Reviewerly/exHarmony huggingface dataset:
Management of sensors in robotic constructions provided by primary school children

The existing work investigates the ways that sensors are being used by primary school children in the automations of educational robotics projects. The research findings revealed that among the available (distance, tilt and sound) sensors the students preferred the distance sensors first, the tilt sensors second and last the sound one in their projects. The study has also recorded that the majority of the contestants made a rather conventional use of the aforementioned sensors. The assessment of the children projects revealed that the beginners predominately used distance sensors, in a conventional way, while their code was ranked in the unistructural level of the SOLO taxonomy. Finally, they used event-driven programming and the interrupt method for event detection. On the other hand, the advanced ones apart from the distance sensor have also used the tilt and the sound sensors, in a more advanced way in terms of innovation. Their code, in terms of the SOLO taxonomy, was ranked between the multi-structural and the relational level and the event detection was held with polling method using a structured programming approach.
Handwritten Text Recognition Using Machine Learning

The human race has shown a huge interest in machines over the years and has developed and advanced to a very large extent in this domain. Starting from the object identification and classification through pictures to editing for the captured image or video everything can be performed through machines and advanced systems, one such part of this advanced technology is deep learning or machine learning. which comes with its own individual set of modules, algorithms, and techniques. Similar to this domain one such idea which was discovered is handwritten digit recognition. This is one of such areas where development and research occur around the numerical also known as digits, where a number of possibilities, permutations, and combinations are attained to gain accurate results this can also be perceived as the ability of computers to interpret and understand the given input which is through number plates, photographs, documents or can be in a handwritten format and to convert it in digital format as an output through screens.
Knowledge Enhanced Transformers System for Claim Stance Classification

In this paper, we present our system for the NLPCC 2021 shared task 1 of "argumentative text understanding for AI debater", where we achieved the 3rd place with the accuracy score of 0.8925 on Track 1, Task 1. Specifically, we proposed a rapid, simple, and efficient ensemble method which uses different pre-trained language models such as BERT, Roberta, Ernie, etc. with various training strategies including warm-up, learning rate schedule and k-fold cross-validation. In addition, we also propose a knowledge enhancement approach, which makes it possible for our model to achieve the first place without introducing external data.
Parallel Antlion Optimisation (ALO) and Grasshopper Optimization (GOA) for Travelling Salesman Problem (TSP)

We present our parallel high-performance version of the adapted Antlion and Grasshopper meta-heuristic algorithms to solve the Travelling Salesman problem. Our detailed experimental evaluation reveals significant improvements over the traditional Genetic and Ant-Colony based solutions in both accuracy and speed with a performance gain of up to 4\(\times \) thereby making it possible to solve Travelling salesman problems for a large number of cities.
Robustness of Named Entity Recognition: Case of Latvian

Recent developments in Named Entity Recognition (NER) have demonstrated good results for grammatically correct texts, even in low resourced settings. However, when the NER model faces ungrammatical text, it often shows poor performance. In this study, we analyze NER performance on datasets containing errors typical for user-generated texts in the Latvian language. We explore three different strategies to increase the robustness of the named entity recognition: error injection into grammatically correct texts, augmenting grammatically correct texts with erroneous texts and augmenting grammatically correct texts with erroneous texts that contain specific types of errors. We demonstrate that in low resourced settings, the best noise-robust model could be obtained by augmenting training data with datasets containing different error types. Our best model achieves an average F1 score of 83.5 (84.1 for baseline) on grammatically correct text, while keeping good performance (79 F1 vs. 66 for baseline) on noisy texts.
Spatially Weighted Mutual Information for Image Registration

In this paper, we have proposed a new similarity metric for image registration. The similarity metric combines mutual information and a weighting function based on alignability of local regions in images. Experimental results show that the new similarity metric can be superior to mutual information and correlation coefficients.
A Recommendation Model for College Career Entrepreneurship Projects Based on Deep Learning

The recommendation system is an active, personalized, and real-time technology platform proposed in the 1990s to solve the problem of information overload. The recommendation system can constantly adjust the recommendation results according to the real-time behaviors of users. In other words, if the user’s interest changes, the recommendation system can present different information to the user. Therefore, the recommendation system is the best way to solve the problem of information overload in entrepreneurial projects. Based on the ConvMF algorithm, this paper proposes an entrepreneurial project recommendation algorithm based on a deep neural network and matrix decomposition. A deep neural network was established for the extraction of the hidden features of entrepreneurial projects, and a convolution neural network was used to process the text description information of entrepreneurial projects. One-hot coding was used to process the regional characteristics and financing round characteristics of entrepreneurial projects, and word embedding was used to process the label features of entrepreneurial projects. The implicit features of users are extracted from the user’s rating matrix using matrix decomposition technology. Finally, recommendations are made according to the implicit characteristics of users and the items learned.
Address Space Layout Randomization Comparative Analysis on Windows 10 and Ubuntu 18.04 LTS

