Reviewerly/exHarmony huggingface dataset:
Problems of scientometrics: Maximum cited publication index as a means of solving some of them

The paper presents a critical analysis of scientometrics as an indicator of scientist’s effectiveness. Six points of criticism of the main scientometric indices clearly show that scientometrics cannot be an indicator of such efficiency. In particular, attention should be paid to the problems of accrual, PR, negative citation, citation of non-scientific publications, publication only in trend areas, co-authorship, and article lifetime. An empirical analysis of the effectiveness of scientometrics is conducted and its ineffectiveness is substantiated. It is explained why, despite all the shortcomings of scientometrics, it continues and is likely to be used in the assessment of scientific activity. It is argued that not many scientists in their lives separately make significant discoveries, most of them work in scientific teams and narrow areas of science. On this basis, the ‘maximum cited publication index’ — the MCP index — is introduced. This index is calculated as the number of citations of a maximally cited work divided by the number of its co-authors. It is proved that, on the one hand, the MCP index really better shows the scientist’s contribution to science, and, on the other hand, it will solve some problems of scientometrics, such as the problem of co-authorship and accrual. An empirical verification of the MCP index is given.
Knowledge-Grounded Target Group Language Recognition in Hate Speech

Hate speech comes in different forms depending on the communities targeted, often based on factors like gender, sexuality, race, or religion. Detecting it online is challenging because existing systems are not accounting for the diversity of hate based on the identity of the target and may be biased towards certain groups, leading to inaccurate results. Current language models perform well in identifying target communities, but only provide a probability that a hate speech text contains references to a particular group. This lack of transparency is problematic because these models learn biases from data annotated by individuals who may not be familiar with the target group. To improve hate speech detection, particularly target group identification, we propose a new hybrid approach that incorporates explicit knowledge about the language used by specific identity groups. We leverage a Knowledge Graph (KG) and adapt it, considering an appropriate level of abstraction, to recognise hate speech-language related to gender and sexual orientation. A thorough quantitative and qualitative evaluation demonstrates that our approach is as effective as state-of-the-art language models while adjusting better to domain and data changes. By grounding the task in explicit knowledge, we can better contextualise the results generated by our proposed approach with the language of the groups most frequently impacted by these technologies. Semantic enrichment helps us examine model outcomes and the training data used for hate speech detection systems, and handle ambiguous cases in human annotations more effectively. Overall, infusing semantic knowledge in hate speech detection is crucial for enhancing understanding of model behaviors and addressing biases derived from training data.
Native Execution of GraphQL Queries over RDF Graphs Using Multi-Way Joins

Purpose: The query language GraphQL has gained significant traction in recent years. In particular, it has recently gained the attention of the semantic web and graph database communities and is now often used as a means to query knowledge graphs. Most of the storage solutions that support GraphQL rely on a translation layer to map the said language to another query language that they support natively, for example SPARQL. Methodology: Our main innovation is a multi-way left-join algorithm inspired by worst-case optimal multi-way join algorithms. This novel algorithm enables the native execution of GraphQL queries over RDF knowledge graphs. We evaluate our approach in two settings using the LinGBM benchmark generator. Findings: The experimental results suggest that our solution outperforms the state-of-the-art graph storage solution for GraphQL with respect to both query runtimes and scalability. Value: Our solution is implemented in an open-sourced triple store, and is intended to advance the development of representation-agnostic storage solutions for knowledge graphs.
Pengaruh penggunaan media sosial dan teman sebaya terhadap prestasi belajar mahasiswa dimediasi motivasi

This study aims to determine the effect of the use of social media and peers on learning achievement mediated by motivation in economics students at the Faculty of Teacher Training and Education, Sebelas Maret University. The type of research used is quantitative with a survey method and the sample used is 262 economics students. The validity test used in this study uses Connfirmatory Factor Analysis (CFA) and uses the Cronbach Alpha reliability test. The data analysis technique used in this study is hierarchical regression analysis. Based on the results of the study, it is concluded that 1) there is a positive and significant influence between the variables of social media use on learning motivation, 2) peer variables directly do not influence learning motivation, 3) motivation variables do not influence learning achievement, 4) there is a significant influence between the variables of social media use on learning achievement, 5) there is a significant influence between peer variables on learning achievement.
ISC-TRANSUNET: MEDICAL IMAGE SEGMENTATION NETWORK BASED ON THE INTEGRATION OF SELF-ATTENTION AND CONVOLUTION

