Reviewerly/exHarmony huggingface dataset:
BERT BiLSTM-Attention Similarity Model

Semantic similarity models are a core part of many of the applications of natural language processing (NLP) that we may be encountering daily, which makes them an important research topic. In particular, Question Answering Systems are one of the important applications that utilize semantic similarity models. This paper aims to propose a new architecture that improves the accuracy of calculating the similarity between questions. We are proposing the BERT BiLSTM-Attention Similarity Model. The model uses BERT as an embedding layer to convert the questions to their respective embeddings, and uses BiLSTM-Attention for feature extraction, giving more weight to important parts in the embeddings. The function of one over the exponential function of the Manhattan distance is used to calculate the semantic similarity score. The model achieves an accuracy of 84.45% in determining whether two questions from the Quora duplicate dataset are similar or not.
Ethically Compliant Planning within Moral Communities

Ethically compliant autonomous systems (ECAS) are the state-of-the-art for solving sequential decision-making problems under uncertainty while respecting constraints that encode ethical considerations. This paper defines a novel concept in the context of ECAS that is from moral philosophy, the moral community, which leads to a nuanced taxonomy of explicit ethical agents. We then propose new ethical frameworks that extend the applicability of ECAS to domains where a moral community is required. Next, we provide a formal analysis of the proposed ethical frameworks and conduct experiments that illustrate their differences. Finally, we discuss the implications of explicit moral communities that could shape research on standards and guidelines for ethical agents in order to better understand and predict common errors in their design and communicate their capabilities.
QUANTUM COMBINATORIAL DESIGN

Abstract Combinatorial Design such as configuration design, design optioneering, component selection, and generative design, is common across engineering. Generating solutions for a combinatorial design task often involves the application of classical computing solvers that can either map or navigate design spaces. However, it has been observed that classical computing resource power-law scales with many design space models. This observation suggests classical computing may not be capable of modelling our future design space needs. To meet future design space modelling needs, this paper examines quantum computing and the characteristics that enables its resources to scale polynomially with design space size. The paper then continues to present a combinatorial design problem that is subsequently represented, constrained and solved by quantum computing. The results of which are the derivation of an initial set of circuits that represent design space constraints. The study shows the game-changing possibilities of quantum computing as an engineering design tool and is the start of an exciting new journey for design research.
Multi-Granularity Sequence Alignment Mapping for Encoder-Decoder Based End-to-End ASR

Encoder-decoder based automatic speech recognition (ASR) methods are increasingly popular due to their simplified processing stages and low reliance on prior knowledge. Conventional encoder-decoder based approaches usually learn a sequence-to-sequence mapping function from the source speech to target units (e.g., subwords, characters) in an end-to-end manner. However, it is still unclear how to choose the optimal target unit, or granularity of multiple units. In general, as increasing the information available for learning sequence-to-sequence mapping functions can improve modeling effectiveness, we therefore propose a multi-granularity sequence alignment (MGSA) approach. This aims to enhance cross-sequence interactions between different granularity units for both modeling and inference stages in the encoder-decoder based ASR. Specifically, a decoder module is designed to generate multi-granularity sequence predictions. We then exploit the latent alignment mapping among units having different levels of granularity, by utilizing the decoded multi-level sequences as input for model prediction. The cross-sequence interaction can also be employed to re-calibrate output probabilities in the proposed post-inference algorithm. Experimental results on both WSJ-80 hrs and Switchboard-300 hrs datasets show the superiority of the proposed method compared to traditional multi-task methods as well as to single granularity baseline systems.
Irrigation water management to increase planting index in Boyolali Regency, Indonesia

Abstract Climate change will have a negative impact on water resources, agriculture, forestry, health and the vulnerability of public infrastructure and the extinction of various species. Water sources as the main means of agriculture, especially rice fields are the main thing in irrigation. Water sources for irrigation of paddy fields in Boyolali include weirs, trenches, springs that are collected in the form of ponds and a small part is a reservoir. Within 2 years after operation, the use of ponds of water for irrigation was able to accelerate yield increases. With various models of water sources, the cropping patterns include paddy-paddy-paddy, paddy-paddy- palawija (crop planted as 2 nd crop in dry season), paddy- palawija - palawija and paddy- palawija -fallow. Water management arrangements in each area that use irrigation channels have an Association of Farmers Using Water who is tasked with making a schedule for the distribution of irrigation between blocks, especially if there is water scarcity such as during the dry season. Types of activities that are usually coordinated by farmer groups are pest control, channel maintenance, and irrigation arrangements. For channel maintenance, usually coordinating with the officers of association of water user farmer and development coordinators in their respective villages.
Hateful meme detection with multimodal deep neural networks