Memory management is one of the main tasks of an Operating System, where the data of each process running in the system is kept. In this context, there exist several types of attacks that exploit memory-related vulnerabilities, forcing Operating Systems to feature memory protection techniques that make difficult to exploit them. One of these techniques is ASLR, whose function is to introduce randomness into the virtual address space of a process. The goal of this work was to measure, analyze and compare the behavior of ASLR on the 64-bit versions of Windows 10 and Ubuntu 18.04 LTS. The results have shown that the implementation of ASLR has improved significantly on these two Operating Systems compared to previous versions. However, there are aspects, such as partial correlations or a frequency distribution that is not always uniform, so it can still be improved.
PSO based Blockchain Committee Member Selection

Implementing blockchain systems with less amount of decentralized computation power is a challenging task. One of the most essential solution in scaling the blockchain is selection of a trusted committee (TC). We intend to develop and apply a new method for selecting committee members depending on their reputation via particle swarm optimization (PSO) based update of the behavior of nodes. Indeed, this is the same concept as accumulating participants in the blockchain throughout the previous rounds of the consensus procedure.
Combination of Variational Autoencoders and Generative Adversarial Network into an Unsupervised Generative Model

Explored building a generative model with the combination of variational autoencoder (VAE) and generative adversarial network (GAN) that offers better result when the agent interacts with the environment. Our agent model can train the unsupervised environment and increase the imaging quality. Moreover, it provides better control option and produce better accuracy results as compared to traditional systems. An experiment performs on the car racing based upon the designed agent model and features can extracted effectively that help to produce reliable information. With the combination of variational autoencoder and generative adversarial network, it provides better feature extraction to gather relevant data and solves the complexity. With the combination of VAE and GAN, elevated-level visual abstraction can be made effectively.
Federal libraries in virtual space: Current state and prospects

Abstract : The findings of monitoring virtual space of federal libraries: their websites and social media, are interpreted. The author analyzes search optimization indicators, i. e. website quality factor, website adaptiveness for mobile systems, as well as behavior indicators, e.g. average visit time, number of viewed pages per visit, etc. The comparison of these findings with prior research evidences on increasing quality of information resources under investigation. The efficiencyоf the federal libraries’ activity in VKontakte (VK), one of the most popular mass media and digital platforms, was analyzed with Socstat.ru web-analytics. The federal libraries’ VK communities are characterized by: number of subscribers, total number of posts, number of posts for a time period, average post length, and the average number of posts per day. Federal library user activity factors areal so analyzed; number of responses, average number of responses, average community engagement, and average post engagement. The author insists on the necessity of monitoring of library presence in the digital environment through web-analytics for libraries efficient integration into the single cultural information space.
A New Approach to Use Modern Object Detection Methods More Efficiently on CCTV Systems

DL architectures rely on extensive usage on powerful computer systems to operate in real-time. Therefore, cooperative and constructive optimizations should be made in both architecture and software parts of the related DL system. In this work, input system of the YOLO architecture is modified to accept several sources at the same time with two effective methods to increase the efficiency of the hardware system. First method is to design a scheduler which will allow YOLO architecture to process several input sources sequentially, allowing the architecture to use its full potential. Second method is to design a preprocessing algorithm to combine 4 or 9 input sources in a single input source as a 2x2 or 3x3 image matrix. In this way, YOLO architecture processes four or nine times more images in the same time, increasing its practical frame per second (FPS) value by four or nine folds. Experiment results on our machine show that the used YOLO architecture can process 3 input sources at the same time with only minimal loss of accuracy of 0.002 in terms of Mean Average Precision (mAP) while using the proposed scheduler. Additionally, using 4 inputs combined increases the practical FPS value from 31 to 120 and using 9 inputs increases the practical FPS value from 13 to 108, all while decreasing the mAP value by only 0.008 for 4 inputs and by only 0.034 for 9 inputs. Considering the obtained FPS values and achieved hardware efficiency, these minimal losses of mAP are easily acceptable.
Query Optimization and Rebalancing Methods based on CMD

