Reviewerly/exHarmony huggingface dataset:
Commonsense knowledge in cognitive robotics: a systematic literature review

Authors:
raw_author_name: Axel-Cyrille Ngonga Ngomo
raw_affiliation_string: DICE Group, Germany; Joint Research Center on Cooperative and Cognition-enabled AI, Germany
raw_author_name: Michael Beetz
raw_affiliation_string: Institute for Artificial Intelligence, Germany; Joint Research Center on Cooperative and Cognition-enabled AI, Germany
raw_author_name: Philipp Cimiano
raw_affiliation_string: Center for Cognitive Interaction Technology, Germany; Joint Research Center on Cooperative and Cognition-enabled AI, Germany

Abstract: One of the big challenges in robotics is the generalization necessary for performing unknown tasks in unknown environments on unknown objects. For us humans, this challenge is simplified by the commonsense knowledge we can access. For cognitive robotics, representing and acquiring commonsense knowledge is a relevant problem, so we perform a systematic literature review to investigate the current state of commonsense knowledge exploitation in cognitive robotics. For this review, we combine a keyword search on six search engines with a snowballing search on six related reviews, resulting in 2,048 distinct publications. After applying pre-defined inclusion and exclusion criteria, we analyse the remaining 52 publications. Our focus lies on the use cases and domains for which commonsense knowledge is employed, the commonsense aspects that are considered, the datasets/resources used as sources for commonsense knowledge and the methods for evaluating these approaches. Additionally, we discovered a divide in terminology between research from the knowledge representation and reasoning and the cognitive robotics community. This divide is investigated by looking at the extensive review performed by Zech et al. (The International Journal of Robotics Research, 2019, 38, 518–562), with whom we have no overlapping publications despite the similar goals.
Multi-channel high-order network representation learning research

Authors:
raw_author_name: Zhonglin Ye
raw_affiliation_string: School of Computer, Qinghai Normal University,  Xining, China
raw_author_name: Yanlong Tang
raw_affiliation_string: School of Computer, Qinghai Normal University,  Xining, China
raw_author_name: Haixing Zhao
raw_affiliation_string: School of Computer, Qinghai Normal University,  Xining, China
raw_author_name: Zhaoyang Wang
raw_affiliation_string: School of Computer, Qinghai Normal University,  Xining, China
raw_author_name: Ying Ji
raw_affiliation_string: School of Computer, Qinghai Normal University,  Xining, China

Abstract: The existing network representation learning algorithms mainly model the relationship between network nodes based on the structural features of the network, or use text features, hierarchical features and other external attributes to realize the network joint representation learning. Capturing global features of the network allows the obtained node vectors to retain more comprehensive feature information during training, thereby enhancing the quality of embeddings. In order to preserve the global structural features of the network in the training results, we employed a multi-channel learning approach to perform high-order feature modeling on the network. We proposed a novel algorithm for multi-channel high-order network representation learning, referred to as the Multi-Channel High-Order Network Representation (MHNR) algorithm. This algorithm initially constructs high-order network features from the original network structure, thereby transforming the single-channel network representation learning process into a multi-channel high-order network representation learning process. Then, for each single-channel network representation learning process, the novel graph assimilation mechanism is introduced in the algorithm, so as to realize the high-order network structure modeling mechanism in the single-channel network representation learning. Finally, the algorithm integrates the multi-channel and single-channel mechanism of high-order network structure joint modeling, realizing the efficient use of network structure features and sufficient modeling. Experimental results show that the node classification performance of the proposed MHNR algorithm reaches a good order on Citeseer, Cora, and DBLP data, and its node classification performance is better than that of the comparison algorithm used in this paper. In addition, when the vector length is optimized, the average classification accuracy of nodes of the proposed algorithm is up to 12.24% higher than that of the DeepWalk algorithm. Therefore, the node classification performance of the proposed algorithm can reach the current optimal order only based on the structural features of the network under the condition of no external feature supplementary modeling.
Validation of system usability scale as a usability metric to evaluate voice user interfaces

Authors:
raw_author_name: Akshay Madhav Deshmukh
raw_affiliation_string: Systems Integration and Re-Engineering Group (IRIS), Department of Computer Languages and Systems, Universitat Jaume I de Castellón, Castello de la Plana, Castellon, Spain.
raw_author_name: Ricardo Chalmeta
raw_affiliation_string: Systems Integration and Re-Engineering Group (IRIS), Department of Computer Languages and Systems, Universitat Jaume I de Castellón, Castello de la Plana, Castellon, Spain.

