Reviewerly/exHarmony huggingface dataset:
Text Mining Methodology for Building Water Leak Maps from Tweets

This article presents a text mining methodology that is used in the framework of a project called Fugometría. The objective of the proposed methodology is the construction of maps with markers that indicate the location of water leaks. To achieve this purpose, the initial step is the collection of tweets issued by citizens to report the existence of leaks in drinking water distribution networks. Once these tweets are collected, they are parsed for an address in the body of the message. If an address is detected, it is converted into a GPS coordinate, which is in turn used to build a map with markers indicating the location of water leaks. In order to show the applicability of the methodology, some preliminary results on the construction of a leak map of Mexico City are shown.
ML Strategies For Automating Code Reviews

The benefits of using machine learning in electronic software code review are considerable
Sensor-Based Navigation Using Hierarchical Reinforcement Learning

Robotic systems are nowadays capable of solving complex navigation tasks. However, their capabilities are limited to the knowledge of the designer and consequently lack generalizability to initially unconsidered situations. This makes deep reinforcement learning (DRL) especially interesting, as these algorithms promise a self-learning system only relying on feedback from the environment. In this paper, we consider the problem of lidar-based robot navigation in continuous action space using DRL without providing any goal-oriented or global information. By relying solely on local sensor data to solve navigation tasks, we design an agent that assigns its own waypoints based on intrinsic motivation. Our agent is able to learn goal-directed navigation behavior even when facing only sparse feedback, i.e., delayed rewards when reaching the target. To address this challenge and the complexity of the continuous action space, we deploy a hierarchical agent structure in which the exploration is distributed across multiple layers. Within the hierarchical structure, our agent self-assigns internal goals and learns to extract reasonable waypoints to reach the desired target position only based on local sensor data. In our experiments, we demonstrate the navigation capabilities of our agent in two environments and show that the hierarchical structure seriously improves the performance in terms of success rate and success weighted by path length in comparison to a flat structure. Furthermore, we provide a real-robot experiment to illustrate that the trained agent can be easily transferred to a real-world scenario.
People Tracking in Panoramic Video for Guiding Robots

A guiding robot aims to effectively bring people to and from specific places within environments that are possibly unknown to them. During this operation the robot should be able to detect and track the accompanied person, trying never to lose sight of her/him. A solution to minimize this event is to use an omnidirectional camera: its $$360^{\circ }$$ Field of View (FoV) guarantees that any framed object cannot leave the FoV if not occluded or very far from the sensor. However, the acquired panoramic videos introduce new challenges in perception tasks such as people detection and tracking, including the large size of the images to be processed, the distortion effects introduced by the cylindrical projection and the periodic nature of panoramic images. In this paper, we propose a set of targeted methods that allow to effectively adapt to panoramic videos a standard people detection and tracking pipeline originally designed for perspective cameras. Our methods have been implemented and tested inside a deep learning-based people detection and tracking framework with a commercial $$360^{\circ }$$ camera. Experiments performed on datasets specifically acquired for guiding robot applications and on a real service robot show the effectiveness of the proposed approach over other state-of-the-art systems. We release with this paper the acquired and annotated datasets and the open-source implementation of our method.
How to cultivate practice-oriented talents in higher vocational music teachin

With the continuous development of economy and society, people's requirements for education are getting higher and higher, therefore, higher vocational music teaching should also develop in a higher direction. In the new era, music education is to meet the practical needs for social development and cultivate high-quality music practice-oriented talents, the so-called practice-oriented talents are those who can master the basic skills or have certain comprehensive quality in practical work. In order to cultivate practical talents that meet the needs of society, higher vocational music teaching must take the cultivation of practical talents as the main direction, and cultivate practical talents from the curriculum, teaching methods and means.
System Hypothesis Implications of Algebraic Geometry

Certain pole-placement concepts, such as an enhanced form of pole location with output response, are proven using fundamental algebraic geometry equations. Illustrations that highlight the algebra-geometric equations drawbacks and its possible application to systems analysis are shown. This study and ones that may come after it may help to make the potent theorems of current algebraic geometry comprehensible and useful for solving technical hurdles.
Edge-Cloud Collaborative Defense against Backdoor Attacks in Federated Learning

