Reviewerly/exHarmony huggingface dataset:
Fuzzy Logic Control Design Based on the Genetic Algorithm for a Modular Servo System

In this article, an optimal Fuzzy PID controller is designed based on genetic optimization for a servo system. For the purpose of the study, an INTECO modular servo system is used in combination with Matlab/Simulink. The proposed controller is with a very simple structure. It uses a one-input fuzzy inference with three rules and six tuning parameters. Also, a conventional fuzzy logic controller with two inputs, 49 rules and three tuning parameters is used. As well a comparison is made with a Standard PID controller. The results of this study show that the system with the synthesized optimal Fuzzy PID controller based on genetic optimization has similar quality characteristics in comparison with the system with a conventional fuzzy PID controller and has a better performance in comparison with a standard PID controller. The use of the genetic algorithm for the synthesis of controller allows a significant reduction in the fuzzy rules base and simple tuning of the controller parameters.
Intellectual property information services and the impacts on academic libraries’ transformation from the perspective of Chinese university libraries

Approximately 100 Intellectual Property Information Services Centres have been established in Chinese university libraries, more than 80% of them since 2017. The context of this boom in Intellectual Property Information Services Centres is the rapidly increasing number of patent applications in China, as well as an unacceptably low transfer ratio. Do Intellectual Property Information Services Centres represent a promising direction for university library transformation? This is the central issue addressed in this article. The characteristics of the Chinese evolutionary path and driving forces are discussed, and distinctive intellectual property information service practices are studied and summarized. Comparisons are made with the USA, the UK, Europe and India. With Intellectual Property Information Services Centres, university libraries can evolve from information providers to innovation catalysts, and establish closer connections between universities, communities and industries. The impacts of Intellectual Property Information Services Centres on university librarianship are multifaceted. The trends and challenges of intellectual property information services are also discussed in the article.
Review of subjective quality assessment methodologies and standards for compressed images evaluation

Lossy image compression algorithms are usually employed to reduce the storage space required by the large number of digital pictures that are acquired and stored daily on digital devices. Despite the gain in storage space, these algorithms might introduce visible distortions on the images. However, users typically value the visual quality of digital media and do not tolerate any distortion. Objective image quality assessment metrics propose to predict the amount of such distortions as perceived by human subjects, but a limited number of studies have been devoted to the objective assessment of the visibility of artifacts on images as seen by human subjects. In other words, most objective quality metrics do not indicate when the artifacts become imperceptible to human observers. An objective image quality metric that assesses the visibility of artifacts could, in fact, drive the compression methods toward a visually lossless approach. In this paper, we present a subjective image quality assessment dataset, designed for the problem of visually lossless quality evaluation for image compression. The distorted images have been labeled, after a subjective experiment held with crowdsourcing, with the probability of the artifact to be visible to human observers. In contrast to other datasets in the state of the art, the proposed dataset contains a big number of images along with multiple distortions, making it suitable as a training set for a learning-based approach to objective quality assessment.
HYDRA: Feedback-driven black-box exploitation of injection vulnerabilities

Injection vulnerabilities remain an omnipresent threat to web application security. These issues arise when user-supplied input is included in commands constructed by the application without applying adequate validation and filtering, permitting attackers to modify the resulting instructions. Tools used in real-world security assessments commonly employ a static list of malicious input strings to be submitted to the system under test (SUT) to gauge the presence of vulnerabilities. However, sanitizing filters may cause these simulated attacks to fail, even if they only mitigate a subset of potentially harmful values. This may result in a false sense of security. This work introduces HYDRA, a feedback-driven black-box security testing approach for the exploitation of injection vulnerabilities. It is capable of constructing inputs designed to evade such imperfect filters while allowing users to define and rank output contexts, abstract locations in the output of the SUT that are associated with desirable semantics (for instance, allowing the execution of JavaScript code). Starting with an innocuous initial input string that is submitted to the SUT and appears anywhere in the output, HYDRA identifies the initial output context. It extends the input string with the goal of reaching contexts that are deemed ”better” according to domain knowledge. This process continues until an ”ideal” output context is reached, usually corresponding to an exploit that impacts the security of the SUT. In addition to this dynamic approach, we present a static variant based on combinatorial security testing. We instantiate our approach by targeting cross-site scripting (XSS) vulnerabilities, detailing the unique challenges posed by HTML parsing, and implement this application of HYDRA in a prototype tool. The evaluation shows that our implementation is able to evade faulty filters and is effective at identifying injection vulnerabilities while remaining more flexible than existing approaches by allowing users to define desirable output contexts. Based on the results of our evaluation, we are confident that including the HYDRA approach in security assessments will increase the number of identified XSS vulnerabilities, particularly those that are difficult to exploit. We anticipate that an application to other classes of vulnerabilities such as SQL injections will significantly advance the state of the art.
Image editing with varying intensities of processing

