Reviewerly/exHarmony huggingface dataset:
Representing logic gates over Euclidean space via heaviside step function

Theoretical concepts asserted by Alan Turing are the basis of the computation and hence of machine intelligence. Turing Machine, the fundamental computational model, has been proven to be reducible to a logic circuit and, at the same time, portable into a computer program that can be expressed through a combination of fundamental programming language control structures. This work proposes a mathematical framework that analytically models logic gates employing Heaviside Step Function. The existence of a correspondence between a generic finite-time algorithm and the proposed mathematical formulation is proven. The proposed interpretation is given through a well-defined logical circuit analytical expression. Relevant geometrical applications, related to polygon processing, having wide implications in engineering branches are presented together with a new Penalty Method for constrained optimization problems handling. A detailed simulation campaign is conducted to assess the effectiveness of the applications derived from the proposed mathematical framework.
Mob Analyzer

Crowd detection and analysis have been widely used from urban design and traffic management to disaster and pandemic evacuation and mobility prediction. Currently, several technologies have been incorporated to manage the crowd but it is not efficient due to the wide range of the population. Crowd observation in public places is an incredibly exigent endeavor to accomplish. The immense population and assortment of human actions enforce the crowded scenes to be additional continual. Monumental challenges occur in crowd management together with correct crowd analysis, identification, observation, and anomalous activity in crowd observation. because of severe litter and occlusions, typical methods for managing the crowd don't seem to be effective. This proposed system highlights the various problems concerned in analyzing crowd behavior and its dynamics together with the classification of crowd analysis techniques people counting/density estimation, folks trailing, and behavior understanding or anomaly detection. We need to find the position of a private in sequent frames. The issues of individuals investigation and trailing area unit correlative, as each has the target of characteristic folks in the thronged scene. However, the matter of counting usually has to approximate the number of participants in-crowd, rather than their position. Thus, the alert system focuses on scrutinizing the status of individuals to inform the authorities in case of risky behavior and mob commotion.
Quantum-effective exact multiple patterns matching algorithms for biological sequences

This article presents efficient quantum solutions for exact multiple pattern matching to process the biological sequences. The classical solution takes Ο(mN) time for matching m patterns over N sized text database. The quantum search mechanism is a core for pattern matching, as this reduces time complexity and achieves computational speedup. Few quantum methods are available for multiple pattern matching, which executes search oracle for each pattern in successive iterations. Such solutions are likely acceptable because of classical equivalent quantum designs. However, these methods are constrained with the inclusion of multiplicative factor m in their complexities. An optimal quantum design is to execute multiple search oracle in parallel on the quantum processing unit with a single-core that completely removes the multiplicative factor m, however, this method is impractical to design. We have no effective quantum solutions to process multiple patterns at present. Therefore, we propose quantum algorithms using quantum processing unit with C quantum cores working on shared quantum memory. This quantum parallel design would be effective for searching all t exact occurrences of each pattern. To our knowledge, no attempts have been made to design multiple pattern matching algorithms on quantum multicore processor. Thus, some quantum remarkable exact single pattern matching algorithms are enhanced here with their equivalent versions, namely enhanced quantum memory processing based exact algorithm and enhanced quantum-based combined exact algorithm for multiple pattern matching. Our quantum solutions find all t exact occurrences of each pattern inside the biological sequence in O((m/C)N) and O((m/C)t) time complexities. This article shows the hybrid simulation of quantum algorithms to validate quantum solutions. Our theoretical-experimental results justify the significant improvements that these algorithms outperform over the existing classical solutions and are proven effective in quantum counterparts.
Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution

Crowd counting is an important part of crowd analysis and has been widely applied in the field of public safety and commercial management. Although researchers have proposed many crowd counting methods, there is little research on non-uniform population distribution. In this research, a new scene adaptive segmentation network (SASNet) is proposed that can focus on crowd area to estimate accurately crowd density in population heterogeneous distribution. First, an image segmentation module is designed that can adaptive horizontal segment an image according to different density levels, and then obtains a close-up view image and a distant view image. Second, a dual branches network based on convolution neural network (CNN) is exploited that contains a distant view network (DVNet) and a close-up view network (CVNet), so as to extract different scales of image features and then generate density maps by each branch, respectively, so that the crowd counting module has robustness on different scales of target. Finally, a comparative experiment on three well-known crowd counting datasets shows that SASNet achieved stabilized performance and robustness in population heterogeneous distribution.
Application of Project-driven Teaching Method in Irrigation and Drainage Engineering

