Reviewerly/exHarmony huggingface dataset:
Research on Concurrency Control in Database Systems

In a database system, if the degree of concurrency is high, using a concurrency control algorithm alone will reduce the performance of the system, and the process of selecting a concurrency control algorithm will virtually improve the knowledge threshold of users. In order to overcome this limitation, this paper proposes a hybrid concurrency control algorithm, which is called cluster based concurrency control algorithm. It creatively puts forward the concept of transaction working set, uses the minimum hash algorithm to calculate the Jaccard similarity between different transaction working sets to measure the conflict rate between different transactions, and uses this as a standard to place transactions in different clusters, Transactions in the same cluster adopt pessimistic concurrency control algorithm, and transactions in different clusters adopt optimistic concurrency control algorithm. The clustering based concurrency control algorithm combines the traditional pessimistic concurrency control algorithm with the optimistic concurrency control algorithm to obtain the advantages of the two algorithms and alleviate the performance bottleneck of the two algorithms. Finally, through simple experiments, it is proved that the concurrency control algorithm based on clustering is indeed better than the traditional pessimistic and optimistic concurrency control algorithms.
An Analysis on the Determination of the Remuneration Among Lower-level Local Council Members: Focusing on the Comparison Among Lower-level Local Governments of Seoul, Gyeongsangnam-do and Gyeongsangbuk-do

This study raises the problem that the local council remuneration system, which started in 2006, is not being established as the purpose of strengthening the capacity of local councils through the entry of competent local talents and pursuing stable legislative activities. Therefore, the purpose of this study was to find the problems and improvement measures through analysis of various factors related to the remuneration of local council members.&#x0D; To this end, this study derived six key factors that influence the decision of remuneration of local councilors based on related laws and previous studies. These key factors include the body in charge of the composition and operation of the remuneration review committee and the recommended field, the composition ratio of each field of the committee, the decision requirements when determining remuneration, the factors to be considered when determining the monthly allowance, the prerequisites for making a decision that exceeds the rate of increase in local public officials’ remuneration, the frequency of the committee meeting.&#x0D; The subject of the fact-finding analysis was 66 local governments within the three higher-level local governments(Seoul Metropolitan City, Gyeongnam and Gyeongbuk Provincial Government).&#x0D; As a result of the analysis, many problems were identified, and based on this, improvement measures could be suggested for each of the six analysis elements.
An Empirical Comparative Analysis of Africans with Asians Using DCNN Facial Biometric Models

Recently, the problem of racial bias in facial biometric systems has generated considerable attention from the media and biometric community. Many investigative studies have been published on estimating the bias between Caucasians and Asians, Caucasians and Africans, and other racial comparisons. These studies have reported inferior performances of both Asians and Africans when compared to other races. However, very few studies have highlighted the comparative differences in performance as a function of race between Africans and Asians. More so, those previous studies were mainly concentrated on a single aspect of facial biometrics and were usually conducted with images potentially captured with multiple camera sensors, thereby compounding their findings. This paper presents a comparative racial bias study of Asians with Africans on various facial biometric tasks. The images used were captured with the same camera sensor and under controlled conditions. We examine the performances of many DCNN-based models on face detection, facial landmark detection, quality assessment, verification, and identification. The results suggested higher performance on the Asians compared to the Africans by most algorithms under the same imaging and testing conditions.
Research on the Promotion of Computer Network Teaching Mode by Big Data Technology

With the continuous progress of social productivity, the continuous development of network, computer science and technology, the original meaningful design courses have a “qualitative” leap. The application of computers to design is a basic ability that every designer must master. This article uses the copy function to repair image damage such as spots, cracks, dirt, etc. Using SketchUp software's material, style, output and other processing and adjustment tools, the drawing work of the building's comprehensive environment and external environment can achieve the expected teaching effect. Some of the many three-dimensional objects made by using 3DMAX software are generated by two-dimensional pattern editing. This is generated by the application edit command. Familiar with the command to edit the points, segments, and lines of line patterns can edit and modify various types of two-dimensional patterns very well and quickly. In the two tests before and after the experimental class, the statistically calculated Z value of 1.94 is very close to the critical value of significant difference (1.96), which is a significant difference. The results show that the big data has certain promoting effect of the teaching pattern of computer aided design.
Action Recognition based on Cross-Situational Action-object Statistics

