Reviewerly/exHarmony huggingface dataset:
Design of a “Roadrunner” Mast-Type Robot Arm Based on a Small UAV

Aiming at the limited loading capacity of small UAVs, a design of a mast-like manipulator imitating a “roadrunner” was proposed in this research. Designed in accordance with the bionic principle, it relies on the flexible and slender “neck” to perform effective reconnaissance when maneuvering on the ground and in the air, realizing high detection on the ground and looking down in the air. This paper completes the overall structure design of the manipulator based on the main technical indicators, using the D-H method to establish the pose matrix of the manipulator, and MATLAB programming to solve the forward and inverse kinematics of the manipulatorand determine the joint angle and arm length parameters. The design of this robotic arm enables the UAV to make full use of ground objects without being trapped by ground objects. It can implement the “roadrunner tactics” with high concealment and rapid three-dimensional penetration, which provides a brand new method of reconnaissance and guided strike for special operations such as urban combat and anti-terrorism.
Implementing Blockchain Technology for Health-Related Early Response Service in Emergency Situations

Throughout late 2019 and in 2020, we have witnessed the COVID19 epidemic, where the self-isolation of newly and potential infected people helped stop the virus spreading. The detection of potential patients in the early disease phase has proven crucial, as did monitoring patients with early symptoms. For any country, it was challenging to monitor people in self-isolation, and also to detect, predict, and observe changes in their health in the early disease phase. The lack of control and untimely access to such information increased the number of patients and victims. For the purpose of monitoring already confirmed infected cases, countries had to open and maintain temporary hospitals. In this paper, we present a model on how losses in financial, logistic, equipment, and human resources could be avoided by applying blockchain technology. On the basis of the immutability and security properties of blockchain, data loss and patients’ malfeasance would be impossible.
On intermediate justification logics

Abstract We study arbitrary intermediate propositional logics extended with a collection of axioms from (classical) justification logics. For these, we introduce various semantics by combining either Heyting algebras or Kripke frames with the usual semantic machinery used by Mkrtychev’s, Fitting’s or Lehmann and Studer’s models for classical justification logics. We prove unified completeness theorems for all intermediate justification logics and their corresponding semantics using a respective propositional completeness theorem of the underlying intermediate logic. Further, by a modification of a method of Fitting, we prove unified realization theorems for a large class of intermediate justification logics and accompanying intermediate modal logics.
Differences and benefits in using a financial consolidation software: CLOUD, LOCAL, SAAS OR PAAS

"The current national and international economic climate is continually changing, and a company’s ability to adapt its services is critical. As a result, in recent years, the interaction between technology and businesses has grown. It’s crucial to think about how finance adds value to a business as the future of accounting unfolds. The way business and finance functions create value is being profoundly transformed by digital technologies. To develop value, businesses must think and behave differently in the digital age. To emphasize the merits of each platform, we offered numerous types of software and various benefits of using them. This paper will address accountants’ worries about software, such as data security and a lack of features, as well as how the software works and the numerous benefits and drawbacks it has."
The Impact of Table and Index Compression

The amount of stored data is growing rapidly, and it brings considerable challenges. The enormous growth in the volume of data makes storage one of the biggest cost elements. For this purpose, relational databases are used very often. Fast access to data is becoming increasingly important and great emphasis is placed on its improvement. This paper deals with the impact of table and index compression on data access time and CPU costs, which is compared in nine different scenarios of different combinations of compressed and uncompressed indexes over compressed and uncompressed tables of various row numbers.
Positive Unlabeled Learning

Machine learning and artificial intelligence (AI) are powerful tools that create predictive models, extract information, and help make complex decisions.
Seeing various adventures through a mirror: Detecting translator’s stylistic visibility in Chinese translations of<i>Alice’s Adventure in Wonderland</i>

Abstract The visibility of translator’s style is a much-discussed topic in translation studies with the application of corpus tools. So far, however, no agreement has been reached. The present study aims to explore this issue by a comparison of three Chinese translations of the English literary work, Alice’s Adventure in Wonderland, using two stylometric techniques, bootstrap consensus tree analyses, and bootstrap consensus network analyses. The results show that all the three Chinese translations preserved the style of the original text and that individual translator’s style could not be identified based on the entire set of part-of-speech (POS) tags. Furthermore, a feature selection method (the chi-square metric) was used to obtain the top fifteen distinctive POS unigrams and bigrams, and these distinctive features successfully identified translatorial fingerprints across the three translations examined. The findings suggest that translators have their own stylistic choices when translating the same text, but their stylistic differences can only be detected by distinctive features. Our attempt to combine feature selection methods and stylometric techniques may offer new insights into the investigation of translator’s stylistic visibility in translation studies.
Contrast enhancement: Cross-modal learning approach for medical images