Abstract: In recent years, user experience (UX) has gained importance in the field of interactive systems. To ensure its success, interactive systems must be evaluated. As most of the standardized evaluation tools are dedicated to graphical user interfaces (GUIs), the evaluation of voice-based interactive systems or voice user interfaces is still in its infancy. With the help of a well-established evaluation scale, the System Usability Scale (SUS), two prominent, widely accepted voice assistants were evaluated. The evaluation, with SUS, was conducted with 16 participants who performed a set of tasks on Amazon Alexa Echo Dot and Google Nest Mini. We compared the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. Furthermore, we derived the confidence interval for both voice assistants. To enhance understanding for usability practitioners, we analyzed the Adjective Rating Score of both interfaces to comprehend the experience of an interface’s usability through words rather than numbers. Additionally, we validated the correlation between the SUS score and the Adjective Rating Score. Finally, a paired sample t-test was conducted to compare the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. This resulted in a huge difference in scores. Hence, in this study, we corroborate the utility of the SUS in voice user interfaces and conclude by encouraging researchers to use SUS as a usability metric to evaluate voice user interfaces.
A bilingual benchmark for evaluating large language models

Authors:
raw_author_name: Mohamed Alkaoud
raw_affiliation_string: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Abstract: This work introduces a new benchmark for the bilingual evaluation of large language models (LLMs) in English and Arabic. While LLMs have transformed various fields, their evaluation in Arabic remains limited. This work addresses this gap by proposing a novel evaluation method for LLMs in both Arabic and English, allowing for a direct comparison between the performance of the two languages. We build a new evaluation dataset based on the General Aptitude Test (GAT), a standardized test widely used for university admissions in the Arab world, that we utilize to measure the linguistic capabilities of LLMs. We conduct several experiments to examine the linguistic capabilities of ChatGPT and quantify how much better it is at English than Arabic. We also examine the effect of changing task descriptions from Arabic to English and vice-versa. In addition to that, we find that fastText can surpass ChatGPT in finding Arabic word analogies. We conclude by showing that GPT-4 Arabic linguistic capabilities are much better than ChatGPT's Arabic capabilities and are close to ChatGPT's English capabilities.
Research on the evaluation method of English textbook readability based on the TextCNN model and its application in teaching design

Authors:
raw_author_name: Ying Qin
raw_affiliation_string: School of Foreign Languages, Wuzhou University, Wuzhou, China
raw_author_name: Azeem Irshad
raw_affiliation_string: Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan

Abstract: English is a world language, and the ability to use English plays an important role in the improvement of college students’ comprehensive quality and career development. However, quite a lot of Chinese college students feel that English learning is difficult; it is difficult to understand the learning materials, and they cannot effectively improve their English ability. This study uses a convolutional neural network to evaluate the readability of English reading materials. It provides students with English reading materials of suitable difficulty based on their English reading ability so as to improve the effect of English learning. Aiming at the high dispersion of students’ English reading level, a text readability evaluation model for English reading textbooks based on deep learning is designed. First, the legibility dataset is constructed based on college English textbooks; second, the TextCNN text legibility evaluation model is constructed; finally, the model training is completed through parameter adjustment and optimization, and the evaluation accuracy rate on the self-built dataset reaches 90%. We use the text readability method based on TextCNN model to conduct experimental teaching, and divided the two groups into comparative experiments. The experimental results showed that the reading level and reading interest of students in the experimental group were significantly improved, which proved that the text readability evaluation method based on deep learning was scientific and effective. In addition, we will further expand the capacity of the English legibility dataset and invite more university classes and students to participate in comparative experiments to improve the generality of the model.
Enhancing medical text detection with vision-language pre-training and efficient segmentation

Authors:
raw_author_name: Tianyang Li
raw_affiliation_string: College of Computer Science and Technology, Northeast Electric Power University, Jilin, 132012, China
raw_author_name: Qingzhu Wang
raw_affiliation_string: College of Computer Science and Technology, Northeast Electric Power University, Jilin, 132012, China