Machine learning models of visual action recognition are typically trained and tested on data from specific situations where actions are associated with certain objects. It is an open question how action-object associations in the training set influence a model’s ability to generalize beyond trained situations. We set out to identify properties of training data that lead to action recognition models with greater generalization ability. To do this, we take inspiration from a cognitive mechanism called cross-situational learning, which states that human learners extract the meaning of concepts by observing instances of the same concept across different situations. We perform controlled experiments with various types of action-object associations, and identify key properties of action-object co-occurrence in training data that lead to better classifiers. Given that these properties are missing in the datasets that are typically used to train action classifiers in the computer vision literature, our work provides useful insights on how we should best construct datasets for efficiently training for better generalization.
Deep Fusion Network based Ensemble Learning Approach for Detection Intensive Classification

Machine learning has become a popular approach for automatic detection of specific patterns. However, each learning algorithm could have its own advantages and disadvantages for dealing with special types of data, e.g. heuristic algorithms could generally lead to the production of biased classifiers, especially when learning from a small data sample that is unlikely to represent a full population. In order to address the above issue, researchers have been motivated to develop ensemble learning approaches for combining individual classifiers, towards reducing the bias of classifiers and thus advancing the classification performance. In this paper, we propose a deep fusion network based ensemble learning approach, which aims to create more complex ensembles and to achieve a strategic combination of the existing rules of fusion (e.g. majority vote, mean, median and max) in a layer-by-layer processing manner, rather than simply using a single fusion rule. The proposed ensemble learning approach is used for a special type of detection tasks, which involves one class as the target class and the other class as the default class. The performance of the proposed ensemble learning approach is evaluated using 6 UCI data sets and the results show that the proposed ensemble learning approach consistently performs very close to or better than the state-of-the art approaches of individual and ensemble learning, while the individual classifiers and the other types of ensembles varied in their performance on different data sets.
High-Dimensional Discrete Bayesian Optimization with Intrinsic Dimension

Bayesian optimization (BO) has achieved remarkable success in optimizing low-dimensional continuous problems. Recently, BO in high-dimensional discrete solution space is in demand. However, satisfying BO algorithms tailored to this issue still lack. Fortunately, it is observed that high-dimensional discrete optimization problems may exist low-dimensional intrinsic subspace. Inspired by this observation, this paper proposes a Locality Sensitive Hashing based Bayesian Optimization (LSH-BO) method for high-dimensional discrete functions with intrinsic dimension. Via randomly embedding solutions from intrinsic subspace to original space and discretization, LSH-BO turns high-dimensional discrete optimization problems into low-dimensional continuous ones. Theoretically we prove that, with probability 1, there exists a corresponding optimal solution in the intrinsic subspace. The empirically results on both synthetic functions and binary quadratic programming task verify that LSH-BO surpasses the compared methods and possesses the versatility across low-dimensional and high-dimensional kernels.
A Comparative Evaluation of Well-known Feature Extractors for Multi-view Vehicle Tracking in VSNs

The choice of local features to use for feature-based vehicle tracking application is of great importance since the tracking accuracy depends directly on the robustness of the extracted features. Aside of the efficiency of features in terms of analysis accuracy, considering the computation speed and the required transmission bitrate in the choice of the feature extraction algorithm is crucial in order to design a system suitable for the constrained resources of visual sensor networks. In this paper, we evaluate the performance of the four most well-know algorithms for feature detection and extraction, SIFT, SURF, ORB, and BRISK, according to their contribution to the multi-view vehicle matching accuracy, to their computational speed, and to their demands in transmission bitrate. From the obtained results, we deduce that SIFT is the most accurate feature extractor for matching vehicle from two different views, but at the same time, is the most expensive algorithm. ORB is the fastest algorithm with lower matching accuracy compared to SIFT. BRISK represents a good compromise between the computation cost and the matching accuracy.
Contrastive Learning Based Visual Representation Enhancement for Multimodal Machine Translation

Multimodal machine translation (MMT) is a task that incorporates extra image modality with text to translate. Previous works have worked on the interaction between two modalities and investigated the need of visual modality. However, few works focus on the models with better and more effective visual representation as input. We argue that the performance of MMT systems will get improved when better visual representation inputs into the systems. To investigate the thought, we introduce mT-ICL, a multimodal Transformer model with image contrastive learning. The contrastive objective is optimized to enhance the representation ability of the image encoder so that the encoder can generate better and more adaptive visual representation. Experiments show that our mT-ICL significantly outperforms the strong baseline and achieves the new SOTA on most of test sets of English-to-German and English-to-French. Further analysis reveals that visual modality works more than a regularization method under contrastive learning framework.
A Heuristic Solution to the Closest String Problem Using Wave Function Collapse Techniques

