Reviewerly/exHarmony huggingface dataset:
Text Recognition by Air Drawing

In this paper, the text drawn by the user in the air is captured by the computer’s camera, followed by the identification of that text. So, the video camera will be turned on at the time of capturing the written text. Now the particular object is defined based on its colour to detect a movement done by the user. The colour is captured by the lower and upper bound of HSV (Hue Saturation Value), which finally leads to object detection at every instant. Lastly, the text will be recognized by the trained model. The model is trained by CNN (Convolution Neural Network) with an accuracy of 98.64% (training) and 98.24% (testing). For completion of the project, OpenCV, python programming language, and its libraries are used. This project requires only a camera and a defined object.
Exploiting Learned Symmetries in Group Equivariant Convolutions

Group Equivariant Convolutions (GConvs) enable convolutional neural networks to be equivariant to various transformation groups, but at an additional parameter and compute cost. We investigate the filter parameters learned by GConvs and find certain conditions under which they become highly redundant. We show that GConvs can be efficiently decomposed into depthwise separable convolutions while preserving equivariance properties and demonstrate improved performance and data efficiency on two datasets. All code is publicly available at github.com/Attila94/SepGrouPy.
Graph rewriting rules for RDF database evolution: optimizing side-effect processing

Purpose Graph rewriting concerns the technique of transforming a graph; it is thus natural to conceive its application in the evolution of graph databases. This paper aims to propose a two-step framework where rewriting rules formalize instance or schema changes, ensuring graph’s consistency with respect to constraints, and updates are managed by ensuring rule applicability through the generation of side effects: new updates which guarantee that rule application conditions hold. Design/methodology/approach This paper proposes Schema Evolution Through UPdates, optimized version (SetUp OPT ), a theoretical and applied framework for the management of resource description framework (RDF)/S database evolution on the basis of graph rewriting rules. The framework is an improvement of SetUp which avoids the computation of superfluous side effects and proposes, via <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"><m:mrow><m:mi mathvariant="normal">SetU</m:mi><m:msubsup><m:mi mathvariant="normal">p</m:mi><m:mrow><m:mi mathvariant="normal">opt</m:mi></m:mrow><m:mrow><m:mi>N</m:mi><m:mi>D</m:mi></m:mrow></m:msubsup></m:mrow></m:math> , a flexible and extensible package of solutions to deal with non-determinism. Findings This paper shows graph rewriting into a practical and useful application which ensures consistent evolution of RDF databases. It introduces an optimised approach for dealing with side effects and a flexible and customizable way of dealing with non-determinism. Experimental evaluation of <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"><m:mrow><m:mi mathvariant="normal">SetU</m:mi><m:msubsup><m:mi mathvariant="normal">p</m:mi><m:mrow><m:mi mathvariant="normal">opt</m:mi></m:mrow><m:mrow><m:mi>N</m:mi><m:mi>D</m:mi></m:mrow></m:msubsup></m:mrow></m:math> demonstrates the importance of the proposed optimisations as they significantly reduce side-effect generation and limit data degradation. Originality/value SetUp originality lies in the use of graph rewriting techniques under the closed world assumption to set an updating system which preserves database consistency. Efficiency is ensured by avoiding the generation of superfluous side effects. Flexibility is guaranteed by offering different solutions for non-determinism and allowing the integration of customized choice functions.
Provable Translational Robustness For Object Detection With Convolutional Neural Networks

In the following work object detection approaches with Convolutional Neural Networks (CNNs), which have provable characteristics regarding translational robustness, are proposed, evaluated in an application scenario, and compared to state of the art approaches. The provable characteristics are achieved by transferring theoretical results from wavelet theory and scattering networks to common CNNs used for classification. Therefore first a CNN is modeled as a scattering network. Needed parameters are estimated with data relevant for application scenarios. With the obtained information first the best feature extractor for a given application scenario is chosen. Afterward, the theory is extended to cover object detection networks. The proposed approaches are trained on simulated and real datasets and evaluated on real datasets.
AERO: Towards Energy-Efficient Autonomous Flight in MAVs Using Approximate Execution