Irrigation and Drainage Engineering is the core compulsory course of agricultural water conservancy engineering. It is professional and practical, cultivating students' ability of planning and design and practice. The traditional teaching method cannot integrate theory with practice well. Therefore, based on the current situation of agricultural water conservancy project teaching in Hebei Agricultural University, Application of project-driven teaching method for Irrigation and Drainage Engineering was discussed in paper, and the main teaching design process was put forward, which provides reference for further deepening the teaching reform of Irrigation and Drainage Engineering.
Weakly-Supervised Action Detection Guided by Audio Narration

Videos are more well-organized curated data sources for visual concept learning than images. Unlike the 2-dimensional images which only involve the spatial information, the additional temporal dimension bridges and synchronizes multiple modalities. However, in most video detection benchmarks, these additional modalities are not fully utilized. For example, EPIC Kitchens is the largest dataset in first-person (egocentric) vision, yet it still relies on crowdsourced information to refine the action boundaries to provide instance-level action annotations.We explored how to eliminate the expensive annotations in video detection data which provide refined boundaries. We propose a model to learn from the narration supervision and utilize multimodal features, including RGB, motion flow, and ambient sound. Our model learns to attend to the frames related to the narration label while suppressing the irrelevant frames from being used. Our experiments show that noisy audio narration suffices to learn a good action detection model, thus reducing annotation expenses.
A novel end-to-end neural network for simultaneous filtering of task-unrelated named entities and fine-grained typing of task-related named entities

Recently, one emerging problem in Named Entity Typing (NET) is the fine-grained classification of task-related entities co-existing with task-unrelated entities. The traditional pipeline framework decomposes this problem into two sub-tasks. The first sub-task filters out the task-unrelated entities, while the second sub-task performs fine-grained classification for task-related entities. In the present study, we have developed an end-to-end neural network to solve the two sub-tasks simultaneously. The new model has two main merits. First, Mention–Mention (MM) relationship learning is developed to capture the interaction of task related and unrelated entities for producing more discriminative features. Second, an Improved Radial Basis Function classifier (ImRBF) with a novel training scheme is developed to jointly solve task-unrelated entity filtering and fine-grained classification of task-related entities. Experiments show that our model outperforms the pipeline methods by 3.3%–6% (F1 score) on the first sub-task and 1.8%–6.3% (F1 score) on the second sub-task.
PBL Writing Lessons and Guidance through a Critical Biography Writing Class

The purpose of this article is to analyze the actual operation cases of the PBL writing task conducted during a class at Sejong University and to clarify the PBL nature and the meaning of this task. Among the problems given in Sejong University's Writing and Presentation for Problem Solving in the first semester of 2018, the students were assigned with the following PBL writing task: “Write a review of ordinary neighbors around us.” This problem tackled genre literature, such as existing reviews and autobiographies, and attempted to approach it anew through new subjects. It had a project-based element in that team activities were necessarily premised in the process of performing the task. Moreover, it had a subjective-based element suitable for the PBL task in that it had an unstructured characteristic to it. At the same time, the fundamental meaning of writing can be found through expressive writing, which has recently been somewhat neglected in college. In addition, the goal was for the students to reflect on the lives of ordinary people in our society, compared to those of the elites who succeeded in describing existing reviews and autobiographies. Interviews with subjects that had to be conducted during the writing process were meaningful in that the students had the experience of enhancing their inter-generational communication skills and constructing data that ended up creating a sort of Korean micro history. Furthermore, from this study we can see that all processes, from data research to actual writing, have improved the students' intellectual abilities and allowed for a degree of reflection and discussion that they were unable to experience from their major courses.
Modeling Self-Representation Label Correlations for Textual Aspects and Emojis Recommendation

The rapid development of Internet services and social platforms encourages users to share their opinions. To help users give valuable comments, content providers expect the recommender system to offer appropriate suggestions, including specific features of the item described in texts and emojis, which are all considered aspects of the user reviews. Hence, the review aspect recommendation task has become significant, where the key lies in handling personal preferences and semantic correlations between suggested items. This article proposes a correlation-aware review aspect recommender (CARAR) system model by constructing self-representation correlations between different views of review aspects, including textual aspects and emojis to make a personalized recommendation. The dependencies between different textual aspects and emojis can be identified and utilized to facilitate the factorization process to learn user and item latent factors. The cross-view correlation mapping between textual aspects and emojis can be built to enhance the recommendation performance. Moreover, the additional information in the real-world environment is also applied to our model to adjust the recommendation results. We constructed experiments on five self-collected and public datasets and compared with six existing models. The results show that our model can outperform the existing models on review aspects recommendation tasks, validating the effectiveness of our approach.
Team of Tiny ANNs: A Way Towards Cost-Efficient Scalable Deep Learning