As the amount of user data increase, the computer performance and I/O speed required for data processing and analysis are getting higher and higher. Distributed file system has become the primary option for big data storage and query. According to the characteristics of high dimensionality and sparseness of data, this paper uses the distributed storage idea of CMD (coordinate modulo distribution) to store data in blocks. We only need to use cheap storage devices to form a distributed storage system, which solves the problem of big data disk I/O read performance to a certain extent. We have improved range query function under the CMD storage method; at the same time, the optimized B+ tree index technology has been used to solve the precise search problem of sparse data. Finally, in view of the unbalanced distribution of different sub-node data, we propose a new data rebalancing method on the CMD storage method.
On the Comparative Analysis of Sequence Mining Algorithms: Case Study in Telecommunications

This paper examines existing sequence mining algorithms. Sequence mining algorithms are used in many domains, including cyber-security, telecommunications, user behaviour, and air quality patterns. We draw the underlying principles of the representative sequence mining algorithms and introduce a comparative analysis methodology for them. To test the methodology, we provide a prototype testing framework. We conduct a comprehensive experimental study on publicly available data sets, real-life telecommunication data set and data sets generated by a data generator. We compare GSP, PrefixSpan and CMRules algorithms. Comparing these sequence mining algorithms, we conclude that the fastest among the targeted three algorithms may differ for different data sets. Furthermore, we search for situations where sequential pattern mining algorithms can be used instead of sequential rule mining algorithms.
A Survey on Bayesian Graph Neural Networks

Graph Neural Networks (GNNs) is an important branch of deep learning in graph structure. As a model that can reveal deep topological information, GNNs has been widely used in various learning tasks, including physical system, protein interface prediction, disease classification, learning molecular fingerprints and so on. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is to say, there is a lot of uncertainty associated with the underlying graph structure. The method of modeling uncertainty is to use Bayesian framework, in which graph is regarded as random variable. Introducing Bayesian framework into graph-based model, especially for semi-supervised node classification, has been shown that it can produce higher classification accuracy. In this paper, some GNNs models and Bayesian neural networks are introduced to better understand how GNNs are combined with Bayesian. Then, the development of Bayesian graph neural networks(BGNNs) in recent years is summarized, and its application in the field of engineering technology is demonstrated. Finally, the future development of BGNNs is prospected and the full text is summarized.
Improving Robustness of Deep Learning Systems with Fast and Customizable Adversarial Data Generation

Deep Learning (DL) is the force behind the success of solving many complicated tasks in recent years. With the use of DL systems in safety-critical applications, it has become of great importance to make these systems robust against adversarial attacks. Adversarial data generation is an effective tool to make DL systems robust against such attacks, with the help of adversarial training. Recent studies focus on gradient-based adversarial attacks. Although they can successfully generate adversarial samples, high computation cost and lack of flexibility over input generation arise the need for an efficient and flexible adversarial attack methodology. In this paper, we present DeepCustom, a fast and customizable adversarial data generation framework towards bridging this gap. Convolutional autoencoders with custom loss functions, enable user-configurable data generation within a much shorter time compared to the state-of-the-art attack method called PGD. Experiments show that our technique produces adversarial samples faster than PGD and using these samples in adversarial training, allows comparable robustness against adversarial attacks.
Characteristics of Visual Saliency Caused by Character Feature for Reconstruction of Saliency Map Model

Visual saliency maps have been developed to estimate the bottom-up visual attention of humans. A conventional saliency map represents a bottom-up visual attention using image features such as the intensity, orientation, and color. However, it is difficult to estimate the visual attention using a conventional saliency map in the case of a top-down visual attention. In this study, we investigate the visual saliency for characters by applying still images including both characters and symbols. The experimental results indicate that characters have specific visual saliency independent of the type of language.
The Effectiveness of Google Form as An Evaluation Medium In VIII A Grade Of MTs N 6 Tulungagung

This study aims to determine the effectiveness of using Google Form as an evaluation medium in VIII A grade of MTs N 6 Tulungagung. This study involved an English teacher at MTs N 6 Tulungagung and 30 students of class VIII A grade. This research process was carried out online using the main communication WhatsApp application. Data collection techniques in this study were using interviews, observation and documentation. Data analysis techniques used in this research is a qualitative method. The results of the interview show that Google Form has effectiveness to be used as an evaluation medium because it makes easier for teacher to manage students' learning outcomes but there are still several factors that make the use of Google Form effectiveless. In order for the evaluation media to run more effectively the teacher must pay attention to several things such as class preparation, subject matter, references, types of evaluations, directions, and scoring criteria. From the results of the study, it can be said that the use of Google Form in learning evaluation has not been fully implemented effectively because students are still involved in a lot of homework, poor signal areas, lack of awareness when taking online classes.
Quantitative performance evaluation of object detectors in hazy environments