Federated learning has a distributed collaborative training mode, widely used in IoT scenarios of edge computing intelligent services. However, federated learning is vulnerable to malicious attacks, mainly backdoor attacks. Once an edge node implements a backdoor attack, the embedded backdoor mode will rapidly expand to all relevant edge nodes, which poses a considerable challenge to security-sensitive edge computing intelligent services. In the traditional edge collaborative backdoor defense method, only the cloud server is trusted by default. However, edge computing intelligent services have limited bandwidth and unstable network connections, which make it impossible for edge devices to retrain their models or update the global model. Therefore, it is crucial to detect whether the data of edge nodes are polluted in time. This paper proposes a layered defense framework for edge-computing intelligent services. At the edge, we combine the gradient rising strategy and attention self-distillation mechanism to maximize the correlation between edge device data and edge object categories and train a clean model as much as possible. On the server side, we first implement a two-layer backdoor detection mechanism to eliminate backdoor updates and use the attention self-distillation mechanism to restore the model performance. Our results show that the two-stage defense mode is more suitable for the security protection of edge computing intelligent services. It can not only weaken the effectiveness of the backdoor at the edge end but also conduct this defense at the server end, making the model more secure. The precision of our model on the main task is almost the same as that of the clean model.
LECA: A learned approach for efficient cover-agnostic watermarking

In this work, we present an efficient multi-bit deep image watermarking method that is cover-agnostic yet also robust to geometric distortions such as translation and scaling as well as other distortions such as JPEG compression and noise. Our design consists of a light-weight watermark encoder jointly trained with a deep neural network based decoder. Such a design allows us to retain the efficiency of the encoder while fully utilizing the power of a deep neural network. Moreover, the watermark encoder is independent of the image content, allowing users to pre-generate the watermarks for further efficiency. To offer robustness towards geometric transformations, we introduced a learned model for predicting the scale and offset of the watermarked images. Moreover, our watermark encoder is independent of the image content, making the generated watermarks universally applicable to different cover images. Experiments show that our method outperforms comparably efficient watermarking methods by a large margin.
What are we looking at? An investigation on the use of deep learning models for image quality assessment

In recent years several different Image Quality Metrics (IQMs) have been introduced which are focused on comparing the feature maps extracted from different pre-trained deep learning models[1-3]. While such objective IQMs have shown a high correlation with the subjective scores little attention has been paid on how they could be used to better understand the Human Visual System (HVS) and how observers evaluate the quality of images. In this study, by using different pre-trained Convolutional Neural Networks (CNN) models we identify the most relevant features in Image Quality Assessment (IQA). By visualizing these feature maps we try to have a better understanding about which features play a dominant role when evaluating the quality of images. Experimental results on four benchmark datasets show that the most important feature maps represent repeated textures such as stripes or checkers, and feature maps linked to colors blue, or orange also play a crucial role. Additionally, when it comes to calculating the quality of an image based on a comparison of different feature maps, a higher accuracy can be reached when only the most relevant feature maps are used in calculating the image quality instead of using all the extracted feature maps from a CNN model. [1] Amirshahi, Seyed Ali, Marius Pedersen, and Stella X. Yu. "Image quality assessment by comparing CNN features between images." Journal of Imaging Science and Technology 60.6 (2016): 60410-1. [2] Amirshahi, Seyed Ali, Marius Pedersen, and Azeddine Beghdadi. "Reviving traditional image quality metrics using CNNs." Color and imaging conference. Vol. 2018. No. 1. Society for Imaging Science and Technology, 2018. [3] Gao, Fei, et al. "Deepsim: Deep similarity for image quality assessment." Neurocomputing 257 (2017): 104-114.
Image demosaicing: Subjective analysis and evaluation of image quality metrics

Most cameras use a single-sensor arrangement with Color Filter Array (CFA). Color interpolation techniques performed during image demosaicing are normally the reason behind visual artifacts generated in a captured image. While the severity of the artifacts depends on the demosaicing methods used, the artifacts themselves are mainly zipper artifacts (block artifacts across the edges) and false-color distortions. In this study and to evaluate the performance of demosaicing methods, a subjective pair-comparison method with 15 observers was performed on six different methods (namely Nearest Neighbours, Bilinear interpolation, Laplacian, Adaptive Laplacian, Smooth hue transition, and Gradient-Based image interpolation) and nine different scenes. The subjective scores and scene images are then collected as a dataset and used to evaluate a set of no-reference image quality metrics. Assessment of the performance of these image quality metrics in terms of correlation with the subjective scores show that many of the evaluated no-reference metrics cannot predict perceived image quality.
Multi-view clustering study based on subspace