The modern advances of social media platforms and content sharing websites led to the popularization of Internet memes, and today's Internet landscape contains websites that are predominantly dedicated to meme sharing. While at their inception memes were mostly humorous, this concept evolved and nowadays memes cover a wide variety of subjects, including political and social commentaries. Considering the widespread use of memes and their power of conveying distilled messages, they became an important method for spreading hate speech against individuals or targeted groups. Given the multimodal nature of Internet memes, our proposed approach is also a multimodal one, consisting of two parallel processing branches, one textual and one visual, that are joined in a final classification step, providing prediction results for the samples. We test our approach on the publicly available Memotion 7k dataset and compare our results with the baseline approach developed for the dataset.
A Fully Automatic Feature-Based Real-Time Traffic Surveillance System Using Data Association in the Probabilistic Framework

Multi-object tracking involves maintaining several trajectories of different objects moving in the scene throughout the video. With this objective, in this article, a fully automatic and real-time tracking algorithm to track multiple vehicles in a video is proposed. The proposed method specifically tries to address the challenges of occlusion and fast-motion in traffic surveillance. The algorithm begins with automatic detection of the moving targets by an adaptive GMM-based background subtraction method. The trajectories of these detected targets are then built using a three-level multi-motion modeled particle filter framework which allows to deal with the challenges of occlusion and fast-motions of the target. The likelihood model for targets is based on their color distribution and edge oriented histogram features. It is contended that the color distribution feature, which can represent the target appearance and the edge oriented histogram, which can describe the target structure are sufficient to represent it in an unique feature space. Based on the similarity of target likelihood, the locations of the targets are filtered. These filtered locations are then associated with the most likely detections using the proposed low-cost and fast data association algorithm based on Euclidean distance and prevailing motion vector. The performance evaluation of the proposed scheme is carried out based on the six measures: Multi Object Tracking Accuracy, Multi Object Tracking Precision, Mostly Tracked trajectories, Mostly Lost trajectories, Identity Switches and Frames Per Second. The results evaluated on stationary camera shot sequences from benchmark datasets as well as real-time shot videos indicate that the proposed algorithm ensures robust tracking and can be used effectively for real-time surveillance of highways.
2DLaserNet: A deep learning architecture on 2D laser scans for semantic classification of mobile robot locations

In this work, we deal with classification of mobile robot locations into semantic categories such as room, corridor, and doorway using 2D laser data. Previous studies were generally able to distinguish room and corridor classes; however, the classification of doorway locations has not been satisfactory. To increase the classification accuracy of doorway class, we proposed a new point-based deep learning architecture, namely 2DLaserNet. In contrast to the well-known point-based deep learning techniques, 2DLaserNet exploits the ordered relation between successive points in the point cloud generated from 2D laser readings. In this way, 2DLaserNet is able to learn the geometric characteristics of laser scans corresponding to room, corridor, and doorway classes. We used the publicly available Freiburg 79 dataset to validate the effectiveness of the proposed approach, especially for the doorway class. Besides, we incorporated synthetic data to account for the intra-class variety for doorway locations. We also conducted experiments on the Freiburg 52 test dataset to examine the generalization ability of the proposed architecture trained with the Freiburg 79 dataset. We observed that 2DLaserNet outperforms state-of-the-art methods and well-known point-based deep learning techniques for doorway class.
Network routing protocols for multi-photon quantum cryptography

In this paper, we propose the use of multi-photon quantum cryptography to provide higher data exchange rates and security in quantum networks. We investigate different routing strategies and their effects on key rates, path establishment probabilities, and security. Our simulation varies parameters such as the network size, topology, length of fiber channels, and probability of channel decoherence. Lastly, we also examine the effect of including a trusted nodes in the network and examine the key-rates under different routing strategies.
Ensemble ellipse fitting by spatial median consensus