Micro aerial vehicles (MAVs) are popular in many intelligent robot areas nowadays. As an battery-powered vehicle, the energy-efficiency of MAVs becomes the bottleneck for its wide adoption. This issue exacerbates for autonomous flight MAVs, since inefficient action executions may results in flight mission failure. In this work, we address the energy-inefficiency of deep Q-network (DQN) based autonomous flight of MAVs via approximate execution. We first investigate data sensing during flight process and observe two consecutive steps tend to perceive consistent data. We further analyze the decision making characteristics of DQN algorithm, and find the margins between maximum and the second largest Q-value in different steps are usually symmetrically distributed between action changes. Leveraging these two characteristics, we propose to Approximately Execute the autonomous flight pROcessing of MAVs for energy-efficiency improvement (AERO). AERO is composed of two techniques of AERO-S and AERO-AP. AERO-S uses the sensed data from the previous step to approximately infer the current sensing data. AERO-AP uses the symmetry of margins between the maximum and the second largest Q-value to skip the decision makings of some steps. Our evaluations show in environments with no obstacle, AERO achieves relative improvement of time (RIT) of 24.28% and relative improvement of energy (RIE) of 18.75% with no reduction of success rate. For environments with obstacles, AERO achieves 19.11% RIT and 12.72% RIE with no reduction of success rate.
Grey-box Fuzzing With Deep Reinforcement Learning And Process Trace Back

Grey-box fuzzing, as a software testing technique, can find possible program bugs such as memory leaks, assertion failures and invalid input by generate random data then input it into a program, and monitor program exceptions. With program running, grey-box fuzzing collected the branch information in order to guide choosing next seeds. In this paper, we try to use the concept of Markov decision processes to formalize grey-box fuzzing as a deep reinforcement learning problem, and use process trace back(Intel Process Trace) to collect branch information in order to improve its efficiency toward binary programs. The experiments show this approach can outperform baseline random fuzzing and gain performance improvement.
A short text sentiment classification method based on feature expansion and bidirectional neural network

Short text sentiment classification has a strong practical value. It also has many applications, one of which is opinion analysis. However, the traditional methods cannot effectively manage and analyze short texts because they generally contain fewer words, and their corresponding features are sparse. In this paper, we investigate the short text sentiment classification problem and propose a novel model by integrating feature expansion and the bidirectional neural network. Firstly, we use the new word discovery algorithm and the forward maximal matching to find out the new words of the short text. Secondly, we remove the deactivated words of the short text and use pkuseg for word separation to get the feature word set of the short text. Next, we use the conditional random field and the approximate lexicon (Synonyms) to expand the feature word set of a short text. Further, we use the Bi-LSTM (Bi-directional Long Short-Term Memory) to extract the features from the expanded short text. Finally, a SoftMax classifier is employed to obtain the sentiment classification results of the short text. Experiments show that this method outperforms many other methods in terms of accuracy, recall, and F1 value.
Securing folder directory using image encryption by Chaos and Rijndael Algorithm

Encryption is the process of encoding a text, image or file in order to secure it. In the world of social media, there is a vast growth in the online contents and the ways of communication. Online contents may be in various forms like text, image, audio, video etc. It is a need of the hour for several system developers to protect the user contents in any forms. There are various commonly used encryption algorithm used by researcher's for file security like DES, AES, Blowfish, Chaos Approach. Based on review of literature it is found that each of these algorithm is giving varying performance as context changes and there are some constraints observed among few of these algorithm.The proposed work aims at addressing few of the challenges as issues and constraint of existing methods by identifying problem areas and devising a solution. Based on the recent study, the primary focus of this work would be Chaos and AES. Actually it uses a modified chaos which has proved better in terms of results by creating high level of confusion while encrypting the file to be secured. So proposed system will try to contribute a better encryption for image security. Performance of the proposed algorithm will be evaluated using key sensitivity, algorithm complexity, histogram analysis.
Improvement Measures of the Internal Teaching Quality Management System in Higher Vocational College

short-paper Improvement Measures of the Internal Teaching Quality Management System in Higher Vocational College Share on Author: Chunguang Feng Hainan College of Software Technology Hainan College of Software TechnologyView Profile Authors Info & Claims IPEC2021: 2021 2nd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2021 Pages 545–548https://doi.org/10.1145/3452446.3452579Online:25 April 2021Publication History 0citation14DownloadsMetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Encryption Of Voice Calls Using CryptoBin Algorithm

Due to the constant attack on network communications and mobile networks by hackers and the few security means of communication networks, a proposal to encrypt voice calls had to be developed. This proposal encrypts voice calls before they reach the phone and decrypts them when they are out of the phone at the receiver. The idea is to encrypt audio data without using servers or intermediary systems between the telephone device and the networks. CryptoBin encryption algorithm is used to encrypt and decrypt the audio data, which are characterized as a new and unknown algorithm, and characterized by speed and strength in encryption. This algorithm uses two secret keys to increase the strength of encryption and security.
A Primitive Solution to Avoid Data Deduplication in Cloud and Overcome Security Threats

