Reviewerly/exHarmony huggingface dataset:
Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions

It has been shown that equivariant convolution is very helpful for many types of computer vision tasks. Recently, the 2D filter parametrization technique has played an important role for designing equivariant convolutions, and has achieved success in making use of rotation symmetry of images. However, the current filter parametrization strategy still has its evident drawbacks, where the most critical one lies in the accuracy problem of filter representation. To address this issue, in this paper we explore an ameliorated Fourier series expansion for 2D filters, and propose a new filter parametrization method based on it. The proposed filter parametrization method not only finely represents 2D filters with zero error when the filter is not rotated (similar as the classical Fourier series expansion), but also substantially alleviates the aliasing-effect-caused quality degradation when the filter is rotated (which usually arises in classical Fourier series expansion method). Accordingly, we construct a new equivariant convolution method based on the proposed filter parametrization method, named F-Conv. We prove that the equivariance of the proposed F-Conv is exact in the continuous domain, which becomes approximate only after discretization. Moreover, we provide theoretical error analysis for the case when the equivariance is approximate, showing that the approximation error is related to the mesh size and filter size. Extensive experiments show the superiority of the proposed method. Particularly, we adopt rotation equivariant convolution methods to a typical low-level image processing task, image super-resolution. It can be substantiated that the proposed F-Conv based method evidently outperforms classical convolution based methods. Compared with pervious filter parametrization based methods, the F-Conv performs more accurately on this low-level image processing task, reflecting its intrinsic capability of faithfully preserving rotation symmetries in local image features.
Measuring Agile software project success beyond the triple constraint

Background: The triple constraint – cost, scope and time – has been previously used to measure the success of traditional developed software projects. However, these triple constraints are no longer effective to measure the success of Agile software projects. Research shows that there are many other metrics that are being used to measure Agile methods beyond the triple constraint. Organisations are adopting Agile methods for a number of reasons which in turn are benefiting the software developing organisation in various ways. However, these software developing organisations still face challenges when adopting Agile methods.Objective: This study aimed to investigate the different metrics to be considered when measuring Agile software project success beyond the triple constraints.Method: Data were collected through an online survey. The population of interest included Agile specialists who are working on Agile software projects. The collected data were analysed using IBM SPSS version 26.Results: Our results showed that software developing organisations implementing Agile software projects are using strategic metrics that bring value to software developing organisations when measuring their Agile software projects. One of the most cited metrics is customer satisfaction, which is believed to show more value because software projects cannot be accepted unless customers see the value in such projects.Conclusion: There is a pragmatic shift in software development method, adoption and implementation. Organisations that are eager to see success are now shifting from using traditional methods to Agile methods of software development, and by doing so, success is inevitable.
Natural Language Processing based Question Answering Techniques: A Survey

The rapid development in the field of information science and the increase in the usage of information retrieval strategies have empowered users to retrieve more accurate information. The availability of information in a different format and across different has presented colossal difficulties for information retrieval using information retrieval techniques. In this paper, an attempt is made at highlighting the question and answering (QA) systems that provide users the platform to express their question using the Natural Language and also retrieve the response from such systems in Natural Language. The four important modules of any QA system are the Natural Language Question (NLQ) processing, document processing, passage processing, and answer processing. Fundamentally, most QA systems combined several techniques from other fields such as information retrieval, knowledge representation, and natural language processing to process NLQ and present the most insightful response based on the stored document. This paper gives a thorough review of the various survey on the QA systems frameworks, their methodologies, types, approaches, and challenges of QA systems.
Real-time Streaming Perception System for Autonomous Driving

Nowadays, plenty of deep learning technologies are being applied to all aspects of autonomous driving with promising results. Among them, object detection is the key to improve the ability of an autonomous agent to perceive its environment so that it can (re)act. However, previous vision-based object detectors cannot achieve satisfactory performance under real-time driving scenarios. To remedy this, we present the real-time streaming perception system in this paper, which is also the 2nd Place solution of Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) for the detection-only track. Unlike traditional object detection challenges, streaming perception task requires achieving a balance of accuracy and latency rather than focusing on the absolute performance, which is crucial for real-time autonomous driving. Based on YOLOv5, we adopted data augmentation, Bag-of-Freebies, and Transformer to improve streaming object detection performance with negligible extra inference cost. On the Argoverse-HD test set, our method achieved 33.2 streaming AP (34.6 streaming AP verified by the organizer) under the required hardware. Its performance significantly surpasses the fixed baseline of 13.6 (host team), demonstrating the potentiality of application.
Secure Knowledge Sharing in Internet of Vehicles: A DAG-Enabled Blockchain Framework