Ellipses are among the most frequently used geometric models in visual pattern recognition and digital image analysis. This work aims to combine the outputs of an ensemble of ellipse fitting methods, so that the deleterious effect of suboptimal fits is alleviated. Therefore, the accuracy of the combined ellipse fit is higher than the accuracy of the individual methods. Three characterizations of the ellipse have been considered by different researchers: algebraic, geometric, and natural. In this paper, the natural characterization has been employed in our method due to its superior performance. Furthermore, five ellipse fitting methods have been chosen to be combined by the proposed consensus method. The experiments include comparisons of our proposal with the original methods and additional ones. Several tests with synthetic and bitmap image datasets demonstrate its great potential with noisy data and the presence of occlusion. The proposed consensus algorithm is the only one that ranks among the first positions for all the tests that were carried out. This demonstrates the suitability of our proposal for practical applications with high occlusion or noise.
Reservoir Computing Using Autonomous Boolean Networks Realized on Field-Programmable Gate Arrays

In this chapter, we consider realizing a reservoir computer on an electronic chip that allows for many tens of network nodes whose connection topology can be quickly reconfigured. The reservoir computer displays analog-like behavior and has the potential to perform computations beyond that of a classic Turning machine. In detail, we present our preliminary results of using a physical reservoir computer for performing the task of identifying written digits. The reservoir is realized on a commercially available electronic device known as a field-programmable gate array on which we create an autonomous Boolean network for information processing. Even though the network nodes are Boolean logic elements, they display analog behavior because there is no master clock that controls the nodes. In addition, the electronic signals related to the written-digit images are injected into the reservoir at high speed, leading to the possibility of full-image classification on the nanosecond time scale. We explore the dynamics of the autonomous Boolean networks in response to injected signals and, based on these results, investigate the performance of the reservoir computer on the written-digit task. For a wide range of reservoir structures, we obtain a typical performance of $$\sim $$ 90% for correctly identifying a written digit, which exceeds that obtained by a linear classifier. This work paves the way for achieving low-power, high-speed reservoir computing on readily available field-programmable gate arrays, which are well matched to existing computing infrastructure.
Digital Transformation and the Post-COVID-19 Institution

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsSusan Grajek Susan Grajek is Vice President for Partnerships, Communities, and Research at EDUCAUSE, a nonprofit association dedicated to advancing higher education through the use of information technology.
Towards Testing ACID Compliance in the LDBC Social Network Benchmark

Verifying ACID compliance is an essential part of database benchmarking, because the integrity of performance results can be undermined as the performance benefits of operating with weaker safety guarantees (at the potential cost of correctness) are well known. Traditionally, benchmarks have specified a number of tests to validate ACID compliance. However, these tests have been formulated in the context of relational database systems and SQL, whereas our scope of benchmarking are systems for graph data, many of which are non-relational. This paper presents a set of data model-agnostic ACID compliance tests for the LDBC (Linked Data Benchmark Council) Social Network Benchmark suite’s Interactive (SNB-I) workload, a transaction processing benchmark for graph databases. We test all ACID properties with a particular emphasis on isolation, covering 10 transaction anomalies in total. We present results from implementing the test suite on 5 database systems.
A Generalized Net Model of the Human Body Excretory System

The Generalized Nets (GN) have been proved as a successful tool for modelling of parallel processes. Up to now they have been used to describe different human body systems. In the present paper, GN is going to be applied for first time for the detailed description of human body excretory system.
Data science and AI in FinTech: An overview

Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas. Smart FinTech is the new-generation FinTech, largely inspired and empowered by data science and new-generation AI (DSAI) techniques. Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and systems. The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and blockchain, and the DSAI techniques including complex system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep learning, federated learning, privacy-preserving processing, augmentation, optimization, and system intelligence enhancement. Here, we present a highly dense research overview of smart financial businesses and their challenges, the smart FinTech ecosystem, the DSAI techniques to enable smart FinTech, and some research directions of smart FinTech futures to the DSAI communities.
SAED: Edge-Based Intelligence for Privacy-Preserving Enterprise Search on the Cloud