In the current medical image segmentation network, the combination of CNN and Transformer has become a mainstream trend. However, the inherent limitations of convolution operation in CNN and insufficient information interaction in Transformer affect the segmentation performance of the network. To solve these problems, an integrated self-attention and convolution medical image segmentation network (ISC-TransUNet) is proposed in this paper. The network consists of encoder, decoder and jump connection. First, the encoder uses a hybrid structure of BoTNet and Transformer to capture more comprehensive image information and reduce additional computing overhead. Then, the decoder uses an upper sampler cascaded by multiple DUpsampling upper blocks to accurately recover the pixel-level prediction. Finally, the feature fusion of encoder and decoder at different resolutions is realized by ResPath jump connection, which reduces the semantic difference between encoder and decoder. Through experiments on the Synapse multi-organ segmentation dataset, compared with the baseline model TransUNet, Dice similarity coefficient of ISC-TransUNet was improved by 1.13%, Hausdorff distance was reduced by 2.38%, and weight was maintained. The experimental results show that the network can effectively segment tissues and organs in medical images, which has important theoretical significance and application value for intelligent clinical diagnosis and treatment.
Extraction and Quantitative Analysis of Core Vocabulary in Maritime English for Vocational Education

For a long time, the focus of maritime English reform has been on undergraduate stu-dents, with limited research on maritime English reform for vocational college students. However, the vocabulary mastery level of vocational college students directly affects their learning quality. This study uses v
The use of soft computing for electronic and mechanical system surveillance and examination

Soft computing has made great strides in recent years, giving cutting-edge methods for observing and managing intricate electronic and mechanical devices. For photovoltaic (PV) systems to maximize energy extraction, Maximum PowerPoint Tracking (MPPT) is essential. However, fluctuating environmental factors and system parameters might have an impact on the effectiveness of MPPT algorithms, resulting in less-than-ideal power generation. So, using soft computing approaches, this research suggests a unique fuzzy logic control and modified mayfly optimization (FLC-MMO) methodology for MPPT monitoring in PV systems. PV system imprecision and nonlinearity are handled by FLC, which offers an adaptable and flexible regulation. Additionally, MMO is used to optimize the FLC's parameters, enhancing its performance and accelerating convergence. MMO was motivated by the foraging behavior of mayflies. The outcomes of the experiments show how successful the suggested strategy is. The suggested MPPT surveillance system produces a greater energy extraction rate as compared to traditional MPPT methods.
Improvement of Modified Social Group Optimization (MSGO) Algorithm for Solving Optimization Problems

Social Group Optimization (SGO) is a class of evolutionary optimization technique which is having many applications recently. There have been few modifications to SGO so far. Modified SGO (MSGO) is a new variant suggested by original developer of SGO. In both SGO and MSGO the individual is chosen based on the better fitness between its past value and current value. However, the change in dimensional values is not been considered when both past and current fitness remains same. In this work, the check is done dimension-wise and the individual having different dimensions is retained even though the fitness remains same between past and current values. This improvement is incorporated in MSGO and simulation experiments are carried out with benchmark functions and comparisons are done with MSGO.
Recognition and Replacement of Handwritten Text into Digitized Text

Handwritten character recognition is one of the emerging areas in the field of machine learning. Various models are already proposed by researchers, to recognize handwritten text and convert it into a plain text/paragraph, they failed to retain positions of the original text. This paper focuses on developing a machine learning model to process an image of handwritten text and convert it into a digitized text, which indeed retains the position. The proposed model in this paper makes use of long short-term memory (LSTM) that is capable of learning long-term dependencies, which forms a basic cell for the bidirectional recurrent neural networks (RNNs) that helps in the classification of various characters given in the input image. To predict and map these classified characters accordingly, the model uses connectionist temporal classification (CTC) that decodes the RNN output matrix, which indeed predicts multiple possible labels for each time-step. This model makes use of word beam search (WBS) which is one of the type under CTC algorithm, WBS is used for decoding the output layer as it yields the most accurate result with least character error rate (CER) 6.66% that is better than the earlier acquired results. This work uses IAM dataset for various training and testing purposes.
Improving Zero-Shot Cross-Lingual Dialogue State Tracking via Contrastive Learning

Recent works in dialogue state tracking (DST) focus on a handful of languages, as collecting large-scale manually annotated data in different languages is expensive. Existing models address this issue by code-switched data augmentation or intermediate fine-tuning of multilingual pre-trained models. However, these models can only perform implicit alignment across languages. In this paper, we propose a novel model named Contrastive Learning for Cross-Lingual DST (CLCL-DST) to enhance zero-shot cross-lingual adaptation. Specifically, we use a self-built bilingual dictionary for lexical substitution to construct multilingual views of the same utterance. Then our approach leverages fine-grained contrastive learning to encourage representations of specific slot tokens in different views to be more similar than negative example pairs. By this means, CLCL-DST aligns similar words across languages into a more refined language-invariant space. In addition, CLCL-DST uses a significance-based keyword extraction approach to select task-related words to build the bilingual dictionary for better cross-lingual positive examples. Experiment results on Multilingual WoZ 2.0 and parallel MultiWoZ 2.1 datasets show that our proposed CLCL-DST outperforms existing state-of-the-art methods by a large margin, demonstrating the effectiveness of CLCL-DST.
Generalization of Neural Networks on Second-Order Hypercomplex Numbers