Knowledge sharing in IoV shows great potential for future vehicular networks. Vehicles, platoons and even traffic infrastructures can exchange the driving experiences or sensing data to facilitate intelligent transportation applications such as autodriving and traffic analysis. However, it is challenging for vehicular knowledge-sharing systems to address the issues brought by information security and vehicular mobility. Although blockchain technology shows defensibility in dealing with trust issues, it is difficult to be applied in large-scale vehicular networks due to the computation consumption of mining process and frequent synchronization of ledger. In this paper, we propose a directed acyclic graph (DAG) enabled knowledge-sharing framework in which vehicular knowledge is encapsulated as a site in the DAG. A new tip selection algorithm (TSA) and a fast authentication scheme for cross-regional vehicles are designed to reduce computation and storage expenditure. Simulation results show that the proposed DAG framework can achieve a higher knowledge sharing quality and lower authentication latency compared with traditional DAG systems.
Development of a Didactic Educational Tool for Learning Fuzzy Control Systems

This paper presents the development of a virtual didactic tool for students of mechatronic engineering taking an intelligent system course. The objective of the tool is for students to learn the structures for fuzzy control systems. This tool makes it easier for students to understand the behavior of the membership functions of input and output variables, the evaluation of the set of fuzzy rules, and the method of defuzzification, giving the students the possibility of applying a fuzzy controller in industrial processes using a data acquisition board. The proposed tool was developed with the virtual instrumentation software LabVIEW. It has the advantage that students can manipulate the internal structure of the fuzzy logic control system in a unique window where students can analyze the behavior of internal signals by looking at the response graphs. The fuzzy controller can be easily translated to a real application by using LabVIEW compatible hardware. To have feedback from students on the use of the tool and to understand if this tool allows an improvement in their academic performance, a 2-hour workshop on the proposed application was given to a group of 93 students. At the end of the workshop, a knowledge assessment and a perception survey were applied to the participants. The academic performance achieved by students who were given the workshop using the proposed teaching tool was compared with the academic performance of students who witnessed the workshop using Matlab tools. The statistical analysis of the results obtained for the knowledge assessment shows that the students that had taken the workshop using the proposed teaching tool had better compression of the topic compared to the students that had taken the workshop using the Fuzzy Logic Toolbox provided by Matlab MathWorks. The students that had taken the workshop using the proposed teaching tool obtained a mean grade of 89.63/100, while students that had taken the workshop using Matlab’s tools obtained a mean grade of 69.85/100. Also, the students’ perception of the proposed tool was that it allowed the design of fuzzy control systems in a simple and intuitive way.
Word Sense Disambiguation Based on Graph and Knowledge Base

Word sense disambiguation determines the specific meaning of ambiguous words according to their specific context, a basic research in the field of natural language processing, and has a direct impact on the upper application of machine translation, information retrieval, text classification, and sentiment analysis. This paper proposes a word sense disambiguation method based on graph and knowledge base, for the insufficient utilization problem of existing knowledge base. It utilizes dependency parsing to obtain contextual knowledge and processes the examples in lexical knowledge base, which have a good ability of sense distinction, to construct disambiguation graph. Then, the disambiguation can be done by combining dependency disambiguation graph and contextual disambiguation graph. Experiments on the dataset of SemEval-2007 task#5 show that the disambiguation accuracy is 0.471, which is better than the mentioned methods.
Joint Coflow Optimization for Data Center Networks

Data parallel applications in data centers generate, process, and store huge volumes of data. Coflow Completion time (CCT) is one of the major performance metrics to capture application-level semantics. This paper is the first one to study the joint consideration of task placement, coflow bandwidth scheduling, and path choice to minimize the average CCT in intra-data center. This paper proposes a joint online scheduling framework, which first develops a 2-approximation algorithm to reduce the CCT of a single coflow, and then follows the Shortest Remaining Time First (SRTF) principle to schedule multiple coflows. Extensive simulations based on practical trace demonstrate that the proposed framework has better performance than the state-of-the-art works.
An Adaptive and Near Parameter-Free BRKGA Using Q-Learning Method