The Closest String Problem (CSP) is an NP-Complete problem which seeks to find the geometrical center of a set of input strings: given <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> strings of length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> and a non-negative integer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> , construct a solution string <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> , if it exists, such that the Hamming distance between <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> and each input string is no larger than <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> . This paper proposes WFC-CSP, a novel heuristic algorithm inspired by Wave Function Collapse (WFC) techniques to solve CSP. Experimental results show that WFC-CSP is highly reliable and efficient in solving CSP across different configurations and instance sizes. Using extensive test data sets, WFC-CSP’s performance was compared with multiple state-of-art algorithms including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gramm et al</i> .’s Fixed-parameter algorithm (FP-CSP) in [5], the Ant-CSP algorithm using the (meta)heuristic techniques proposed in [12], the third IP formation algorithm in [19], the LDDA_LSS algorithm in [22] and a sequential version of the heuristic algorithm in [21] referred to as Heuris_Seq; we observe that WFC-CSP outperforms the other algorithms in solution quality or run time or both metrics. The WFC-CSP algorithm has wide applications in solving CSP in the fields of computational biology and coding theory.
Attaining Class-Level Forgetting in Pretrained Model Using Few Samples

In order to address real-world problems, deep learning models are jointly trained on many classes. However, in the future, some classes may become restricted due to privacy/ethical concerns, and the restricted class knowledge has to be removed from the models that have been trained on them. The available data may also be limited due to privacy/ethical concerns, and re-training the model will not be possible. We propose a novel approach to address this problem without affecting the model’s prediction power for the remaining classes. Our approach identifies the model parameters that are highly relevant to the restricted classes and removes the knowledge regarding the restricted classes from them using the limited available training data. Our approach is significantly faster and performs similar to the model re-trained on the complete data of the remaining classes.
An Efficient Offline Signature Verification Method Based on Improved Feature Extraction

As a kind of biometric system, handwritten signature verification has always been a research hotspot. Offline signature verification (OSV), although heavily researched in the past, remains shortcomings such as incomplete feature extraction, too many training samples and inconvenient promotion in practical applications, especially in distinguishing genuine signatures from skilled forgeries, which may be very close in appearance to the genuine ones. To solve these problems, in this paper, we propose a writer-dependent (WD) offline signature verification system, which extracts features from signature images through Gray Level Co-occurrence Matrix (GLCM) and Improved Local Binary Pattern (ILBP) respectively, and these features are fused with geometric features, and finally we use Support Vector Machine (SVM) for classification. The MCYT75 and CEDAR public datasets are used to validate the proposed system, and the experimental results show advantages in both accuracy and average error rate (AER) compared with other existing methods. The accuracy rate (AR) reaches 97.58% and 97.0%, respectively, and 5.64% AER and 3.49% AER, respectively. The experimental results compare favorably with many of the state-of-the-art in handwritten signature verification, proving the usefulness of our designed system.
Wavelet Transform Assisted Neural Networks for Human Activity Recognition

Human activity recognition (HAR) is a challenging area of research with many applications in human-computer interaction. With advances in artificial neural networks (ANNs), methods of HAR feature extraction from wearable sensor data have greatly improved and have increased interest in their classification using ANNs. Most prior work has only investigated the software implementations of ANN-based HAR. Here, we investigate, for the first time, two novel hardware implementations for use in resource-constrained edge devices. Through architecture exploration, we identify first a hybrid ANN we call DCLSTM incorporating the convolutional and long-short-term memory techniques. The second is a much more compact implementation WCLSTM that uses wavelet transforms (WTs) to enhance feature extraction; it can achieve even better accuracy while being smaller and simpler; it is therefore the better choice for resource-constrained applications. We present hardware implementations of these ANNs and evaluate their performance and resource utilization on the UCI HAR and WISDM datasets. Synthesis results on an FPGA platform show the superiority of the WT-assisted version in accuracy and size. Moreover, our networks achieve a better accuracy than earlier published works.
Breast Density Segmentation in Mammograms Based on Dual Attention Mechanism