In the recent era, a large number of private and corporate users redistribute the data to the cloud providers. In this extremely large amount of data, there exists large amount of copies of repeated duplicate data. The term data duplication is nothing but compressed repeated data. In the existing system, the process of access control is implemented along with data deduplication which can be done on encrypted data as well. In the proposed system, the user will upload the files to the cloud and it will get changed into an encrypted format by the process of hashing which is done by using SHA-256 algorithm. Based on the existence of the file on the cloud server the user may upload or download the same. In addition, DDoS attack detection and implementation of two types of attacks, collusion attack and sybil attack detection for user identity is addressed. Experimental analysis shows that the storage utilization can be maximized and the efficiency and security of cloud server is improved.
Optimized execution method for queries with materialized views: Design and implementation

A query application for On-Line Analytical Processing (OLAP) examines various kinds of data stored in a Data Warehouse (DW). There have been no systematic studies that look at the impact of query optimizations on performance and energy consumption in relational and NoSQL databases. Indeed, due to a lack of precise power calculation techniques in various databases and queries, the energy activity of several basic database operations is mostly unknown, as are the queries themselves, which are very complicated, extensive, and exploratory. As a result of the rapidly growing size of the DW system, query response times are regularly increasing. To improve decision-making performance, the response time of such queries should be as short as possible. To resolve these issues, multiple materialized views from individual database tables have been collected, and queries have been handled. Similarly, due to overall maintenance and storage expenses, as well as the selection of an optimal view set to increase the data storage facility’s efficacy, materializing all conceivable views is not viable. Thus, to overcome these issues, this paper proposed the method of energy-aware query optimization and processing, on materialized views using enhanced simulated annealing (EAQO-ESA). This work was carried out in four stages. First, a Simulated Annealing (SA) based meta-heuristic approach was used to pre-process the query and optimize the scheduling performance. Second, the optimal sets of views were materialized, resulting in enhanced query response efficiency. Third, the authors assessed the performance of the query execution time and computational complexity with and without optimization. Finally, based on processing time, efficiency, and computing cost, the system’s performance was validated and compared to the traditional technique.
Sarod: Efficient End-To-End Object Detection On SAR Images With Reinforcement Learning

Generally, object detection on Synthetic-Aperture Radar (SAR) images is known to be more challenging than that in Electro-Optical (EO) satellite images because SAR images have non-negligible speckle noise and require extensive data pre-processing. Nevertheless, object detection in SAR images is important, as SAR imagery can be obtained under severe weather and time conditions. While many recent object detection approaches on SAR imagery focus on improving detection accuracy, few studies focus on improving processing efficiency. In fact, there are significant challenges and trade-offs to achieve both high accuracy and efficiency at the same time. In this work, we introduce SAROD, a novel efficient end-to-end object detection framework on SAR images based on Reinforcement Learning (RL) to balance the tradeoffs. Our proposed model consists of two detectors, coarse and fine-grained detectors, with an RL agent, where RL has not yet been utilized for object detection on SAR imagery. Our model was evaluated on a challenging SAR imagery dataset, achieving performance comparable to state-of-the-art detectors while maintaining high efficiency of source data usage.
Could Blockchain Help With COVID-19 Crisis?

The novel coronavirus that causes the Coronavirus Disease 2019 (COVID-19) has spread all over the world at an unprecedented rate. With growing recognition of the distributed nature of health services, the technology of blockchain has recently reached the impetus of the healthcare domain. This article provides: 1) a panoramic overview of existing solutions and scenarios incorporating blockchain to combat COVID-19 in the healthcare domain along with their benefits and challenges; as well as 2) a framework that will facilitate new research activities on this subject.
Research and Improvement of TextRank Algorithm Adding Degree Adverbs

Abstract Textrank algorithm is an approach to exact keywords in articles which will be the premise to find useful information in massive network data. After analysing the characteristics of this algorithm, this paper improves the accuracy of keyword extraction by adding degree adverbs. Based on the function of the original algorithm, a degree adverb table is established, which contains all the degree adverbs that need to be marked. By adding the degree adverbs marker, the results of keyword extraction for the same text will be improved. According to experimental studies, for most articles, adding degree adverbs judgment function increases the importance of key words in these articles. At the end of this paper, the keyword extraction results for different types of articles is demonstrated and compared.
A Visual Tour of Bias Mitigation Techniques for Word Representations

