Reviewerly/exHarmony huggingface dataset:
Lights Out on graphs

Abstract We model the Lights Out game on general simple graphs in the framework of linear algebra over the field $$\mathbb{F}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>F</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> . Based upon a version of the Fredholm alternative, we introduce a separating invariant of the game, i.e., an initial state can be transformed into a final state if and only if the values of the invariant of both states agree. We also investigate certain states with particularly interesting properties. Apart from the classical version of the game, we propose several variants, in particular a version with more than only two states (light on, light off), where the analysis relies on systems of linear equations over the ring $$\mathbb{Z}_{n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>Z</mml:mi> <mml:mi>n</mml:mi> </mml:msub> </mml:mrow> </mml:math> . Although it is easy to find a concrete solution of the Lights Out problem, we show that it is NP-hard to find a minimal solution. We also propose electric circuit diagrams to actually realize the Lights Out game.
Human-in-the-loop extension to stream classification for labeling of low altitude drone imagery

In general, there is a severe demand for, and shortage of, large accurately labeled datasets to train supervised machine learning (ML) algorithms for domains like smart cars and unmanned aerial systems (UAS). This impacts a number of real-world problems from standing up ML on niche domains to ML performance in/across different environments. Herein, we consider the task of efficiently, meaning requiring the least amount of human intervention possible, converting large UAS data collections over a shared geospatial area into accurately labeled training data. Herein, we take a human-in-the-loop (HITL) approach that is based on coupling active learning and self-supervised learning to efficiently label low altitude UAS imagery for the goal of training ML algorithms for underlying tasks like detection, localization, and tracking. Specifically, we propose an extension to our stream classification algorithm StreamSoNG based on human intervention. We also extend StreamSoNG to rely on a second and initially more mature, but assumed incomplete, ML classifier. Herein, we use the Unreal Engine to simulate realistic ray-traced low altitude UAS data and facilitate algorithmic performance analysis in a controlled fashion. While our results are preliminary, they suggest that this approach is a good trade off between not overloading a human operator and circumventing fundamental stream classification algorithm limitations.
Making the Milrem Themis UGV ready for autonomous operations

The usage of Unmanned Ground Vehicles (UGVs) in defence application is increasing, and much research effort is put into the field. Also, many defence vehicle producers are developing UGV platforms. However, the autonomy functionality of these systems are still in need of improvement. At the Norwegian Defence Research Establishment a project for developing an autonomous UGV was started in 2019 and use a Milrem THeMIS 4.5 from Milrem Robotics as the base platform for achieving this. In this paper we will describe the modifications made to the vehicle to make it ready for autonomous operations. We have added three cameras and a Lidar as vision sensors, for navigation we have added a GNSS, IMU and velocity radar, and all sensors get a common time stamp from a time server. All the sensors have been mounted on a common aluminium profile, which is mounted in the front of the vehicle. The vision and navigation sensors have been mounted on the common aluminium profile to ensure that the direction the vision sensors observe is known with as little uncertainty as possible. In addition to the hardware modification, a control software framework has been developed on top of Milrem’s controller. The vehicle is interfaced using ROS2, and is controlled by sending velocity commands for each belt. We have developed a hardware abstraction module that interfaces the vehicle and adds some additional safety features, a trajectory tracking module and a ROS simulation framework. The control framework has been field tested and results will be shown in the paper.
Green Cloud Computing: E-Commerce Case Study

Cloud computing is getting probably the most objective, purpose, and dreams in most IT organizations on account of the advantages they are getting by relocating their advancements into it like expense and asset sparing. Green blurring processing is turning into a significant pattern with a solid relationship with distributed computing. It is about green and productivity that won't just prompt a superior business yet in addition a superior world. This paper will discuss a major phase of the software development life cycle, which is an arrangement, and how the selection of sending computerization is superior to the manual one particularly in a manner to make your cloud green.&#x0D;
Understanding Information Retrieval System in the Library of the National Green Tribunal, Eastern Zone Bench, Kolkata: A Case Study

Information retrieval is the backbone of an information-serving institution. There is predominant use of printed media in Indian judiciary, as public outreach of the electronic media is still negligible. This work tries to explore the usage of different media of information among users in the library at the National Green Tribunal, Eastern Zone Bench at Kolkata, identify the shortcoming, trace the user’s feedback on the Judgment Information System (JUDIS) and improve it. Based on users’ responses, a system improvement for information retrieval is suggested, framing an alternative indexing mechanism to improve the proper and accurate search, locally.
IMPLEMENTATION OF A COMPETENCE-BASED APPROACH IN THE PROFESSIONAL TRAINING OF STUDENTS AT TECHNICAL UNIVERSITIES THROUGH ELECTRONIC SIMULATORS