With the widespread existence of multi-view data, many multi-view clustering methods have emerged. The purpose of multi-view clustering is to classify data into multiple clusters based on different views. Existing multi-view clustering methods usually do not sufficiently mine the complementary information of data, which makes the effective information of multi-view data cannot be fully utilized. By considering the diversity of different views, we propose a new multi-view subspace clustering method. Specifically, we first extend single-view self-expression learning to the multi-view domain. Then, based on manifold learning, the public information of multi-view data is obtained. In addition, diversity among the data was measured using the Hilbert-Schmidt Independence Criteria (HSIC). Experimental results on four datasets show that our model has good clustering performance on different evaluation indicators.
The ICA evolutive position on international crisis, the example of the Russian invasion of Ukraine

Established in 1948, in the aftermath of a World War immediately followed by the Cold War, the ICA promoted a neutrality that reflected the fears and values of a world exposed to clashes between geopolitical blocs and the terror of nuclear weapons. As the world has evolved, so has the ICA, reflecting the priorities, diversity and ambitions of its members. Increasingly, our members have addressed important social issues that impact on the preservation and use of records and archives. The resolution of support for Ukraine adopted by the ICA Executive Board on 10 March 2022 reflects this development as it is the role of the ICA to act responsibly and decisively in defense of records, archives, our profession and its members.
GamaBox-One: A Proposed Architecture for Cloud-based Big Data Management Platform for Multipurpose Computation using Hybrid Architecture

The architecture of the data center is the main key for producing a highly functional demand big data management platform for multipurpose uses. Nowadays various technologies have come to provide construction of that purpose, providing several use cases for big data analytics and processing. In this paper, we want to explore possibilities of architecture that had to be built in answer to the multipurpose data center, such as analytical research, scientific simulation, machine learning, deep data learning, and data orchestration. We discover how Hadoop and its element supporter can be used alongside cloud orchestrators such as Terraform or Occopus and container orchestrators such as Kubernetes or Docker Swarm. We also provide possible supporting components that can handle the different jobs in High-Performance Computing and how the system can be secured. Our proposed approach in this research has developed the architecture for cloud-based big data management for multipurpose computation.
Analysis of Educational Impact in the Doda Region of Jammu and Kashmir

Jammu and Kashmir's ECE services have taken a "Great Leap Forward" since the central government released the Outline of National Plan for Medium- and Long-term Education Reform and Development (hereafter referred to as "the Plan") and the State Council's Several Opinions on the Current Development of Early Childhood Education (hereafter referred to as "the Opinions") in 2010. Based on the findings of this study, it is clear that an integrated strategy is necessary for fostering youth development in rural Jammu and Kashmir. By taking this tack, we can increase the likelihood that students and their communities benefit economically, socially, and culturally from their schooling. Adult education and youth development are synonymous with literacy since both focus on helping people who have not finished their formal education for various reasons become productive members of society by completing their education and achieving their developmental goals. That's why literacy's importance extends to both the training of adults and the development of children.
Curriculum learning based overcomplete U-Net for liver tumor segmentation from computed tomography images

In this paper, we have proposed an overcomplete U-Net to perform liver tumor segmentation jointly using a curriculum learning strategy. Liver tumor segmentation is the most prominent and primary step in treating liver cancer and can also help doctors with proper diagnosis and therapy planning. However, it is challenging because of variations in shape, position, and depth of tumors and adjacent boundaries with internal organs around the liver. We have presented a promising solution by designing a U-Net-based segmentation network with two branches: an overcomplete branch to fine grade the small structures and an undercomplete branch to fine grade the high-level structures. This combination allows the network to learn all types of tumor artifacts more accurately. We also changed the conventional learning paradigm to curriculum learning where the input images are fed to the network from easy to hard ones to achieve faster convergence. Finally, our network segments the tumors directly from the whole medical images without the need for segmented liver region of interests (ROIs). The proposed network achieved a DICE score of 75% in tumor segmentation which is a decent value when compared with some existing deep learning methods for liver tumor segmentation.
Low light image enhancement using curvelet transform and iterative back projection

With the advancement of technology in image capturing, people are accustomed to high-resolution images. One of the primary necessities of an image capturing system is to provide the same. However, in many cases, the image resolution may not be reaching the expectations of the user which leads to a decrease in user experience. This is a common phenomenon that occurs when the images are captured in low light or if the image encounters a distortion either because of lack of exposure or the image capturing devices may be equipped with a small size sensor. In this work, a resolution enhancement technique using the concepts of curvelet transform and iterative back projection is presented. Sparse representation of images can be enhanced using a combination of curvelet transforms with iterative back projection. Application of curvelet transform along with iterative back projection algorithm on low light images results in enhancing the resolution of the images. The resultant images from here then passed through the inverse transform block and gives an image with contrast enhancement which leads to the user experience improvement. The antiquated image enhancement with improvement in the resolution is validated with the measurement of peak signal-to-noise ratio and structural similarity index. The usage of curvelet transform with iterative back projection leads to the restoration of the image resolution by minimizing the distortions, thus leading to an enhanced image whose edge details are retained.
128-bit Vectorization on Cha-Cha20 Algorithm for Device-to-Device Communication