Current image editing algorithms prevailingly involve specific processing intensities. After the network training phase, the definite mapping is carried on testing images, which leads to under- or over-editing in certain cases. However, for many real problems, having access to diverse intensities of processing of the output is preferable. The hypothesis is that the part of parameters that correspond to the intensity of processing of the network are implicitly embedded in the whole parameter set and can be explicitly extracted and utilized, even with limited supervision. In this paper, we introduce the concept of Intensity of Processing, termed as IP, and propose image editing with Varying Intensities of Processing, termed as VIP. Given the input and corresponding fully-edited output, we would like to not only figure out the particular mapping from input to output, but also be able to obtain multiple continuous intermediate states with in-between intensities of processing. Refer to the network that deals with the original image editing operation as Base Network. By adding an extra module, which we call Condition Encoder, to condition and regulate the Base Network, we make the whole image editing process controllable. Moreover, we develop a triplet loss and construct the sequentiality of generated intermediate states with limited supervision. A significant advantage of our method is that it does not require groundtruth images of the middle states during training, which substantially relaxes the range of applicable problems. Extensive experiments demonstrate the effectiveness and potentiality of the proposed framework; discussion and future work are also provided.
Real-time On-board Path Planning for UAS-based Wildfire Monitoring

The use of Unmanned Aircraft System (UAS) in wildfire monitoring is increasing significantly due to its benefits in terms of flexibility, lower cost, and safety. To effectively monitor and collect data about a wildfire, UAS path planning is essential. Many of the applications adopt a centralized approach that uses a computer on the ground to provide path planning information for individual UASs. The applicability and robustness of such centralized approaches are limited due to the challenging wildfire environments. In addition, very few of the existing applications focus on the most actively spreading region of the fire which needs special monitoring attention. In this paper, we present an on-board path planning algorithm for UAS-based wildfire monitoring. The proposed algorithm uses real-time data collected by the UAS to direct the UAS to monitor the wildfire with special attention to the most active regions of the fire. Simulation results have been presented to show the effectiveness of the algorithm in different simulated wildfire scenarios.
Graph Convolutional Matrix Completion via Relation Reconstruction

To alleviate sparsity and improve recommender systems performance, it is necessary to go beyond modeling user-item interactions and take auxiliary information into account. Besides user-item interactions, auxiliary information can be used to build relation graphs. Recently, Graph Convolution Networks (GCNs), which can integrate content information and structural information of nodes, have been demonstrated to be powerful in learning on graph data and applied in recommendation systems. However, existing approaches do not consider multiple types of relations between nodes and high-order structural information. In this paper, we propose a new model called Graph Convolutional Matrix Completion via relation reconstruction (RE-GCMC) to capture structural information and relations between nodes in the graph. We construct user-user, item-item, and user-item relation graphs by evaluating the feature similarity of the nodes. Then, we introduce the Graph Convolutional Networks (GCNs) and self-attention mechanism to be applied in the graphs to refine feature embeddings. We apply the proposed model to four datasets and experimental results demonstrate that our approach outperforms state-of-the-art recommender baselines.
DDUO: General-Purpose Dynamic Analysis for Differential Privacy

Differential privacy enables general statistical analysis of data with formal guarantees of privacy protection at the individual level. Tools that assist data analysts with utilizing differential privacy have frequently taken the form of programming languages and libraries. However, many existing programming languages designed for compositional verification of differential privacy impose significant burden on the programmer (in the form of complex type annotations). Supplementary library support for privacy analysis built on top of existing general-purpose languages has been more usable, but incapable of pervasive end-to-end enforcement of sensitivity analysis and privacy composition. We introduce DDuo, a dynamic analysis for enforcing differential privacy. DDuo is usable by non-experts: its analysis is automatic and it requires no additional type annotations. DDuo can be implemented as a library for existing programming languages; we present a reference implementation in Python which features moderate runtime overheads on realistic workloads. We include support for several data types, distance metrics and operations which are commonly used in modern machine learning programs. We also provide initial support for tracking the sensitivity of data transformations in popular Python libraries for data analysis. We formalize the novel core of the DDuo system and prove it sound for sensitivity analysis via a logical relation for metric preservation. We also illustrate DDuo's usability and flexibility through various case studies which implement state-of-the-art machine learning algorithms.
Human Activity Recognition Using Time Series Pattern Recognition Model-Based on Tsfresh Features