The use of simulators in the learning environment is constantly expanding with the development of computer technologies. Despite of the constant interest to this problem, Russian pedagogy still lacks serious research on the theoretical and practical aspects of using simulators in the vocational training of students at technical universities. The attempt to study the essence of the concepts “simulator”, “electronic simulator”, the methodical potential of simulators in the educational process of the university, identifying various approaches to the classification of this phenomenon is taken in this paper. The authors also demonstrate the possibilities of the didactic use of simulators for the implementation of competence-based learning.
Hyperbolic Neural Collaborative Recommender

This paper explores the use of hyperbolic geometry and deep learning techniques for recommendation. We present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic representation learning method that exploits mutual semantic relations among users/items for collaborative filtering (CF) tasks. HNCR contains two major phases: neighbor construction and recommendation framework. The first phase introduces a neighbor construction strategy to construct a semantic neighbor set for each user and item according to the user-item historical interaction. In the second phase, we develop a deep framework based on hyperbolic geometry to integrate constructed neighbor sets into recommendation. Via a series of extensive experiments, we show that HNCR outperforms its Euclidean counterpart and state-of-the-art baselines.
Tripartite correlation performance for use in quantum radar systems

In recent years, quantum radar has focused entirely on using bipartite squeezed states of light as a mechanism for target detection. This paper studies the performance of a quantum radar that uses a tripartite squeezed state, whereby two signal beams are sent out towards the target which both correlate with the idler. It is found that for very low signal strengths, the bipartite has better performance. As the signal strength increases however, the tripartite becomes dominant. This result suggests that quantum radar (declared useful only in the low SNR regime) may possess more possibilities of increased performance at higher SNRs when different states are used for correlation. The bottleneck, of course, is the ability to generate transmit powers necessary to utilize.
ALGORITMI E NUOVE FORME DI DISCRIMINAZIONE: UNO SGUARDO AL DIRITTO EUROPEO

Algorithmic discrimination is one of the risks of the more and more frequent use of artificial intelligence systems. The paper criticizes the neutrality of algorithms, demonstrating that, in some cases, the digital revolution we are witnessing amplifies forms of discrimination already present in society and in the market and create new ones. Faced with such a complicated scenario, a balanced approach is proposed between mistrust and too much emphasis on artificial intelligence applications. In particular, it is advised that we not to give up the transparency and control of algorithms, to prevent them from being allowed to do what men are not allowed to, becoming a shield for the responsibility of some people and a tool to reduce other people to mere “ cases” to be classified, normalized or excluded.
Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

This study focuses on the reentry target classification and fuses target features based on the generalized evidence theory. The features are extensively investigated, and the ballistic factor and length of the high-resolution range profile are selected. The evidence theory is advantageous for solving feature fusion, representing uncertainty, and is widely used in defense applications. However, determining the generalized basic probability assignment (GBPA) and dealing with uncertainty is a matter that requires further improvement. In this paper, we propose a new method to determine GBPA using uncertainty with time-series radar data. First, the samples of each known class are encoded as a generalized fuzzy number (GFN), and the power set comprising the frame of discernment (FOD) is calculated from the GFN and each intersection area. Subsequently, the test samples with uncertainty are encoded as triangular fuzzy numbers, reflecting the mean and standard deviation of a Kalman filter. Finally, the firing strength between the model and the input is calculated as the degree of support for the class hypothesis, which is used to determine the GBPA. The proposed algorithm is compared with the existing methods and exhibits high classification accuracy and a short classification time without leakage. In experiments with various input uncertainties, the results demonstrate that our method can effectively reflect the input uncertainty and determine the GBPA.
Multi-modal sensor fusion and selection for enhanced situational awareness

Collaborative multi-sensor perception enables a sensor network to provide multiple views or observations of an environment, in a way that collects multiple observations into a cohesive display. In order to do this, multiple observations must be intelligently fused. We briefly describe our existing approach for sensor fusion and selection, where a weighted combination of observations is used to recognize a target object. The optimal weights that are identified control the fusion of multiple sensors, while also selecting those which provide the most relevant or informative observations. In this paper, we propose a system which utilizes these optimal sensor fusion weights to control the display of observations to a human operator, providing enhanced situational awareness. Our proposed system displays observations based on the physical locations of the sensors, enabling a human operator to better understand where observations are located in the environment. Then, the optimal sensor fusion weights are used to scale the display of observations, highlighting those which are informative and making less relevant observations simple for a human operator to ignore.
Codewithzichao@DravidianLangTech-EACL2021: Exploring Multilingual Transformers for Offensive Language Identification on Code Mixing Text