The Biased Random-Key Genetic Algorithm (BRKGA) is an efficient metaheuristic to solve combinatorial optimization problems but requires parameter tuning so the intensification and diversification of the algorithm work in a balanced way. There is, however, not only one optimal parameter configuration, and the best configuration may differ according to the stages of the evolutionary process. Hence, in this research paper, a BRKGA with Q-Learning algorithm (BRKGA-QL) is proposed. The aim is to control the algorithm parameters during the evolutionary process using Reinforcement Learning, indicating the best configuration at each stage. In the experiments, BRKGA-QL was applied to the symmetric Traveling Salesman Problem, and the results show the efficiency and competitiveness of the proposed algorithm.
Zero Inclusion Victim: Isolating Core Caches from Inclusive Last-level Cache Evictions

The most widely used last-level cache (LLC) architecture in the microprocessors has been the inclusive LLC design. The popularity of the inclusive design stems from the bandwidth optimization and simplification it offers to the implementation of the cache coherence protocols. However, inclusive LLCs have always been associated with the curse of inclusion victims. An inclusion victim is a block that must be forcefully replaced from the inner levels of the cache hierarchy when the copy of the block is replaced from the inclusive LLC. This tight coupling between the LLC victims and the inner-level cache contents leads to three major drawbacks. First, live inclusion victims can lead to severe performance degradation depending on the LLC replacement policies. Second, a process can victimize the blocks of another process in an LLC shared by multiple cores and this can be exploited to leak information through well-known eviction-based timing side-channels. An inclusive LLC makes these channels much less noisy due to the presence of inclusion victims which allow the malicious processes to control the contents of the percore private caches through LLC evictions. Third, to reduce the impact of the aforementioned two drawbacks, the inner-level caches, particularly the mid-level cache in a three-level inclusive cache hierarchy, must be kept small even if a larger mid-level cache could have been beneficial in the absence of inclusion victims.We observe that inclusion victims are not fundamental to the inclusion property, but arise due to the way the contents of an inclusive LLC are managed. Motivated by this observation, we introduce a fundamentally new inclusive LLC design named the Zero Inclusion Victim (ZIV) LLC that guarantees freedom from inclusion victims while retaining all advantages of an inclusive LLC. This is the first inclusive LLC design proposal to offer such a guarantee, thereby completely isolating the core caches from LLC evictions. We observe that the root cause of inclusion victims is the constraint that an LLC victim must be chosen from the set pointed to by the set indexing function. The ZIV LLC relaxes this constraint only when necessary by efficiently and minimally enabling a global victim selection scheme in the inclusive LLC to avoid generation of inclusion victims. Detailed simulations conducted with a chip-multiprocessor model using multi-programmed and multi-threaded workloads show that the ZIV LLC gracefully supports large mid-level caches (e.g., half the size of the LLC) and delivers performance close to a non-inclusive LLC for different classes of LLC replacement policies. We also show that the ZIV LLC comfortably outperforms the existing related proposals and its performance lead grows with increasing mid-level cache capacity.
Edge Sensing and Control Co-Design for Industrial Cyber-Physical Systems: Observability Guaranteed Method

The new generation of the industrial cyber-physical system (ICPS) supported by the edge computing technology facilitates the deep integration of sensing and control. System observability is the key factor to characterize the internal relationship of them. In most existing works, the observability is regarded as the assumption for subsequent sensing and control. But, in fact, with the gradually expanded network scale, this assumption is more difficult to directly satisfy sensing design. For this problem, we propose the observability guaranteed method (OGM) for edge sensing and control co-design. Specifically, the nonconvex observability condition is transformed into the convex range of key parameters of the sensing strategy based on the graph signal processing (GSP) technology. Then, we establish the relationship between these parameters and control performance. In OGM, except the previous design from sensing to control, we reversely adjust the sensing design for control demands to satisfy observability. Finally, our algorithm is applied into the hot rolling laminar cooling process based on the semiphysical evaluation. The effectiveness is verified by the results.
Very Short-Term Photovoltaic (PV) Power Forecasting Using Deep Learning (LSTMs)