The vast majority of existing neural networks operate by rules set within the algebra of real numbers. However, as theoretical understanding of the fundamentals of neural networks and their practical applications grow stronger, new problems arise, which require going beyond such algebra. Various tasks come to light when the original data naturally have complex-valued formats. This situation is encouraging researchers to explore whether neural networks based on complex numbers can provide benefits over the ones limited to real numbers. Multiple recent works have been dedicated to developing the architecture and building blocks of complex-valued neural networks. In this paper, we generalize models by considering other types of hypercomplex numbers of the second order: dual and double numbers. We developed basic operators for these algebras, such as convolution, activation functions, and batch normalization, and rebuilt several real-valued networks to use them with these new algebras. We developed a general methodology for dual and double-valued gradient calculations based on Wirtinger derivatives for complex-valued functions. For classical computer vision (CIFAR-10, CIFAR-100, SVHN) and signal processing (G2Net, MusicNet) classification problems, our benchmarks show that the transition to the hypercomplex domain can be helpful in reaching higher values of metrics, compared to the original real-valued models.
Self Question-Answering: Aspect Sentiment Triplet Extraction via a Multi-MRC Framework Based on Rethink Mechanism

The purpose of Aspect Sentiment Triplet Extraction (ASTE) is to extract a triplet, including the target or aspect, its associated sentiment, and related opinion terms that explain the underlying cause of the sentiment. Some recent studies fail to capture the strong interdependence between ATE and OTE, while others fail to effectively introduce the relationship between aspects and opinions into sentiment classification tasks. To solve these problems, we construct a multi-round machine reading comprehension framework based on a rethink mechanism to solve ASTE tasks efficiently. The rethink mechanism allows the framework to model complex relationships between entities, and exclusive classifiers and probability generation algorithms can reduce query conflicts and unilateral drops in probability. Besides, the multi-round structure can fuse explicit semantic information flow between aspect, opinion and sentiment. Extensive experiments show that the proposed model achieves the most advanced effect and can be effectively applied to ASTE tasks.
My Alphabet Application: Alphabet Introduction Learning Media for 1st-grade Elementary School using Augmented Reality Technology

Reading skills can be initiated by familiarizing children with letter recognition first. Students who cannot recognize and differentiate letters will have difficulty reading. Implementing interactive learning media in the learning process can enhance students' motivation, leading to a higher interest in participating in learning activities. Augmented Reality (AR)-based learning media is one approach to creating an interactive learning environment. The research methodology used in this study is the Multimedia Development Life Cycle (MDLC), which consists of six stages: concept, design, material collecting, assembly, testing, and distribution. This research aims to create a better learning atmosphere for alphabet recognition by incorporating interactivity. This is intended to engage students throughout the learning process. Evaluation is conducted using the black box method to validate the application. The evaluation results demonstrate that the application functions according to the developer's plan, making it suitable for elementary school alphabet recognition learning activities.
Practice of “Business Model” Subject as a Part of Design Thinking Education in Faculty of Engineering and Design

This paper introduces “Introduction to Business Models”, a specialized course for third-year students established by the Faculty of Engineering and Design at Kagawa University in 2018. The primary purpose of this class is to improve students’ ability to think from multiple perspectives through understanding the business models of various companies and to become familiar with the Business Model Canvas (BMC) used in the class so that they can use it in the design thinking process. The class consisted of an explanation of the BMC, an analysis of a leading U.S. company using the BMC, an analysis of a company chosen freely by each student, and a plenary presentation of selected submitted materials. Class management was designed to promote reframing and the formation of collective knowledge. This class can be said to be a prototype of a class for learning business models.
All-fiber and Calibration-free Intrinsically Stable Polarization-modulated Units for Measurement-Device-Independent Quantum Key Distribution

There are several groups have implemented polarization encoding measurement-device-independent quantum key distribution (MDI-QKD). However, these groups use manual polarization controller to achieve the initial polarization state for encoders, and some of the encoders cannot fully satisfy the two bases for polarization encoding quantum key distribution. Here, we apply an all-fiber and calibration-free intrinsically stable polarization-modulated unit (APMU), which can maintain stable encoding for several hours without any calibration. A proof-of-principle demonstration of MDI-QKD based on our APMU is implemented with the four-intensity decoy state protocol. The proposed APMU can realize stable MDI-QKD encoding and simplify the operation for Alice and Bob, which makes the polarization encoding MDI-QKD more practical.
Real-time Improper Driving Action Recognition Using TSM Model with Facial Key Points and Temporal Variations in Pixels