This paper describes our solution submitted to shared task on Offensive Language Identification in Dravidian Languages. We participated in all three of offensive language identification. In order to address the task, we explored multilingual models based on XLM-RoBERTa and multilingual BERT trained on mixed data of three code-mixed languages. Besides, we solved the class-imbalance problem existed in training data by class combination, class weights and focal loss. Our model achieved weighted average F1 scores of 0.75 (ranked 4th), 0.94 (ranked 4th) and 0.72 (ranked 3rd) in Tamil-English task, Malayalam-English task and Kannada-English task, respectively.
Forged Multinational Currency Recognition System Using Convolutional Neural Network

Forged multinational currency recognition and identification system is developed to verify that the currency is genuine or fake currency. In current scenario of counterfeiting is affecting the banking sector and other fields as well. This paper introduces as advance convolutinal neural network technique for identification and detection of forged currency banknotes using image dataset. This technique is considered as relatively superior to earlier techniques. Proposed approach uses deep learning algorithm as nowadays deep learning is being measured as incredible achievement in image classification tasks. This approach can also be helpful for normal human being in recognizing a forged currency note through an image in real time of various countries. Currently, this approach is implemented on various denominations of Indian and Saudi Arabian Country. This systems success rate can be measured in terms of both precision and time. The key objective behind this system is to concentrate on those minute parameters that will be adequate to differentiate between forged and original banknotes.
Traffic sign detection and recognition based on CNN-ELM

Abstract Aiming at the problem of low traffic sign recognition rate and slow speed, a traffic sign recognition algorithm combining CNN and Extreme Learning Machine is proposed. First, the ResNet50 network is used to extract image features, and then the Region Proposal Network (RPN) is used to generate proposals from the extracted image feature maps. Finally, the extreme learning machine is used to classify the generated proposals, and the fully connected layer is used for regression prediction. The experiment shows that compared with the Faster R-CNN model, the CNN+ELM improves the recognition accuracy on the TT-100K dataset 7.7% and reduces the training time per epoch by 32 seconds.
The intellectual foundation of the elite of Brazilian researchers on knowledge organization domain

Abstract This study aims to analyze the intellectual foundation of the literature on knowledge organization published from 1972 to 2018 by authors enrolled in the Research Productivity Fellowship from Conselho Nacional de Desenvolvimento Científico e Tecnológico, in Brazil. The corpus of analysis is composed of 166 papers indexed in Base de Dados Referencial de Artigos de Periódicos em Ciência da Informação, which is a database that gathers the scientific literature published in Information Science journals in Brazil. It is an exploratory study that uses citation analysis as the methodological procedure, through author co-citation analysis and author bibliographic coupling. Fujita is identified as the most productive author, with 18 articles. Dahlberg is the most cited author, with 53 citations. The highest frequency of author co-citation is between Hjørland and Dahlberg; Tálamo and Kobashi. They are the seminal authors to the Brazilian scientists studied in this paper. The strongest relationships in the author bibliographic coupling network are between Lara and Bufrem, Lara and Guimarães, and Bufrem and Fujita. They cited 9 authors in common in the papers analyzed in this research. The conclusion is that there is an influence of European literature among the scientists addressed in this study. The results indicate the possibility of developing diachronic studies on the continuing influences of cited authors, especially from seminal authors, to analyze their permanence or transience over time.
Unsupervised Machine Translation On Dravidian Languages

Unsupervised neural machine translation (UNMT) is beneficial especially for low resource languages such as those from the Dravidian family. However, UNMT systems tend to fail in realistic scenarios involving actual low resource languages. Recent works propose to utilize auxiliary parallel data and have achieved state-of-the-art results. In this work, we focus on unsupervised translation between English and Kannada, a low resource Dravidian language. We additionally utilize a limited amount of auxiliary data between English and other related Dravidian languages. We show that unifying the writing systems is essential in unsupervised translation between the Dravidian languages. We explore several model architectures that use the auxiliary data in order to maximize knowledge sharing and enable UNMT for distant language pairs. Our experiments demonstrate that it is crucial to include auxiliary languages that are similar to our focal language, Kannada. Furthermore, we propose a metric to measure language similarity and show that it serves as a good indicator for selecting the auxiliary languages.
Filtering cold outliers in SSTs retrieved from early AVHRRs for the second AVHRR GAC reanalysis