In response to the problem that poor segmentation accuracy results from artifacts in the mammogram, this paper proposes combining the U-Net with Coordinated Attention and Attention Gates to enhance target feature information and suppress irrelevant regions. First of all, the INbreast dataset is preprocessed to remove external artifacts. Second, in the contracting path, enhance features of the Region of Interest(ROI) of the mammogram through the Coordinate Attention Module. Finally, in the expansive path, the local feature enhancement can be achieved by Attention Gates is used instead to combine the shallow layer and upsampling feature maps directly. The experimental results show that the proposed algorithm has a good segmentation effect for the mammogram, and its the Dice Similarity Coefficient (DSC) and the Intersection of Union(IoU) are 91.8% and 85.8%, respectively. Furthermore, we obtained DSC and IoU of 98.4%, 96.8%, respectively, for women with high breast density. Compared with the conditional Generative Adversarial Networks (cGAN) algorithm, the DSC increased by 3.36%, IoU increased by 5.91%. The better segmentation achieved can help doctors accurately judge breast density categories.
What do you want from me?

Autonomous systems, like drones and self-driving cars, are becoming part of our daily lives. Multiple people interact with them, each with their own expectations regarding system behaviour. To adapt system behaviour to human preferences, we propose and explore a game-theoretic approach. In our architecture, autonomous systems use sensor data to build game-theoretic models of their interaction with humans. In these models, we represent human preferences with types and a probability distribution over them. Game-theoretic analysis then outputs a strategy, that determines how the system should act to maximise utility, given its beliefs over human types. We showcase our approach in a search-and-rescue (SAR) scenario, with a robot in charge of locating victims. According to social psychology, depending on their identity some people are keen to help others, while some prioritise their personal safety. These social identities define what a person favours, so we can map them directly to game-theoretic types. We show that our approach enables a SAR robot to take advantage of human collaboration, outperforming non-adaptive configurations in average number of successful evacuations.
Using Context-Free Grammar to Generate Synthetic Technical Short Texts

Valuable technical information are buried in the under-utilised, user-generated technical texts in engineering domains, such as manufacturing, logistics and maintenance. For maintenance and reliability personnel, the unstructured technical text in maintenance work orders (MWO) hold crucial information about failures and work performed on physical assets. However, the domain-specific language used and scarcity of shared labelled data sets in these contexts present formidable challenges to contemporary natural language processing (NLP) techniques, resulting in inability to achieve performance similar to those in non-engineering domains. In this work, we explore the structure of language in technical short texts by learning a context-free grammar (CFG) through unsupervised grammar induction on industrial MWO texts. We exploit the grammar's generative properties for novel sentence generation and corpus construction and assess its viability for developing synthetic MWO data sets. The results demonstrate a) there exists a grammar in the MWOs, b) the grammar was able to model aspects of the maintenance technical language to produce 12k of synthetic MWO texts 93% as natural and 87% as correct as real texts, and c) the domain-specific language used in technical short text remains challenging to parse due to low data quality and sparsity. Contributions of this work include baseline results for a grammar-based synthetic technical text generation and an appreciation for challenges in assessing the engineering correctness and naturalness of the new synthetic texts.
Technical Job Distribution at BSD SHARP Service Center Using Combination of Naïve Bayes and K-Nearest Neighbour

Works distribution is a routine carried out every day by the head of the branch in the SHARP Service Center. The accuracy of the labor division is very important to get customer satisfaction. Inappropriate work distribution can increase complaints from customers. Currently, works distribution in SHARP Service Center is carried out manually, where the works received on the selected system is then shared through the document provided. Time taken for this process is about 1.42 minutes on average for each damage reports. Speed of Service also depends on the Head of Department's expertise and experience. In this study, an automatic system based on Machine Learning will be designed for the technicians work distribution by using a combination of k Nearest Neighbor (k-NN) and Naïve Bayes. Naïve Bayes algorithm is used to improve the feature extraction accuracy by considering the feature below the average (α). Meanwhile, k-NN algorithm is used to classify the experimental data. From the study, it is found that the best of k value for k-NN algorithm is 15. It is known that a high number of accuracy values, the labor distribution can be more accurate. The validation of the proposed method is conducted by using a confusion matrix with a composition of 80% training data and 20% test data. The single Classifier test with the Naïve Bayes algorithm produces the highest accuracy value of 72.7%, while using k-NN algorithm is 81.5%. With a combination of Naive Bayes and k-NN algorithms, the accuracy value is increasing to 86%. This result shows that the proposed method improves the accuracy by 13.3% on single Naive Bayes algorithms and 4% on a single k-NN algorithm. The results obtained show that in the manual process, the average time per job is 1.42 minutes, while by using the proposed method, the average processing time is around 0.03 seconds per job. An increase of 2480 times faster is found and confirmed during the implementation of the proposed method.