Abstract: Abstract Detecting text within medical images presents a formidable challenge in the domain of computer vision due to the intricate nature of textual backgrounds, the dense text concentration, and the possible existence of extreme aspect ratios. This paper introduces an effective and precise text detection system tailored to address these challenges. The system incorporates an optimized segmentation module, a trainable post-processing method, and leverages a vision-language pre-training model (oCLIP). Specifically, our segmentation head integrates three essential components: the Feature Pyramid Network (FPN) module, which combines a residual structure and channel attention mechanism; the Efficient Feature Enhancement Module (EFEM); and the Multi-Scale Feature Fusion with RSEConv (MSFM-RSE), designed specifically for multi-scale feature fusion based on RSEConv. By introducing a residual structure and channel attention mechanism into the FPN module, the convolutional layers are replaced with RSEConv layers that employ a channel attention mechanism, further augmenting the representational capacity of the feature maps. The EFEM, designed as a cascaded U-shaped module, incorporates a spatial attention mechanism to introduce multi-level information, thereby enhancing segmentation performance. Subsequently, the MSFM-RSE adeptly amalgamates features from various depths and scales of the EFEM to generate comprehensive final features tailored for segmentation purposes. Additionally, a post-processing module employs a differentiable binarization strategy, allowing the segmentation network to dynamically determine the binarization threshold. Building on the system’s improvement, we introduce a vision-language pre-training model that undergoes extensive training on various visual language understanding tasks. This pre-trained model acquires detailed visual and semantic representations, further reinforcing both the accuracy and robustness in text detection when integrated with the segmentation module. The performance of our proposed model was evaluated through experiments on medical text image datasets, demonstrating excellent results. Multiple benchmark experiments validate its superior performance in comparison to existing methods. Code is available at: https://github.com/csworkcode/VLDBNet .
A low-light image enhancement method for personnel safety monitoring in underground coal mines

Authors:
raw_author_name: Shuai Wang
raw_affiliation_string: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China
raw_author_name: Wei Chen
raw_affiliation_string: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China
raw_author_name: Zijian Tian
raw_affiliation_string: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China

Abstract: Abstract Intelligent monitoring technology plays an important role in promoting the development of coal mine safety management. Low illumination in the coal mine underground leads to difficult recognition of monitoring images and poor personnel detection accuracy. To alleviate this problem, a low illuminance image enhancement method proposed for personnel safety monitoring in underground coal mines. Specifically, the local enhancement module maps low illumination to normal illumination at pixel level preserving image details as much as possible. The transformer-based global adjustment module is applied to the locally enhanced images to avoid over-enhancement of bright areas and under-illumination of dark areas, and to prevent possible color deviations in the enhancement process. In addition, a feature similarity loss is proposed to constrain the similarity of target features to avoid the possible detrimental effect of enhancement on detection. Experimental results show that the proposed method improves the detection accuracy by 7.1% on the coal mine underground personal dataset, obtaining the highest accuracy compared to several other methods. The proposed method effectively improves the visualization and detection performance of low-light images, which contributes to the personnel safety monitoring in underground coal mines.
Leveraging pre-trained language models for code generation

Authors:
raw_author_name: Ahmed Soliman
raw_affiliation_string: Department of Computer Engineering, Cairo University, Giza, Egypt
raw_author_name: Samir Shaheen
raw_affiliation_string: Department of Computer Engineering, Cairo University, Giza, Egypt
raw_author_name: Mayada Hadhoud
raw_affiliation_string: Department of Computer Engineering, Cairo University, Giza, Egypt

Abstract: Abstract Code assistance refers to the utilization of various tools, techniques, and models to help developers in the process of software development. As coding tasks become increasingly complex, code assistant plays a pivotal role in enhancing developer productivity, reducing errors, and facilitating a more efficient coding workflow. This assistance can manifest in various forms, including code autocompletion, error detection and correction, code generation, documentation support, and context-aware suggestions. Language models have emerged as integral components of code assistance, offering developers the capability to receive intelligent suggestions, generate code snippets, and enhance overall coding proficiency. In this paper, we propose new hybrid models for code generation by leveraging pre-trained language models BERT, RoBERTa, ELECTRA, and LUKE with the Marian Causal Language Model. Selecting these models based on their strong performance in various natural language processing tasks. We evaluate the performance of these models on two datasets CoNaLa and DJANGO and compare them to existing state-of-the-art models. We aim to investigate the potential of pre-trained transformer language models to revolutionize code generation, offering improved precision and efficiency in navigating complex coding scenarios. Additionally, conducting error analysis and refining the generated code. Our results show that these models, when combined with the Marian Decoder, significantly improve code generation accuracy and efficiency. Notably, the RoBERTaMarian model achieved a maximum BLEU score of 35.74 and an exact match accuracy of 13.8% on CoNaLa, while LUKE-Marian attained a BLEU score of 89.34 and an exact match accuracy of 78.50% on DJANGO. Implementation of this work is available at https://github.com/AhmedSSoliman/Leveraging-Pretrained-Language-Models-for-Code-Generation .
CL-BPUWM: continuous learning with Bayesian parameter updating and weight memory

Authors:
raw_author_name: Yao He
raw_affiliation_string: School of Mechanical Engineering, Guizhou University, Guiyang, 550025, China
raw_author_name: Jing Yang
raw_affiliation_string: School of Mechanical Engineering, Guizhou University, Guiyang, 550025, China
raw_author_name: Shaobo Li
raw_affiliation_string: School of Mechanical Engineering, Guizhou University, Guiyang, 550025, China
raw_author_name: Jianjun Hu
raw_affiliation_string: Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, 29208, USA
raw_author_name: Qing Ji
raw_affiliation_string: School of Mechanical Engineering, Guizhou University, Guiyang, 550025, China