In 5G networks, device to device(D2D) communication is the advanced technology, and it has the major advantages when compared with traditional systems. The coverage area of the system increases by device-to-device transmission which renders with low latency. No doubt, for the upcoming technologies D2D communications is an added advantage but in various views this kind of transmission is still under risk. D2D communication is transferring the information from one device to another device without the involvement of base station. So, the communication is possible with less delay time. Attackers are possible into the D2D communications. To provide the security in communication, encryption algorithms are used. The main theme of the Encryption algorithms is it should execute with less delay time to provide faster communication and in particular CHA-CHA20 offers the secure communication for encrypting the data packets to securing the data. Vectorization on Cha-Cha20 Algorithm provides the security with less delay time compared to AES encryption Algorithm and Cha-Cha20 algorithm. Compared to other encryption algorithms in cryptography, Cha-Cha20 is suited for resource constrained devices.
Text-Independent Automatic Dialect Recognition of Marathi Language using Spectro-Temporal Characteristics of Voice

Text-independent dialect recognition system is proposed in this paper for Marathi language. India is rich in language varieties. Each language in turn has its unique dialect variations. Maharashtra has Marathi as official language and for Goa it is a co-official language . In literature there are very few studies available for Indian language recognition and then their respective dialect recognition. So research work available for regional languages such as Marathi is extremely limited. As a part of research work, an attempt is made to generate a case study of a low resourced Marathi language dialect recognition system. The study was carried out using Marathi speech data corpus provided by Linguistic Data Consortium for Indian Language (LDC- IL). This corpus includes four major dialects of Marathi speakers. The efficiency and performance evaluation of the explored spectral (rhythmic) and temporal features are carried out to perform classification tasks. We investigated the performance of six different classifiers; K-nearest neighbor (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) classifier , Stochastic Gradient Descent (SGD) classifier and Ridge Classifier (RC). Experimental results have demonstrated that the RC classifier worked well with 84.24% of accuracy for fifteen spectral and temporal features. With twelve MFCCs it has been observed that SGD has outperformed among all classifiers with accuracy of 80.63%. For further study, a prominent feature subset as a part of dimensionality reduction has been identified using chi square, mutual information and ANOVA-f test. In this chi-square based feature extraction method has proven to be the best over over mutual information and ANOVA f-test.
Statistical Issues on Evaluating Agreement Between the Grade Groups Determined by Nonlinear Microscopy vs Formalin-Fixed Paraffin-Embedded Histology

We read with interest the recent article entitled “Real-time diagnosis and Gleason grading of prostate core needle biopsies using nonlinear microscopy,” which was published online in Modern Pathology on November 2, 2021. 1 Cahill L.C. Rosen S. Yoshitake T. et al. Real-time diagnosis and Gleason grading of prostate core needle biopsies using nonlinear microscopy. Mod Pathol. 2022; 35: 539-548 Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar In their article, the authors described a method for evaluating prostate core needle biopsy specimens in minutes using nonlinear microscopy (NLM) and evaluated this method by reading specimens from patients who underwent magnetic resonance imaging–guided core needle biopsy procedures in real time. After assessing the accuracy and procedure time of NLM for diagnosing prostate carcinoma, Gleason scoring, and estimating the amount of biopsy core involved by carcinoma, the authors proposed that NLM is a promising method for future rapid evaluation of prostate needle core biopsy specimens.
Homomorphic Encryption of Neural Networks

The modern world is moving towards an intelligent future where AI and ML will be playing an important role in improving technologies across all domains. The most basic requirement for training an ML model is data. Without the availability of data, there is no use for AI or ML. Data is freely and openly available but still, the privacy of the data is a deeper concern, which can create hurdles in developing new technologies. This paper suggests various ways which can be used to train ML models on such private data while maintaining the privacy of both the data as well as the ML model, using homomorphic encryption. This paper implemented a neural network on homomorphic encryption and proved the increase in accuracy of finding attacks over data on the fly and data at still. Thus, this paper mainly focuses on what happens when we apply Neural networks along with Homomorphic Encryption over data on still and data on the fly.