Transition from carbon-intensive sources to renewable energy sources is of paramount importance and will curb effects of greenhouse gas emissions. India has witnessed rapid expansion in the solar power sector. Prime utilization of solar power calls for a reliable prediction model. Precise real time data of PV systems is the need of the hour. Market today requires the use of very short-term forecasts. This study aims to present a multivariate approach to forecast solar power generation of PV systems on a very short-term basis using LSTM, a deep-learning algorithm. System performance and capabilities are validated on the basis of a standard metric, Mean Absolute Error (MAE). The model incorporates other features of Machine Learning like hyper parameter tuning and optimum feature selection which further refines the accuracy.
Real Time Hand Gesture Recognition Using Leap Motion Controller Based on CNN-SVM Architechture

In rapidly growing field of Artificial Intelligence (AI), Hand Gesture Recognition (HGR) is an important entity. In the real world system it is very challenging to detect and classify Dynamic Hand Gestures (DHG). As there is considerable diversity in gesture performed by individuals and the system should be real time to overcome the delay between performing and classifying the gesture. In this work, we proposed a new approach for efficient HGR using Convolutional Neural Network (CNN) along with Support Vector Machine (SVM) classifier. CNN used to avoid feature extraction and to minimized the number of trained parameters. However, to reduce the error, Error Break Propagation Algorithm (EBPA) is implemented. For the system's validity and robustness SVM optimizer has been used. An overall accuracy of 93 % has achieved on DHG 14/28 dataset.
Sustainability without Geology? A Shortsighted Approach

Over the last few decades, the concept of sustainability has been proposed and championed as the answer to the impending challenges our society will be facing in the future. It has been a rallying opportunity for the broad earth sciences community and a good starting point for such a community to impact societal and policy decisions; however, it has been an opportunity we have largely missed thus far.
A Deep Adversarial Framework for Visually Explainable Periocular Recognition

In the biometrics context, the ability to provide the reasoning behind a decision has been at the core of major research efforts. Explanations serve not only to increase the trust amongst the users of a system, but also to augment the system’s overall accountability and transparency. In this work, we describe a periocular recognition frame-work that not only performs biometric recognition, but also provides visual representations of the features/regions that supported a decision. Being particularly designed to explain non-match ("impostors") decisions, our solution uses adversarial generative techniques to synthesise a large set of "genuine" image pairs, from where the most similar elements with respect to a query are retrieved. Then, assuming the alignment between the query/retrieved pairs, the element-wise differences between the query and a weighted average of the retrieved elements yields a visual explanation of the regions in the query pair that would have to be different to transform it into a "genuine" pair. Our quantitative and qualitative experiments validate the proposed solution, yielding recognition rates that are similar to the state-of-the-art, but - most importantly - also providing the visual explanations for every decision.
Effectiveness Evaluation Method of Application of Mobile Communication System Based on Factor Analysis

The application mode of army mobile communication networks is closely related to combat mission and application environment. Different combat missions and application environments result in different network structures and different service priorities, which requires a semi-automatic system to support the network scheme design. Therefore, evaluating the efficiency of network schemes generated by automatic planning is a problem that needs to be urgently addressed. In the past, researchers have proposed a variety of methods to evaluate the effectiveness of mobile communication systems, most of which are based on simulation methods and ignore the historical data of network usage. This paper studies an effectiveness evaluation method of mobile communication network design schemes and proposes a design scheme for the evaluation and optimization of network plans. Furthermore, the improved method of effectiveness evaluation based on factor analysis is discussed in detail. The method not only effectively uses historical data but also reduces the amount of data collection and calculation. In order to adapt to the preference requirements of different task scenarios, a decision preference setting method based on cluster analysis is proposed, which can render the output optimization result more reasonable and feasible.
Fuzzy Prediction Model to Measure Chatbot Quality of Service

Detecting breakdown is a common phenomenon in the conversational system, which is referred to when the system fails to provide appropriate responses to the user. Existing studi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">es</sup> are detect breakdown using different features such as word similarity, topic transition, and clustering. In this paper, we focus on the different important feature, which is human thinking and reasoning. We use this feature to model chatbot quality of services (CQoS) based on detecting the breakdown. Thus we introduce the fuzzy prediction rule-based framework to measure chatbot quality of service by detecting the breakdown utterance considering end-user and chatbot points of view. Inputs utilized in the proposed fuzzy logic-based model are multiple useful features extracted from utterances. The outputs are the degrees of relevance for each utterance to the quality of services. Several fuzzy rules are designed, and the defuzzification method is used in order to achieve desired CQoS results. Based on the outputs from the fuzzy model, the handover mechanism will activate. We evaluate the proposed formwork with other state-of-the-art models.