Wearable devices are increasingly used to monitor people's activities, so data acquired from sensors are more available to establish models for recognizing human activities. This paper proposes a TSPR-model that can extract time-series features from sensor data by tsfresh in python. The model, which is less sensitive to data from different people, is able to mine the characteristics of sensor data and solve the noise of on-site data. Besides catering to the actual need in daily life, we perform subject-dependent and subject-independent evaluations. The accuracy has dramatically improved, especially in the latter, indicating that the proposed model is robust in distinctive subjects. Finally, due to different accelerometer patterns in different sexes and age groups shown in figures, we propose models based on clustered samples according to personal characteristics. They outperform models based on total samples.
Detection and Classification of Malaria Parasite Using Python Software

Around 228 million Malaria instances have been expected in 89 countries worldwide as per data given by the World Malaria Report of 2019. India has made a vision of a malaria-free country by 2027 and complete malaria elimination by 2030. The Plasmodium parasites are responsible for malaria disease. Malaria is a blood disorder caused due to bite of the female Anopheles mosquito. The disease malaria is of five different types depending upon the parasite species: Plasmodium vivax, Plasmodium ovale, Plasmodium malariae, Plasmodium Knowlesi, Plasmodium falciparum. The general method used to detect Plasmodium using blood films is Malaria microscopy. The WHO approved this microscopic malaria detection method. In this method, blood has been collected through the finger, pricked and spread on the clean grease-free plate glass, and left to be airdried. Thin blood smears have been utilized for parasite identification in the microscope. If the presence of parasites is low, thick blood smears are used. The accuracy of work depends on the skills and experience of the technicians. The work presents an algorithm to detect the presence of malaria parasites by counting the infected blood cells. The obtained images are Giemsa-stained blood smear images, and these colored images are firstly transformed into gray-scale images. Then basic image enhancement techniques have been employed to minimize noise and enhance the contrast of these images. The cells' segmentation has been done, followed by classification using python Jupyter notebook to detect the parasites efficiently. A Convolutional Neural Network classifier has been used to differentiate positive and negative cases of malaria. This classifier gives an accuracy of about 97%, which is quite good. It can be employed in laboratories to save quality time and for accurate determination.
Structural mapping and interpretation of lineaments related to the In Teria volcanism (southeastern Algeria) using Landsat 8 OLI TIRS images and aeromagnetic data

The In Teria region is located South-East of Algeria, in the North-East of the Illizi basin. Generally, this area is recognized as a volcanic region with circular craters controlled by faults. In this research, structural lineaments were extracted using aeromagnetic data and satellite images (Landsat 8 OLI TIRS), and the direction of distribution of volcanic craters was analyzed by the center-to-center method. The mains observations show, at different structural levels, the following results: 1- the directions of the surface lineaments are E-W, WNW-ESE and ENE-WSW while those of crustal scale are N–S, E-W, NNW-SSE to NW-SE. 2- The volcanic craters are located at the intersection of these two directions (E-W and NW-SE) with the N–S lineaments. Some of these directions are linked to a reactivation of Pan-African mega-shear zones (N–S, NW-SE to NNW-SSE) during the Cenozoic, and some others (NW-SE and NNW-SSE) had already been reactivated during the Cretaceous rifting. 3- The depth estimation methods used thanks to Euler deconvolution and Tilt derivative showed, for the first time, the presence of magnetic anomalies related to magmatic bodies at a shallow depth of 2.5–3 km. The spectral analysis method gave a depth of more than 11 km for the deep sources. 4- The depth of the basement estimated by 2D forward modeling according to an N–S profile which passes through the outcrop zone of the craters, varies from 200 to 6000 m. All these data and new results presented in this work helped to highlight on the dynamics of In Teria's volcanism and to derive an updated structural mapping of this volcanic region.
Identifying Challenges for OSS Vulnerability Scanners - A Study &amp; Test Suite

The use of vulnerable open-source dependencies is a known problem in today's software development. Several vulnerability scanners to detect known-vulnerable dependencies appeared in the last decade, however, there exists no case study investigating the impact of development practices, e.g., forking, patching, re-bundling, on their performance. This paper studies (i) types of modifications that may affect vulnerable open-source dependencies and (ii) their impact on the performance of vulnerability scanners. Through an empirical study on 7,024 Java projects developed at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAP</i> , we identified four types of modifications: re-compilation, re-bundling, metadata-removal and re-packaging. In particular, we found that more than 87 percent (56 percent, resp.) of the vulnerable Java classes considered occur in Maven Central in re-bundled (re-packaged, resp.) form. We assessed the impact of these modifications on the performance of the open-source vulnerability scanners OWASP Dependency-Check (OWASP) and Eclipse Steady, GitHub Security Alerts, and three commercial scanners. The results show that none of the scanners is able to handle all the types of modifications identified. Finally, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Achilles</i> , a novel test suite with 2,505 test cases that allow replicating the modifications on open-source dependencies.
Research on 3D Medical Image Segmentation based on improved 3D-Unet