Contrast is an imperative perceptible attribute embodying the image quality.In medical images, the poor quality, specifically low contrast inhibits precise interpretation of the image.Contrast enhancement is, therefore, applied not merely to improve the visual quality of images but also enabling them to facilitate further processing tasks.In this paper, we propose a contrast enhancement approach based on cross-modal learning using two-way Generative Adversarial Network (GAN), where U-Net augmented with global features acts as a generator.Besides, individual batch normalization has been used to make generators adapt specifically to their input distributions.The proposed method learns the global contrast characteristics of T1-w brain magnetic resonance images (MRI) to improve the contrast of T2-w images.The experiments were conducted on a publicly available IXI dataset.Comparison with recent CE methods and quantitative assessment using two prevalent metrics FSIM and BRISQUE validate the superior performance of the proposed method.
Augmented Intelligence: Deep Learning Models for Healthcare

Actionable insights and learning from a highly complex biomedical dataset is a key challenge in smart healthcare. Traditional data processing algorithms fails to provide the better results with complex data. Recent advancements in artificial intelligence methods introduced an end to end complex learning models called deep neural networks often referred as deep learning models. In this chapter, we reviewed recent advancements of deep learning models and its applications related to healthcare. We also discussed the challenges and opportunities faced by the deep learning models.
Analysis of Online Media Content Using Data Science Techniques

online media content plays an important role on the social media. It consists of textual, visual, audio, video, images and animations. The idea of the web is to link different content that is available on different web sites. This may include personal web pages, YouTube channels, files, email messages and more. The content present on the social media is not all genuine and every content is not quality content. Some websites use fake thumbnails to attract content to their site but the content is of low quality. Google penalized many websites who are doing this type of work. There should be a content management system to manage the content on all these types of websites. The websites should make their content user friendly so that the site can be navigated easily. This paper provides review of various data science techniques used on social media dataset for online media content. In this survey, we also describe some online content problems that can be solved by using data mining techniques.
Web Browser Forensics for Retrieving Searched Keywords on the Internet

Nowadays, most of the reported cybercrimes use the Internet. It is seen that Web Browsers leave forensically sound footprints of Internet browsing activities on the computer’s storage media. The traces once retrieved stand as crucial hints that help the investigator to reconstruct various events that the suspect has done. However, the usage of private or portable web browsers prevents the creation of those files on the Suspect’s Computer. This makes the job of investigators more challenging. But, the remnants of browsing activities done using any web browser is recorded in the Physical Memory of the Computer. Hence, analyzing the Physical Memory Dump of the suspect's machine, collected as part of Live Forensics, may provide lots of forensically sound evidence related to the Internet-related activities in a cybercrime investigation. This information provides highly crucial information which can render hints in the investigation process. One of the most important features of any Cyber Forensics Analysis Tool is keyword searching. But identifying and finalizing the keywords to be searched during the analysis process in a particular Cyber Crime is always challenging for the investigators. The task of retrieving searched keywords is crucial as it directly provides the criminal background of the Suspect if any. This information enables cyber forensics investigators to conduct a focused approach while analyzing the evidence. This paper mainly presents a novel methodology for the reconstruction of searched keywords from the Physical Memory Dump collected from the Suspect’s Windows 10 Computers. A methodology for retrieving keywords from the browser files generated by Web Browsers in the storage media has also been described. The keywords retrieved in this way helps the Investigators to identify the associated highly crucial information during the deep-dive analysis of the storage media in Offline Forensic Analysis.
Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble Models

Random Forest algorithm is a prediction technique where a set of tree predictors are combined to construct an ensemble model. If a set of conditions are satisfied, we can affirm that random forest avoids overfitting and converges. On the other hand, grammatical evolution, the popular variant of genetic programming where solutions are built following a grammar, has been successfully applied to a plethora of different problems. Among them, symbolic regression is one of the hits of grammatical evolution. Although encoded in codons and decoded by a grammar, solutions in grammatical evolution are trees that represent mathematical expressions. In this paper, we investigate the convenience of combining the best of both approaches, and we propose Random Structured Grammatical Evolution as an adaptation of Random Forest to a symbolic regression problem. Using structured Grammatical Evolution, a set of weak predictors are built and combined on an ensemble model for prediction.
CellSegNet: an adaptive multi-resolution hybrid network for cell segmentation

