mitigate the response time fluctuations of the target web application compared to the state-of-the-art cloud scaling strategies such as EC2-AutoScaling.
Towards Security Attack and Risk Assessment during Early System Design

The advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) enabled a new class of smart and interactive devices. With their continuous connectivity and their access to valuable information in both the digital and physical world, they are attractive targets for security attackers. Hence, with their integration into both the industry and consumer devices, they added a new surface for cybersecurity attacks. These potential threats call for special care of security vulnerabilities during the design of IoT devices and CPS. The design of secure systems is a complex task, especially if they must adhere to other constraints, such as performance, power consumption, and others. A range of design space exploration tools have been proposed in academics, which aim to support system designers in their task of finding the optimal selection of hardware components and task mappings. Said tools offer a limited way of modeling attack scenarios as constraints for a system under design. The framework proposed in this paper aims at closing this gap, offering system designers a way to consider security attacks and security risks during the early design phase. It offers designers to model security constraints from the view of potential attackers, assessing the probability of successful security attacks and security risk. The framework's feasibility and performance is demonstrated by revisiting a potential system design of an industry partner.
An Improved Lossless Information Hiding in SMVQ Compressed Images

Reversible data hiding has always been the focus of researchers, because it can retrieve the cover image at the same time when obtaining secret message. Chang, Nguyen and Lin's scheme [1] proposed a reversible compression code hiding based on search order coding (SOC) algorithm and SMVQ. Based on their method, we have proposed some modification in this paper. In the embedding phase, we used SOC or SMVQ to encode the index, and used 1 bit as an indicator to indicate that the index is encoded. In the extraction phase, the receiver extracts the secret message and reconstructs the original SMVQ index us simple steps. Experimental results show that in the case of a state codebook with a size of 32, the compression rate of the proposed method was 0.26 bpp, which is better than the compression rate of 0.36 bpp, proposed by Chang et al.'s scheme. In the case of a state codebook with a size of 64, the compression rate of the proposed method was 0.27 bpp, which is still better than the compression rate of 0.41 bpp proposed by Chang et al.'s scheme. In the above two cases, embedding rate was kept same as the scheme proposed by Chang et al.
On the Vietnamese Name Entity Recognition: A Deep Learning Method Approach

Named entity recognition (NER) plays an important role in text-based information retrieval. In this paper, we combine Bidirectional Long Short-Term Memory (Bi-LSTM) [7], [27] with Conditional Random Field (CRF) [9] to create a novel deep learning model for the NER problem. Each word as input of the deep learning model is represented by a Word2vec-trained vector. A word embedding set trained from about one million articles in 2018 collected through a Vietnamese news portal (baomoi.com). In addition, we concatenate a Word2Vec [18]-trained vector with semantic feature vector (Part-Of-Speech (POS) tagging, chunk-tag) and hidden syntactic feature vector (extracted by Bi-LSTM network) to achieve the (so far best) result in Vietnamese NER system. The result was conducted on the data set VLSP2016 (Vietnamese Language and Speech Processing 2016 [28]) competition.
Synonym Suggestion System using Word Embeddings

Word Embeddings have been used in various Natural Language Processing tasks. Recent methods of learning vector space representations of words have succeeded in capturing semantic and syntactic regularities in vector space. In this project, the main aim has been to use these word embeddings for generating synonyms that could be used to improve the quality of writing. This technique gives us the advantage of taking the context of a particular word into consideration. A regular dictionary framework may give synonyms which may not be used in the same context.
Pattern Retrieval on the Game of Go

It is said that the game of Go is the drosophila of artificial intelligence and machine learning. A series of AlphaGo and AlphaZero programs created history. For assisting human learning, we believe Go can also play the role of the drosophila. Interesting games and specific capturing races problems in Go game are recorded in Smart Game Format files. Valuable information takes place in these files. A Go information retrieval system would be helpful for assisting human learning Go. This article proposes a sequential algorithm to handle the Go pattern searching process in the Go game records, but the search time increases linearly with the size of game records. Thus, we make the index structure in the Go game database. The index structure also integrates methods of information retrieval and human domain knowledge of the Go game, four applications of proposed structure based on user needs. The index structure can improve the speed of pattern retrieval on the game of Go.
Data-Driven Human Modeling by Sparse Representation

Data-driven methods for modeling the realistic shapeof 3D human bodies need to access datasets that contain a large amount of 3D human models. We develop a method based on sparse representation in this paper to represent 3D human models as signals of patches. Unlike the general mesh compression approaches, all mesh models used in a data-driven human modeling framework have the same mesh connectivity. By using this property, we segment a human model into patches containing the same number of vertices. L0-learning algorithm is selected to train an overcomplete dictionary matrix, which in turn introduces sparse representation of the dataset. Patch signals of individual human models can then be extracted by using the dictionary matrix. With the ease of balance control between sparsity and accuracy that is featured by the chosen learning algorithm, a representation with high compression ratio and low shape-approximation error can be determined. The results have been compared with the widely used statistic representation based on principal component analysis (PCA) to verify the effectiveness of our approach. Moreover, the method for using sparse representation in the regression-based statistical modeling of 3D human models has been presented at the end of the paper.
A Cybersecurity Model for Decision-Making Problems Under Uncertainty Using Game Theory

We propose a new cybersecurity decision-making model based on game-theory and decision-making process. The proposed model allows the cyber defender to make optimal decisions by considering perceived damage, cost of potential defensive actions, and benefit to mission. We provide detailed examples and numerical results supporting our model to show how game theory can help the decision-making process for cybersecurity by focusing on situations of conflict between an attacker and a defender.
Classification of Open Unemployment Rate in Indonesia with Mamdani Fuzzy Inference System

Abstract Unemployment is a very common problem in developing countries such as Indonesia. One of the government’s efforts to overcome the problem is having skill training. However, the labor often faced difficulties in classifying unemployment in Indonesia. Therefore, the utilization of fuzzy logic is considered as an appropriate way to examine the state’s open unemployment rate by Mamdani method implementation. The input used in this case is the number of unemployment and labor force whereas the output of this system is the classification of open unemployment rate. Then the model output is compared to the classified data of the labor service and the result indicates that a matching percentage of 70,6 percent. It shows that the fuzzy model is able to determine the open unemployment rate in Indonesia.
Toward Voice-Assisted Browsers

Web browsers allow people to find, organize, and manage information on the web. While voice interaction research has evaluated the support of web search, the broader role of voice interactions within the browser have yet to be explored at depth. We report findings from a preliminary exploration of the challenges, opportunities, and directions of voice assistants embedded in modern web browsers. We drive our inquiry with Firefox Voice, a browser extension that implements a voice assistant into the Firefox desktop browser. Through a think-aloud study (n = 5), we explore the strengths and shortcomings of Firefox Voice to better understand the role that voice interaction can play in supporting people both in the browser and beyond it.
Analysis of Costs for Smart Contract Execution

This work analyses the differences in Gas cost consumption for the execution of individual functions between the two Smart Contract (SC) programming languages Solidity and Vyper. For this purpose, SCs for the most basic types ERC-20, MultiSig and Atomic Swap were created for both programming languages. To enable a comparison, it was ensured that a SC fulfills exactly the same task in each programming language. It turns out, that the calculated estimated Gas amount from the compiler is mostly meaningless. There is also no programming language, that is always superior to the other in Gas cost efficiency for each SC type and that Waves can be an alternative.