Cloud-based enterprise search services (e.g., AWS Kendra) have been entrancing big data owners by offering convenient and real-time search solutions to them. However, the problem is that individuals and organizations possessing confidential big data are hesitant to embrace such services due to valid data privacy concerns. In addition, to offer an intelligent search, these services access the user's search history that further jeopardizes his/her privacy. To overcome the privacy problem, the main idea of this research is to separate the intelligence aspect of the search from its pattern matching aspect. According to this idea, the search intelligence is provided by an on-premises edge tier and the shared cloud tier only serves as an exhaustive pattern matching search utility. We propose Smartness at Edge (SAED mechanism that offers intelligence in the form of semantic and personalized search at the edge tier while maintaining privacy of the search on the cloud tier. At the edge tier, SAED uses a knowledge-based lexical database to expand the query and cover its semantics. SAED personalizes the search via an RNN model that can learn the user's interest. A word embedding model is used to retrieve documents based on their semantic relevance to the search query. SAED is generic and can be plugged into existing enterprise search systems and enable them to offer intelligent and privacy-preserving search without enforcing any change on them. Evaluation results on two enterprise search systems under real settings and verified by human users demonstrate that SAED can improve the relevancy of the retrieved results by on average ≈24% for plain-text and ≈75% for encrypted generic datasets.
Improvement of salp swarm algorithm (SSA2) for intelligent fault detection in smart wireless sensor networks

This paper highlighted on a new improvement of the salp swarm algorithm (SSA) that is taken from the behavior of salp fishes. This shows a promising result, however it suffered from some drawbacks that limited its ability. The proposed improvement of SSA is called SSA 2. In this paper the design system where the ZigBee protocol received temperature, humidity and gas from the sensors. The base station collects received data and sends it to platform in order to process these data via SSA2. This system enables the ability to monitor and track any fault that may be occurring. A new term has been developed to make the algorithm more targeted in finding the optimal answer. Moreover, the calculation results and simulation results in Matlab shows great enhancement in the accuracy and precision of the results at very fast speed for convergence compared with the original salp swarm algorithm (SSA).
Enhancing the ORCA framework with a new Fuzzy Rule Base System implementation compatible with the JFML library

Classification and regression techniques are two of the main tasks considered by the Machine Learning area. They mainly depend on the target variable to predict. In this context, ordinal classification represents an intermediate task, which is focused on the prediction of nominal variables where the categories follow a specific intrinsic order given by the problem. Nevertheless, the integration of different algorithms able to solve ordinal classification problems is often unavailable in most of existing Machine Learning software, which hinders the use of new approaches. Therefore, this paper focuses on the incorporation of an ordinal classification algorithm (NSLVOrd) in one of the most complete ordinal regression frameworks, “Ordinal Regression and Classification Algorithms framework (ORCA)” by using both fuzzy rules and the JFML library. The use of NSLVOrd in the ORCA tool as well as a case study with a real database are shown where the obtained results are promising.
Lecture Summarization using Video Processing and Automatic Text Summarization

Nowadays machine learning has achieved a lot of success in technical fields. One of these is video processing but text is recognized which is in a computer readable format, our paper is focusing on detection of handwritten text and summarizing the document for which we found a better result with some methods which are clearly explained in this paper. This paper is following three modules to achieve the better result as from video selecting keyframes without losing whiteboard data, after selecting keyframes to identify the text which is a difficult task can be achieved. Preparing short notes with more accuracy is required for that this paper shows some ML libraries.These paper will give us a brief idea about which methods are used for modules to achieve a good accuracy.
A <scp>Winner‐Take‐All</scp> Autoencoder Based Pieceswise Linear Model for Nonlinear Regression with Missing Data

Abstract Missing data is a prevailing problem in predictive analytics. In this paper, a winner‐take‐all (WTA) autoencoder‐based piecewise linear model is developed to solve the nonlinear regression problem under the missing value scenario, which consists of two parts: an overcomplete WTA autoencoder and a gated linear network. The overcomplete WTA autoencoder is a stacked denoising autoencoder (SDAE) designed to play two roles: (1) to estimate the missing values; (2) to realize a sophisticated partitioning by generating a broad set of binary gate control sequences. Besides, an iterative algorithm with renewed teacher signals is developed to train the SDAE. On the other hand, the gated linear network with the generated binary gate control sequences implements a flexible piecewise linear model for nonlinear regression. By composing a quasi‐linear kernel based on the gate control sequences, the piecewise linear model is then identified in the same way as a support vector regression. Experimental results have shown that our proposed hybrid model has a better performance than traditional models. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.