Deep neural networks (DNNs) have latterly accomplished enormous success in various image recognition tasks. Although, training large DNN models are computationally expensive and memory intensive. So the natural idea is to do network compression and acceleration without significantly diminishing the performance of the model. In this paper, we propose a rapid and accurate method of training a neural network that has a small computation time and fewer parameters. The features are extracted using the Discrete Wavelet Transform (DWT) method. A voting-based classifier comprising a team of tiny artificial neural networks is proposed. The proposed classifier combines all the classification votes from the different sub-bands (models) to obtain the final class label, thus, achieving a similar classification accuracy of standard neural network architecture. The experiments were illustrated on benchmark data-sets of MNIST and EMNIST. On MNIST dataset, the trained models achieve the highest accuracy of 93.16 % for original and 90.44 % for Low-Low (LL) sub-band images. On the EMNIST dataset, accuracy of 90.13% for original and 87.40% for LL sub-band images has been obtained, respectively.
A Comparative Study of ConvoBiGRU and ConvoBiLSTM on Amazon Dataset

The categorization of emotions (positive and negative) inside data using text analysis techniques is referred as sentiment analysis. Sentiment analysis can aid in the rapid acquisition of concepts from massive amounts of textual data. Using the power of digital technology, sentiment analysis models can be trained to comprehend literature beyond simple notions, read for context, irony, and other elements, and detect the writer's genuine mood and feeling. In today's world, companies may have mountains of customer feedback, and when they're drowning in data, sentiment research can help them figure out what the most serious worries are. Sentiment analysis could be used to focus on user comments that have a significant negative tone. Similarly, we can look at positive client comments to see why they love us. As a result of recent advances in deep learning, algorithm's capacity to analyze text has substantially improved. Advanced artificial intelligence algorithms, when used creatively, can be a great tool for conducting in-depth research. The appropriate classifier is chosen by comparing the accuracy of two distinct deep learning-based models: ConvoBiLSTM and ConvoBiGRU, in order to increase classification accuracy. According to the evaluation results ConvoBiGRU perform better when compared to ConvoBiLSTM Model. Large scale e-commerce businesses such as FlipKart, Amazon, and a host of other online marketplaces would be able to obtain the product's polarized interpretation. As a result, their sales revenue rises. Customers believe they are being heard and that their requirements are being met, which improves a company's image.
Reading signboards for the visually impaired using Pseudo-Zernike Moments

It is necessary to provide essential daily support for the visually impaired, allowing them to locate themselves in an underground station, to change trains, to read signboards like those in doctors offices, law firms, shops, etc. In this paper, we present a Reading Signboards System (called RSbS) based on Pseudo Zernike Moments (PZMs) and stroke features from a video captured by a camera. The system is made up of three main steps: (i) signboard detection, (ii) text localization and (iii) filtering of non-textual regions. In a video, signboard region contains all the necessary information. Therefore, the signboard region should be located and segmented from the other parts of a frame that will be considered as the background. Signboards localization and segmentation are performed using Pseudo Zernike Moments. Text localization and extraction is done using PZMs segmentation which is based on the HSV color space and specifically on the Value channel. Non-textual regions will be filtered out using the outline function. System evaluation shows that it is robust to changes in lighting, low resolution and camera tilt, and it gives very few false positives compared to other approaches. In addition, the resulting images are transmitted to an optical character recognition engine (the best known is OCR) to extract the text which will be converted to speech using one of the existing APIs like Google Text to Speech API (gTTS API).
Hybrid Smart Contracts for Privacy-Preserving-Aware Insurance Compensation

Large-scale applications of electronic medical records (EHRs) based on blockchain technology have prompted us to find an intelligent way to realize insurance compensation, which brings convenience for both insurance companies and patients. However, due to the public verifiability of blockchains, straightforward utilizing a blockchain to construct an insurance compensation scheme would cause leakage of patients&#x2019; privacy. In this paper, we propose an agent compensation model based on smart contract to guarantee the credibility of EHRs and to enable automatical insurance compensation without requiring interactions between insurance companies and patients. A hybrid smart contract privacy-preserving insurance compensation scheme is put forward based on the agent compensation model. The "private" and "public" smart contracts are deployed on the private and public blockchain respectively. By designing private smart contracts, we limit the visitors of private data and prevent third parties from accessing patient private data. By deploying public smart contract that introduces zero-knowledge proof and blockchain oracles, we realize compensations information verification and agent payment without privacy data leakage. Security analysis and performance evaluations are conducted to prove that our scheme is secure against various attacks while achieving high efficiency.
Parameter Sensitive Pointer Analysis for Java