Image segmentation is one primary area in which deep learning has made a major contribution to medical image analysis. The automatic and precise segmentation of cells in cytopathology, or cytology for short, can significantly reduce the diagnostic work from pathologists. The biomedical image segmentation task routinely employs an encoder-decoder structure, e.g. U-Net, in which the receptive field is often fixed. However, to achieve a better morphological segmentation performance, we empirically found receptive field should be correlated with cell size by differential structures. In this paper, we proposed a novel deep-learning based cytology image segmentation model, namely CellSegNet. This model can dynamically catalog cells by their size, and subsequently fit to their corresponding light-weight structures, characterized with weighted multiple receptive fields to better retrieve feature extraction. The proposed model can outperform other state-of-art biomedical image segmentation networks with observable improvements. Moreover, the high interpretability of the proposed model can be flexibly extended to other cytology datasets. The source code in the experiments and part of our collection of cervical images are publicly available at <a href="https://github.com/SJTU-AI-GPU/CellSegNet">https://github.com/SJTU-AI-GPU/CellSegNet</a>.
LoG and Structural Based Arbitrary Oriented Multilingual Text Detection in Images/Video

Text in an image or a video affords more precise meaning and text is a prominent source with a clear explanation of the content than any other high-level or low-level features. The text detection process is a still challenging research work in the field of computer vision. However, complex background and orientation of the text leads to extremely stimulating text detection tasks. Multilingual text consists of different geometrical shapes than a single language. In this article, a simple and yet effective approach is presented to detect the text from an arbitrary oriented multilingual image and video. The proposed method employs the Laplacian of Gaussian to identify the potential text information. The double line structure analysis is applied to extract the true text candidates. The proposed method is evaluated on five datasets: Hua's, arbitrarily oriented, multi-script robust reading competition (MRRC), MSRA and video datasets with performance measures precision, recall and f-measure. The proposed method is also tested on real-time video, and the result is promising and encouraging.
Small Object Detection of Remote Sensing Images Based on Residual Branch of Feature Fusion

In recent years, the detection of remote sensing images has been developed widely, and small objects have been paid more and more attention. The existing small object detection methods fuse the multi-scale features of different layers directly when using the feature pyramid network. However, due to the decrease of channels in feature fusion, the top-level feature of pyramid will lose information of the object, which is disadvantageous to detect small object ion. In order to fuse multi-scale features more effectively, we propose an object detection method based on the residual branch of feature fusion (RBFF), which is specially used to detect small objects. Our approach improves the network structure of the feature pyramid. We also recalculated the weights to reduce the semantic gap in feature fusion. In addition, we also introduce sub-pixel convolution to reconstruct the low-frequency information of the feature map accurately, to obtain the feature map with more information. The experimental results show that our method has a good effect.
Research on UAV Human Tracking Method Based on Vision

Based on the Adaboost method and the MHT multi-hypothesis tracking algorithm, the DMW dynamic minimum window algorithm is proposed to detect and track the human body image. It optimizes the image processing speed, and achieves the effect of smooth operation on the mobile processor. Combining this algorithm with UAV target tracking application scenarios, a complete UAV visual tracking system design scheme is proposed. In the system implementation, the third-generation Raspberry Pi's quad-core A53 processor is used. According to the work flow of image acquisition, target detection and tracking, and drone flight control, the automatic tracking flight function is successfully realized. The effect of algorithm application is evaluated qualitatively and quantitatively.
Effective Prediction Modes Design for Adaptive Compression With Application in Video Coding

Adaptive prediction is an important tool for efficient compression of non-stationary signals. A common approach to achieve adaptivity is to switch between a set of prediction modes, designed to capture variations in signal statistics. The design poses several challenges including: i) catastrophic instability due to statistical mismatch driven by propagation through the prediction loop, and ii) severe non-convexity of the cost surface that is often riddled with poor local minima. Motivated by these challenges, this paper presents a near-optimal method for designing prediction modes for adaptive compression. The proposed method builds on a stable, open-loop platform, but with a subterfuge that ensures that the design is asymptotically optimized for closed-loop operation. The non-convexity is handled by deterministic annealing, a powerful optimization tool to avoid poor local minima. To demonstrate the impact of the proposed approach on practical applications, we consider adaptive, transform-domain predictor design for enhancing standard video coding. Experimental results validate the benefits of the proposed design in terms of significant performance gains for both predictive compression systems in general and video coding in particular.
Fraud Detection System in Online Ride-Hailing Services