Abstract: Abstract Catastrophic forgetting in neural networks is a common problem, in which neural networks lose information from previous tasks after training on new tasks. Although adopting a regularization method that preferentially retains the parameters important to the previous task to avoid catastrophic forgetting has a positive effect; existing regularization methods cause the gradient to be near zero because the loss is at the local minimum. To solve this problem, we propose a new continuous learning method with Bayesian parameter updating and weight memory (CL-BPUWM). First, a parameter updating method based on the Bayes criterion is proposed to allow the neural network to gradually obtain new knowledge. The diagonal of the Fisher information matrix is then introduced to significantly minimize computation and increase parameter updating efficiency. Second, we suggest calculating the importance weight by observing how changes in each network parameter affect the model prediction output. In the process of model parameter updating, the Fisher information matrix and the sensitivity of the network are used as the quadratic penalty terms of the loss function. Finally, we apply dropout regularization to reduce model overfitting during training and to improve model generalizability. CL-BPUWM performs very well in continuous learning for classification tasks on CIFAR-100 dataset, CIFAR-10 dataset, and MNIST dataset. On CIFAR-100 dataset, it is 0.8%, 1.03% and 0.75% higher than the best performing regularization method (EWC) in three task partitions. On CIFAR-10 dataset, it is 2.25% higher than the regularization method (EWC) and 0.7% higher than the scaled method (GR). It is 0.66% higher than the regularization method (EWC) on the MNIST dataset. When the CL-BPUWM method was combined with the brain-inspired replay model under the CIFAR-100 and CIFAR-10 datasets, the classification accuracy was 2.35% and 5.38% higher than that of the baseline method, BI-R + SI.
Eliminating bias: enhancing children’s book recommendation using a hybrid model of graph convolutional networks and neural matrix factorization

Authors:
raw_author_name: Lijuan Shen
raw_affiliation_string: National Child Development Research Centre, Universiti Pendidikan Sultan Idris, Perak, Malaysia

Abstract: Managing user bias in large-scale user review data is a significant challenge in optimizing children's book recommendation systems. To tackle this issue, this study introduces a novel hybrid model that combines graph convolutional networks (GCN) based on bipartite graphs and neural matrix factorization (NMF). This model aims to enhance the precision and efficiency of children's book recommendations by accurately capturing user biases. In this model, the complex interactions between users and books are modeled as a bipartite graph, with the users' book ratings serving as the weights of the edges. Through GCN and NMF, we can delve into the structure of the graph and the behavioral patterns of users, more accurately identify and address user biases, and predict their future behaviors. Compared to traditional recommendation systems, our hybrid model excels in handling large-scale user review data. Experimental results confirm that our model has significantly improved in terms of recommendation accuracy and scalability, positively contributing to the advancement of children's book recommendation systems.
A proposed reconstruction method of a 3D animation scene based on a fuzzy long and short-term memory algorithm

Authors:
raw_author_name: Ming Zhou
raw_affiliation_string: Zhou Ming Anhui Sanlian College, Hefei, China

Abstract: With the development of computer technology leading to a broad range of virtual technology implementations, the construction of virtual tasks has become highly demanded and has increased rapidly, especially in animation scenes. Constructing three-dimensional (3D) animation characters utilizing properties of actual characters could provide users with immersive experiences. However, a 3D face reconstruction (3DFR) utilizing a single image is a very demanding operation in computer graphics and vision. In addition, limited 3D face data sets reduce the performance improvement of the proposed approaches, causing a lack of robustness. When datasets are large, face recognition, transformation, and animation implementations are relatively practical. However, some reconstruction methods only consider the one-to-one processes without considering the correlations or differences in the input images, resulting in models lacking information related to face identity or being overly sensitive to face pose. A face model composed of a convolutional neural network (CNN) regresses 3D deformable model coefficients for 3DFR and alignment tasks. The manuscript proposes a reconstruction method for 3D animation scenes employing fuzzy LSMT-CNN (FLSMT-CNN). Multiple collected images are employed to reconstruct 3D animation characters. First, the serialized images are processed by the proposed method to extract the features of face parameters and then improve the conventional deformable face modeling (3DFDM). Afterward, the 3DFDM is utilized to reconstruct animation characters, and finally, high-precision reconstructions of 3D faces are achieved. The FLSMT-CNN has enhanced both the precision and strength of the reconstructed 3D animation characters, which provides more opportunities to be applied to other animation scenes.