Improper driving actions, such as fatigue driving, talking while driving, etc., are the main causes of traffic accidents. Hence, it is pertinent to develop tools that promptly identify dangerous driving actions and alert the driver to reduce the risk. However, few research works on action recognition focus on both facial expression and body movement. This study combines real-time video of driving actions with facial key points and temporal changes in pixel values and develops the Temporal Shift Module (TSM) on the YawDD driver action dataset. By providing the key point features of the driver’s face and the changes in pixel values due to motion, improved accuracy is obtained, in addition to the benefits of real-time action recognition. Experimental results show that the accuracy of our model results is 9.6% higher than that of the original TSM. At the same time, our model also has fewer calculations and parameters and hence has real-time performance. Compared with other models, our model improves the accuracy of driver action recognition and detection, reaches the standard of real-time detection, and has a wider range of applications. Thus, this work has implications for improving driving safety.
Lightweight Deep Learning Model for Hand Gesture Recognition Based on ㎜Wave Radar Point Cloud

This paper introduces a lightweight deep learning model for human-hand-gesture recognition, leveraging point cloud data acquired from a mmWave radar. The proposed 2D projection method can be applied for the preprocessing of input data for lightweight deep learning models by effectively preserving the spatial and coordinate information of each point within the 3D voxel point cloud. In addition, we proposed a 2D-CNN-TCN deep learning model that significantly reduces the number of learnable parameters while maintaining or improving the accuracy of hand-gesture recognition. The mmWave radar sensor module used in this study was IWR6843AoPEVM from Texas Instruments, and a comprehensive dataset consisting of nine distinct hand gestures was collected, with each gesture captured over a duration of 20–25 min, resulting in a total collection time of 190 min. The proposed model was trained and evaluated on a general-purpose PC. The proposed 2D-CNN-TCN model was compared to the 3D-CNN-LSTM model to reflect the 3D voxel input and time-series characteristics. The performance evaluation demonstrated that the performance of the proposed model was 1.3% enhanced with respect to the 3D-CNN-LSTM model, resulting in a recognition accuracy of 95.06% for the proposed model. Moreover, the proposed model achieved a 5.5% reduction in the number of model parameters with respect to the 3D-CNN-LSTM model. Furthermore, the lightweight deep learning model was successfully deployed as an Android application, and the usability of the model was verified through real-time hand-gesture recognition.
Dynamic Anchor Box-based Instance Decoding and Position-aware Instance Association for Online Video Instance Segmentation

Video instance segmentation (VIS) is a vision task that involves simultaneously detecting, classifying, segmenting, and tracking object instances in videos. In this study, we introduce dynamic anchor box and deformable attention for VIS (DAB-D-VIS), a novel transformer-based model for online VIS. To enhance the multilayer transformer-based instance decoding for each video frame, our proposed model uses deformable attention mechanisms that focus on a small set of key sampling points. Additionally, dynamic anchor boxes are employed to explicitly represent the region of candidate instances. These two methods have already been proven to be effective for transformer-based object detection from images. Furthermore, to address the constraints of online VIS, our model incorporates a robust inter-frame instance association method. This method leverages both similarity in the contrastive embedding space and positional difference in the images between two instances. Extensive experiments conducted on the YouTube-VIS benchmark dataset validate the effectiveness of our proposed DAB-D-VIS model.
Diagnosis of Mental Illness Using Deep Learning: A Survey

Deep learning is a robust technique that aims to build systems that can improve automatically based on experience using advanced statistical and probabilistic data. It has led to significant advances in the health sector by collaborating health professionals with data scientists that solved challenges previously not possible. Mental illness deeply impacts the day-to-day living of a person and is found to be very common in society these days. However, it is taken for granted in many cases. To overcome this issue, analytical techniques are implemented on mental health data which has shown broad potential in improving patient outcomes and also, to better understand the existence of psychological conditions in a wider community. With the rapidly growing field of bioinformatics, deep learning plays a pivotal role in detection by enabling speedy and scalable analysis of complex data like images of the brain. While this technique can be employed and worked at the practice for the early diagnosis of a mental illness, the same can be extended for real-time monitoring of the individual and improving the efficiency of treatment. This chapter gives an insight into the involvement of Neural Networks in the medical profession along with the comprehension of bioinformatics. Furthermore, the chapter progresses with the practice for diagnosing mental illness like Alzheimer’s, anxiety, depression, schizophrenia, dementia, etc., eventually discussing how deep learning can be implemented in the detection of mental illness. Finally, the chapter summarizes the implications for future work.