Pointer analysis is an important tool for program analysis and program optimization procedures used in modern compilers and bug checkers. This paper introduces parameter-sensitivity, a new methodology that improves pointer analysis for object-oriented programming languages such as Java. In this approach, we construct a method context for a callee method by a list of objects including both the receiver object and the objects that are passed as parameters at a call site. We believe that such a context is able to represent a significant portion of the current abstract state before the method invocation. The algorithm has been implemented in the Soot framework, and it is evaluated on the benchmarks from DaCapo, compared with the standard object-sensitive pointer analysis algorithms. The preliminary result demonstrates that our parameter-sensitive pointer analysis can achieve better precision with less time consumption than the standard k-object-sensitive algorithms.
Impact of COVID-19 pandemic to EDUCON: a bibliometric analysis

In recent years, bibliometrics has been widely used as a methodology for the analysis of scientific production, proving to be an excellent instrument for supporting scientific policy decisions. In this paper, we present a bibliometric analysis of the 12 editions of the Global Engineering Education Conference (EDUCON). Several indicators were used, namely number of submitted articles, the rate of accepted submissions, number of article citations, and the number of references per article, among other indicators. We also intend to add the comparison of the results obtained in the ten first editions held in-site and the last two editions, which have the particularity of having been carried out fully online, evaluating, by this way, the impact of the COVID-19 pandemic crisis to EDUCON. The results show an increase in the number of papers submitted and published over the years, evidencing a growing notoriety of the conference. The same growth tendency is visible regarding the number of authors involved in the conference. Regarding the impact of the online editions, data from the next edition is still required to establish better comparisons and conclusions.
PCA-Pruner: Filter pruning by principal component analysis

Deep Convolutional Neural Networks (CNNs) have been widely used in various domains due to their outstanding performance. However, they simultaneously bring enormous computational overhead, making it difficult to deploy to mobile and edge devices. Therefore, researchers use network compression techniques such as quantization, knowledge distillation and neural network pruning to alleviate this problem. Among network pruning, filter pruning has received broad attention. At present, most of the filter pruning methods need to define pruning rates manually, which is a trial-and-error process and requires rich experimental experience. Some methods obtain global optimal network parameters by Neural Architecture Search (NAS) or Evolutionary Algorithms (EA) to overcome this difficulty. However, they also introduce huge computational burden. To mitigate the above problems, this study proposes a pruning strategy based on Principal Component Analysis (PCA) called PCA-Pruner. Filter weights of a layer is regarded as a set of features, and the number of filters responding to feature dimension. Then, the number of reserved filters in each layer can be determined by PCA which is a classical dimensionality reduction technology. After that, we calculate the L1 norm of each filter in each layer and use it as an importance measurement to prune filters. Experimental results show that PCA-Pruner achieve performance improvements over the state-of-the-arts algorithms. For example, we compress the FLOPs and parameters of ResNet-56 on CIFA-10 by 45.8% and 47.1%, with an increase in accuracy of 0.27%. For ResNet-110 on CIFAR-10, we improve the accuracy by 0.58% and reduce the FLOPs and Params of the model by 58.3% and 56.2%, respectively. Towards ResNet-56 on CIFAR-100 dataset, we achieve a 38.8% FLOPs decrease and 38.0% Params reduction with only 0.69% accuracy loss.
How to divide a permission token in the delegation process of blockchain-based access control for IoT

There are several security problems arising from the characteristics of IoT, and one of them is weak access control. Traditional access control models require one centralized authority that stores all the information for access control and validates access rights. This single point of failure in IoT access control could lead to situations where a single breach can cause sensitive information leakage across the entire system. Various studies have been conducted to mitigate this security risk by introducing a decentralized architecture based on blockchain technology called BBAC. However, most BBAC models consider only a simple access control situation, which can lead to a “the Greatest privilege problem”. This study proposes a novel access control model that enforces minimum privilege to an access token by the division and modification of access rights. As a result, we contributed to enhancing the practicality of the BBAC and mitigating risks that may arise in the delegation process.