Advances in technology and the human tendency to use virtual services are constantly increasing in all areas of life. Online ride-hailing services are not an exception to this rule. Due to the financial transactions in these systems, the possibility of fraud by profiteers also increases which can affect the revenue of such services significantly. In this paper, we propose a system that can detect fraud in online ride-hailing systems. We address frauds that occur using the ride collusion method or creating a fake ride using GPS spoofing applications. We have used real unlabeled data from one of the largest ride-hailing companies in Iran for this purpose. Our system first identifies the most important features that help us distinguish real rides from fake rides, then it uses unsupervised learning methods to detect ride anomalies. After identifying the anomalies and examining these rides, we label the data, and use supervised learning methods to construct the fraud detection model.
Automatic Recognition of Pictograms with Convolutional Neural Network Approach for Literacy

Literacy is a fundamental and intrinsic right for every person; therefore, government agencies, NGOs, and private donors seek to implement sustainable and long-term plans that allow citizens to access quality, inclusive, and equitable education. But despite their efforts, illiteracy has not yet been fully eradicated, so educational programs and tools have been created to work to solve this problem. In this research, a web application is developed that takes the resources of the Google QuickDraw API to generate an interactive image interpretation tool, which aims to support the learning of reading and writing in people with illiteracy. The tool allows an illiterate person to draw pictograms that are interpreted by the software and through convolutional neural networks with deep learning, a prediction of the image is generated. The result is presented in a graphical user interface, which displays a real image corresponding to the prediction together with text and audio assistance, essential for user learning. The results show a performance of 75% accuracy in the training and 56% in the test. The performance tests yielded indicate that the tool meets the minimum software requirements for its future implementation in institutions that work in the education of people with illiteracy.
Report on the 5th workshop on international and interdisciplinary perspectives on children &amp; recommender and information retrieval systems (KidRec 2021) at IDC 2021

We summarize outcomes from the 2021 KidRec Workshop, co-located with the ACM Interaction Design and Children conference. The theme of the 5 th edition of KidRec was Search and Recommendation Technology through the Lens of a Teacher. During the workshop, we discussed why teachers are one of the main stakeholders when designing information retrieval tools for the classroom and how to involve them as part of the research and development process to ensure that the resulting tools are suitable and supportive for learning. Date : 27 June, 2021. Website : https://kidrec.github.io/.
Image quality assessment: Learning to rank image distortion level

Over the years, various algorithms were developed, attempting to imitate the Human Visual System (HVS), and evaluate the perceptual image quality.However, for certain image distortions, the functionality of the HVS continues to be an enigma, and echoing its behavior remains a challenge (especially for ill-defined distortions).In this paper, we learn to compare the image quality of two registered images, with respect to a chosen distortion.Our method takes advantage of the fact that at times, simulating image distortion and later evaluating its relative image quality, is easier than assessing its absolute value.Thus, given a pair of images, we look for an optimal dimensional reduction function that will map each image to a numerical score, so that the scores will reflect the image quality relation (i.e., a less distorted image will receive a lower score).We look for an optimal dimensional reduction mapping in the form of a Deep Neural Network which minimizes the violation of image quality order.Subsequently, we extend the method to order a set of images by utilizing the predicted level of the chosen distortion.We demonstrate the validity of our method on Latent Chromatic Aberration and Moiré distortions, on synthetic and real datasets.Figure 1.Automatically ordered images (a)-(c) from the TID2013 [15] by their predicted Lateral Chromatic Aberration level (0, 2, 4 pixels accordingly) using the proposed method in this paper; the "<" sign indicates that the left image is less distorted than the one on the right; (d) a plot of the expected (the x-axis) versus the predicted image order (the y-axis).
Superpixel Pre-segmentation of HER2 Slides for Efficient Annotation

Supervised deep learning has shown state-of-the-art performance for medical image segmentation across different applications, including histopathology and cancer research; however, the manual annotation of such data is extremely laborious. In this work, we explore the use of superpixel approaches to compute a pre-segmentation of HER2 stained images for breast cancer diagnosis that facilitates faster manual annotation and correction in a second step. Four methods are compared: standard simple linear iterative clustering (SLIC) as a baseline, a domain adapted SLIC, and superpixels based on feature embeddings of a pretrained ResNet-50 and a denoising autoencoder. To tackle oversegmentation, we propose to hierarchically merge superpixels, based on their content in the respective feature space. When evaluating the approaches on fully manually annotated images, we observe that the autoencoder-based superpixels achieve a 23% increase in boundary F1 score compared to the baseline SLIC superpixels. Furthermore, the boundary F1 score increases by 73% when hierarchical clustering is applied on the adapted SLIC and the autoencoder-based superpixels. These evaluations show encouraging first results for a pre-segmentation for efficient manual refinement without the need for an initial set of annotated training data.