Magnetic resonance imaging (MRI) images have high tissue resolution and rich contrast, and are important data for the study of hippocampal morphology. The hippocampal volume can not achieve the ideal segmentation effect in human brain MRI images, and few people have studied the 3D reconstruction of human brain MRI. An improved 3D-Unet medical image processing is proposed in this paper. Through the establishment of 3D segmentation algorithm, the 3D reconstruction and visualization of brain tissue are realized, and the linkage between segmentation algorithm and 3D visualization system is realized. Experiments show that the improved algorithm has high recognition rate and strong adaptability.
Camera Handoff for Multi-camera Surveillance

A handoff algorithm based on SURF is implemented in this paper. In multiple camera video surveillance systems, for consistently tracking an object, we require the concept of handoff. Using multiple cameras improves the flexibility and the area to be tracked which is why we need to consider handoff concept. Handoff or handover is transferring control of a mobile object from FOV of one camera to FOV of another camera. We have performed background subtraction by choosing one image as reference and one as a new frame. The resultant foreground image is used for extracting SURF features. SURF descriptors are saved for tracking the object by performing consecutive frame comparison. The presence of feature location within the handoff boundary indicates the need for handoff, either giving or accepting. The features found in the handoff boundaries of adjacent cameras are compared. If the features match, handoff will be initiated.
On the Reversible Jump Markov Chain Monte Carlo (RJMCMC) Algorithm for Extreme Value Mixture Distribution as a Location-Scale Transformation of the Weibull Distribution

Data with a multimodal pattern can be analyzed using a mixture model. In a mixture model, the most important step is the determination of the number of mixture components, because finding the correct number of mixture components will reduce the error of the resulting model. In a Bayesian analysis, one method that can be used to determine the number of mixture components is the reversible jump Markov chain Monte Carlo (RJMCMC). The RJMCMC is used for distributions that have location and scale parameters or location-scale distribution, such as the Gaussian distribution family. In this research, we added an important step before beginning to use the RJMCMC method, namely the modification of the analyzed distribution into location-scale distribution. We called this the non-Gaussian RJMCMC (NG-RJMCMC) algorithm. The following steps are the same as for the RJMCMC. In this study, we applied it to the Weibull distribution. This will help many researchers in the field of survival analysis since most of the survival time distribution is Weibull. We transformed the Weibull distribution into a location-scale distribution, which is the extreme value (EV) type 1 (Gumbel-type for minima) distribution. Thus, for the mixture analysis, we call this EV-I mixture distribution. Based on the simulation results, we can conclude that the accuracy level is at minimum 95%. We also applied the EV-I mixture distribution and compared it with the Gaussian mixture distribution for enzyme, acidity, and galaxy datasets. Based on the Kullback–Leibler divergence (KLD) and visual observation, the EV-I mixture distribution has higher coverage than the Gaussian mixture distribution. We also applied it to our dengue hemorrhagic fever (DHF) data from eastern Surabaya, East Java, Indonesia. The estimation results show that the number of mixture components in the data is four; we also obtained the estimation results of the other parameters and labels for each observation. Based on the Kullback–Leibler divergence (KLD) and visual observation, for our data, the EV-I mixture distribution offers better coverage than the Gaussian mixture distribution.
Leveraging Style and Content features for Text Conditioned Image Retrieval

Image Search is a fundamental task playing a significant role in the success of wide variety of frameworks and applications. However, with the increasing sizes of product catalogues and the number of attributes per product, it has become difficult for users to express their needs effectively. Therefore, we focus on the problem of Image Retrieval with Text Feedback, which involves retrieving modified images according to the natural language feedback provided by users. In this work, we hypothesise that since an image can be delineated by its content and style features, modifications to the image can also take place in the two sub spaces respectively. Hence, we decompose an input image into its corresponding style and content features, apply modification of the text feedback individually in both the style and content spaces and finally fuse them for retrieval. Our experiments show that our approach outperforms a recent state of the art method in this task, TIRG, that seeks to use a single vector in contrast to leveraging the modification via text over style and content spaces separately.