Global long-term SST record is being created under the AVHRR GAC Reanalysis (RANs) project, by historical reprocessing 4 km data of the AVHRR/2 and /3 instruments flown onboard multiple NOAA satellites from 1981 – present. The AVHRR data are reprocessed with the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) system. During RANs, the ACSPO algorithms are being adjusted to mitigate various issues intrinsic to AVHRR sensors, especially AVHRR/s on the earlier NOAA missions in 1980s and 1990s. The focus of the latest interim release of RAN2, Beta 02, is the contamination of retrieved SSTs with massive cold biases, originating from two sources. First, in all AVHRR missions, periodic cold SST outliers occur at night due to solar impingement on the black body calibration target, when the satellite orbit approaches the terminator. Second, multiple cold outliers appear in the NOAA-7, -11 and -12 SSTs following three major volcanic eruptions of Mt. El Chichon (1982), Mt. Pinatubo (1991) and Mt. Hudson (1991). The current mitigation algorithm exploits the fact that in both cases, the spatial densities of the cold outliers exhibit well-expressed latitudinal dependencies. The algorithm identifies 5° latitudinal bands with abnormally high density of outliers and makes the cloud mask more conservative within those bands. This improves filtering cold SST outliers in the contaminated areas without increasing the false cloud detection rate in the unaffected parts of the ocean. We also discuss the ongoing development to mitigate cold SST biases by correcting AVHRR L1b calibration (rather than eliminating the affected SST data).
Real time circle detection by simplified Hough transform on smartphones

Real time circle detection requires a considerable amount of computing power, especially with growing image size. This paper presents a modified version of the Hough transform with a dedicated and streamlined pre- processing stage to detect circles in video images in real-time using mid-range performance smartphones. Hough transform for detection of co-circular line pixels requires a 3-dimensional data space instead of 2 dimensions for detection of co-linear pixels. This dimensional complexity and the fact that Hough transform in general requires computational expensive pre-processing, make optimizations for hand-held or embedded systems inevitable. Multiple modifications for tuning the algorithms by trading mathematical accuracy against processing speed are shown in this paper, which improve the overall computational performance, significantly. Some of these optimizations allow e.g. to replace the edge detection process completely by a simple but smart thresholding and pixel-wise neighbourhood inspection, using pre-calculated lookup tables instead of complex calculations and restricting the Hough space in size and precision. These modifications where implemented and tested on both desktop and mobile devices for comparison but without any support by the GPU. Benchmarks showed that more than 60 FPS on desktops and more than 20 FPS on mobile devices are achievable for processing full HD resolution images, which allows implementations meeting the real time constraints and deadlines specified by a concrete application of an ambulant water quality analysis scenario.
Collaborative E-Learning Process Discovery in Multi-tenant Cloud

With the appearance of the COVID-19 pandemic, the practice of e-learning in the cloud makes it possible to: avoid the problem of overloading the institutions infrastructure resources, manage a large number of learners and improve collaboration and synchronous learning. In this paper, we propose a new e-leaning process management approach in cloud named CLP-in-Cloud (for Collaborative Learning Process in Cloud). CLP-in-Cloud is composed of two steps: i) design general, configurable and multi-tenant e-Learning Process as a Service (LPaaS) that meets different needs of institutions. ii) to fulfill the user needs, developpe a functional and non-functional awareness LPaaS discovery module. For functional needs, we adopt the algorithm A* and for non-functional needs we adopt a linear programming algorithm. Our developed system allows learners to discover and search their preferred configurable learning process in a multi-tenancy Cloud architecture. In order to help to discover interesting process, we come up with a recommendation module. Experimentations proved that our system is effective in reducing the execution time and in finding appropriate results for the user request.
A Comparative Study of Different Machine Learning Algorithms on Bitcoin Value Prediction

Machine learning is growing rapidly and has made many theoretical breakthroughs which find its application in many fields. Bitcoin is a very secure, decentralized, peer to peer currency with no third-party involvement. The price prediction of Bitcoin in the following years is a difficult task. The objective is to take a dig in the prediction of the future prices, dealing with real world data. A comparative study of the results produced by different machine learning models, along with graphs for epoch vs price, error and accuracy for each model using both linear and non-linear functions is done. We are using both neural network algorithms, such as artificial neural network (ANN), recurrent neural network (RNN) and convolutional neural network (CNN), as well as some famous supervised learning algorithms such as Random Forest (RF) and k-nearest neighbors (k-NN), to form the analysis. The time price prediction graphs and the epoch loss accuracy graphs are used for the analysis of each algorithm working on the same data and produces different results. Finally, the best suited algorithm are used for the prediction of future Bitcoin price.