We present a quantitative performance analysis of a wide range of state-of-the-art object detection models, such as Mask R-CNN He et al.(2017)[8], RetinaNet Lin et al.(2017)[17] and EfficinetDet Tan et al.(2019)[28] in haze affected environments. This work uses two key performance metrics (Mean Average Precision and Localised Recall Precision) to provide a nuanced view of real world performance of these models in an on-road driving application. Our findings show that the presence of haze further exacerbates the performance differences between single-stage and multi-stage detection models. In addition, not all aspects of the model performance are affected equally. The inclusion of Local Recall Precision (LRP) Oksuz et al.(2018)[21] suggests that more recent models have much improved localisation performance even with similar false negative and false positive results. We also highlight some of the inherent limitations of Neural Network based approaches that could be addressed by Bayesian Neural Networks in the future.
Path Planning Technology of Unmanned Vehicle Based on Improved Deep Reinforcement Learning

As the basic problem of unmanned vehicle navigation control, path planning has been widely studied. Reinforcement learning (RL) has been found an effective way of path optimization for the highly nonlinear and unmodeled dynamics. However, the RL based methods suffer from the "dimension disaster" under the high-dimension state spaces. In this paper, the path planning of an unmanned vehicle with collision avoidance is considered, and an improved Deep Q-Network (DQN) algorithm is proposed to reduce the computation load in the high-dimension state space. First, the states, actions and rewards are determined based on the task requirement, and a smoothing function is defined as an additional penalty term to modify the basic reward function. Then, the two-dimension grid of the state space is mapped to a gray image, which is applied as the input of a neural network, i.e., the Q-Network. Finally, simulation results show that the modified DQN algorithm is more stable and the fluctuation frequency is significantly reduced.
Two procedures for out-of-plane displacement calculation in simultaneous shearography images

Shearography is an interferometric technique capable of measuring the gradient of the displacement field. Shearography has also been used to determine out-of-plane displacement fields. In the latter case, conventional integration procedures are not the ideal method for determining the displacement field, as they are more susceptible to error propagation. In this work, two integration techniques are presented, the deconvolution technique and a new procedure called analytical integration, both are capable of determining the complete out-of-plane displacement. For the deconvolution technique, an appropriate kernel is proposed to process the sum of two shearography images with orthogonal shear directions. The analytical integration technique makes use of a weighted combination of two shearography images with different shear directions. The proposed techniques were applied to simulated and experimental shearography images obtained in a controlled experiment using a circular aluminum sample. Both techniques showed results with errors lower than 6% compared to the reference displacements.
Direct Edge-to-Edge Many-to-Many Latent Feature Transfer Learning

Smart cameras that leverage edge-computing (edge cameras) are increasingly being combined with deep neural networks to realize the Artificial Intelligence of Things (AIoT), enabling a smart life with “low-touch services,” such as unmanned stores. However, deploying many edge cameras and training their models (edge models) in unmanned stores is time consuming and labor intensive. Therefore, studies have utilized transfer learning methods, but training edge models often need powerful servers. Although we previously proposed direct edge-to-edge instance transfer, this approach did not exploit latent features, still required high bandwidth and long training time, and caused privacy leakage. Therefore, we propose direct edge-to-edge many-to-many latent feature transfer learning, which includes elite latent feature (ELF) extraction, direct edge-to-edge one-to-many latent feature transfer learning (DeOmf), and direct edge-to-edge many-to-one latent feature transfer learning (DeMof). Through ELF extraction, DeOmf allows one source edge camera to transfer latent features to multiple target edge cameras, which improves knowledge reuse and accelerates initial model training. Further, DeMof can help exploit the diversity of multiple source edge cameras for one target edge camera. The experimental results show that DeOmf improves accuracy by 6.30%, reduces training time by 32.15%, and saves 22.92% (maximum 83.33%) on transmission cost. In addition, DeMof increases accuracy by 3.42% and saves 66.99% on training time and 56.67% on transmission cost. These improvements can greatly broaden the applicability of our proposed system when comparing to instance-based transfer learning.
A Variational Training Perspective to GANs for Hyperspectral Image Generation

Utilizing multiple spectral bands provides more information compared to the limited number of channels present in RGB images. However, hyperspectral imaging equipment is expensive. It also poses a hindrance in terms of availability and ease of operation. This work aims to explore the potential Generative Adversarial Networks (GANs) have to generate 31-band hyperspectral images from RGB images. In this work, we employ the idea of neural oscillations to train GANs. Various experiments were conducted on different generative and discriminative models, utilizing different training techniques. The results show that GANs and variable training ratios are a promising approach for hyperspectral image generation.