Word vector embeddings have been shown to contain and amplify biases in data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and attenuate these biases in word representations. In this tutorial, we will review a collection of state-of-the-art debiasing techniques. To aid this, we provide an open source web-based visualization tool and offer hands-on experience in exploring the effects of these debiasing techniques on the geometry of high-dimensional word vectors. To help understand how various debiasing techniques change the underlying geometry, we decompose each technique into interpretable sequences of primitive operations, and study their effect on the word vectors using dimensionality reduction and interactive visual exploration.
Negotiating the Digital Dystopia: The Role of Emotion, Atmosphere and Social Contact in Making Decisions about Information Use in Physical and Digital Contexts

During the pandemic we have found that the experience of interaction online, whether with other people, or with information is very different from what we do in person. And this experience is seldom compared favourably to physical, interaction. Thanks to the A level algorithm fiasco, algorithms are treated with growing scepticism and social media is increasingly regarded as intrinsically toxic associated with deadly misinformation, racism and hate speech. Yet, as information professionals know, digital delivery is ideal for certain types of information, such as journal articles or digital images of rare manuscripts. AI offers the potential to interrogate and make connections between digital collections at an unprecedented scale. But can we build trust if users regard digital interaction and computational techniques with scepticism or even hostility? This article will explore these questions, and consider the importance of emotion and affect in interaction with digital and physical information environments.
Job Recommendation System Using Content and Collaborative-Based Filtering

Dealing with the big amount of information on the web, employment seeker constantly spends hours discover useful ones. We do all this process in an easy manner. Recommendation systems usually consist of exploiting relations among understood features and content that describes services and products (content-based filtering) or the overlap of comparable users who interacted with or rated the goal item (collaborative filtering). We reveal a comparison between content filtering and based that is collaborative.
Big Data Economics

During the last 10 years, the amount of data generated, stored, and distributed has surpassed one zettabyte. This data consists of unstructured data spanning almost all aspects of society. Big data refers to technologies by which such gigantic data sets can be analyzed and utilized in a variety of new scientific, technological, and economic applications. The dark side of the evolution is that these data can also be exploited for purposes such as social population control and violation of human rights. This chapter provides a brief introduction to the technology and lists some of the benefits and downsides the technology may generate. Nevertheless, big data economics will play a major role in the digital economy and new economic thinking, and methods must be developed to understand and utilize all aspects of this emerging, complex, and interdisciplinary field.
Single Image Super-resolution Reconstruction Algorithm Based on Deep Learning

Abstract Recover the high-resolution image from a single low-resolution image is known as single image super-resolution reconstruction (SISR). SSIR is currently a research hotspot in computer vision. In this paper, we study the feature learning module of the magnification-arbitrary network for Super-Resolution(Meta-SR), and propose an improved method of image feature learning. Its idea is multi-scale residual dense block (MRDB). We integrate it into the existing structure of the Meta-SR network. As a result, the same network has increased the receptive field, and it can obtain more detailed features of the image. This structure can be extended to other existing SISR network models. In the published and standard test set Set14, compared with the interpolation method, the average PSNR value of the algorithm in this paper increases by 3.79dB, 3.01dB and 2.85dB at amplification factors 2, 3 and 4, respectively. Through the analysis of SISR results, it can be obtained that the improved method proposed in this paper has a significant improvement in all indicators, and can more fully extract the image detail features, thereby improving the quality of the reconstructed image.
Deployment coordination for cross-functional DevOps teams

Software stability and reliability are the core concerns of DevOps. They are improved by tightening the collaboration between developers and operators in cross-functional teams on the one hand and by automating operations through continuous integration (CI) and infrastructure as code (IaC) on the other hand. Ideally, teams in DevOps are fully independent. Still, their applications often depend on each other in practice, requiring them to coordinate their deployment through centralization or manual coordination.
Research on Visualization Method of Congestion Pattern in Urban Transportation Hub

With the deepening of the urbanization process, the number of motor vehicles is increasing day by day, plenty of cities are encountering the problem of traffic saturation. There are many interlaced road sections in urban traffic hubs, which makes the traffic jam during rush hours extremely serious. In order to clearly display the daily conditions of traffic hubs, and better assist relevant personnel to conduct the congestion research and analysis, this paper adopts the data visualization method to analyze the vehicle trajectory data of traffic hubs, which can be used to display various congestion patterns and causes, and provide references for road planning decision-making.