Reviewerly/exHarmony huggingface dataset:
Parallel Photonic Reservoir Computing Based on Wavelength Multiplexing

We present a parallel time-delay reservoir computing network based on four wavelengths of a Fabry-Perot laser in experiment. It is proved that the parallel scheme raises the clock rate of the network by four times and improves the performance on the nonlinear channel equalization task.
Implementation of Occluded Pedestrian Detection Method Based on Improved YOLOv5 in ROS Platform

In this paper, we propose an improved YOLOv5 pedestrian detection algorithm to solve the problems of target missing and low accuracy in the ROS platform. By adding a small detection layer of 160*160, the method improves the detection performance of the model and effectively reduces the false detection rate of occluded pedestrians, especially heavily occluded targets. In order to further improve the detection accuracy, it fuses the underlying features of the backbone network to achieve a path aggregation network with multi-feature fusion. Furthermore, the Soft-DIoU-NMS algorithm is used for post-detection processing to reduce the miss rate. Experiments on a public dataset show that mAP of the modified model, compared to the original network, is improved by 1.5%, and the false detection rate is reduced. The method is applied to the ROS platform, and the implementation results show that the proposed method is efficient for detecting occluded pedestrians, in addition, it is able to achieve real-time detection with an average detection speed of 13. 6ms.
Suspect Management

This chapter highlights significant tactical elements of suspect management. It begins with suspect identification. Suspects can be identified through a variety of methods, such as being found in the act of committing a crime, witness/victim interviews, forensic evidence, passive data collection, admissions, information and intelligence, analytical work, proactive overt or covert work, financial investigation, or just simple, prompt actions taken at or near a crime scene. The Senior Investigating Officer (SIO) may need to produce a strategy that outlines various methods in which suspects can be identified. The chapter then looks at the issues surrounding a decision to declare ‘suspect’ status, the making and timing of arrests, detention and interviews, recovering forensic evidence, charging, and bail, including some practical points on ways to conduct effective and proactive suspect hunts.
A Comprehensive Privacy-Preserving Federated Learning Scheme with Secure Authentication and Aggregation for Internet of Medical Things

Data mining, integration, and utilization are the inevitable trend of the Internet of Medical Things (IoMT) in the context of big data. With the increasing demand for data privacy, federated learning has emerged as a new paradigm, which enables distributed joint training of medical data sources without leaving the private domain. However, federated learning is suffering from security threats as the shared local model will reveal original datasets. Privacy leakage is even more fatal in healthcare because medical data contains critically sensitive information. In addition, open wireless channels are susceptible to malicious attacks. To further safeguard the privacy of IoMT, we propose a comprehensive privacy-preserving federated learning scheme with a tactful dropout handling mechanism. The proposed scheme leverages blind masking and certificateless proxy re-encryption (CL-PRE) for secure aggregation, ensuring the confidentiality of the local model and rendering the global model invisible to any parties other than clients. It also provides authentication of uploaded models while protecting identity privacy. Compared with other relevant schemes, our solution has better performance on functional features and efficiency, and is more applicable to IoMT systems with many devices.
An Interactive Approach to Teaching Students of the Digital Generation

From the perspective of preserving traditional forms of teaching and using innovations in the educational process, the educational tracks of students of the digital generation should be determined taking into account their cognitive, communicative and personal characteristics. The use of an interactive approach meets the above requirements. It is one of the innovative approaches to learning and is considered the best educational practice. The article examines the role of an interactive approach to teaching students of the digital generation, highlights their key features that teachers should take into account in the educational process, identifies the priorities in teaching students of the digital generation, taking into account their characteristics. It was concluded that the use of an interactive approach to teaching students of the digital generation enables to maximize the positive effects of their features, increases the effectiveness of training due to the opportunities provided to students in their interaction with each other and with the teacher in the learning environment.
A federated learning and blockchain-based data sharing framework for developing intelligent building design models

Intelligent building design can reduce manual work and streamline the design process by automatically generating design content using artificial neural networks (ANNs). However, it is challenging to collect sufficient drawings to develop a high-performance ANN. Data owners may not be willing to share their drawings with untrusted parties due to privacy considerations. To address these challenges, this paper proposes a novel data sharing framework of confidential building design information to facilitate the development of intelligent auxiliary building design models. The data sharing framework utilises the federated learning technique and blockchain technology to encourage data sharing through fair benefits allocation based on the Shapley value. A case study was conducted to evaluate the effectiveness and feasibility of the proposed framework. The results show that the intersection over union is improved by more than 10%. More benefits are allocated to data owners who provide datasets with higher quality and quantity. Methodologically, the paper should facilitate the effective integration of the fragmented and confidential project data to train building design models and add much value by addressing the data sharing complexity and dynamics in modern construction. Practically, the paper demonstrates a novel way to train auxiliary design models for building designers.
A novel approach to attention mechanism using kernel functions: Kerformer

Artificial Intelligence (AI) is driving advancements across various fields by simulating and enhancing human intelligence. In Natural Language Processing (NLP), transformer models like the Kerformer, a linear transformer based on a kernel approach, have garnered success. However, traditional attention mechanisms in these models have quadratic calculation costs linked to input sequence lengths, hampering efficiency in tasks with extended orders. To tackle this, Kerformer introduces a nonlinear reweighting mechanism, transforming maximum attention into feature-based dot product attention. By exploiting the non-negativity and non-linear weighting traits of softmax computation, separate non-negativity operations for Query ( Q ) and Key ( K ) computations are performed. The inclusion of the SE Block further enhances model performance. Kerformer significantly reduces attention matrix time complexity from O ( N 2 ) to O ( N ), with N representing sequence length. This transformation results in remarkable efficiency and scalability gains, especially for prolonged tasks. Experimental results demonstrate Kerformer's superiority in terms of time and memory consumption, yielding higher average accuracy (83.39%) in NLP and vision tasks. In tasks with long sequences, Kerformer achieves an average accuracy of 58.94% and exhibits superior efficiency and convergence speed in visual tasks. This model thus offers a promising solution to the limitations posed by conventional attention mechanisms in handling lengthy tasks.
Improving Extraction of Japanese Functional Expressions with Discontinuous Types through Part-Of-Speech Tagging

In learning process of Japanese as a second language (JSL), the acquisition of grammatical knowledge is the requisite foundation to learn Japanese well. There are various types of functional expressions in Japanese grammar, which is one of the difficult problems in Japanese language teaching and learning. This paper proposes a simple but effective method to Japanese language processing by Part-Of-Speech (POS) tagging with morphological analysis technology, which handles the automatic extraction of the discontinuous Japanese functional expressions for assisting JSL learners with their study of Japanese grammar. In this task, discontinuous functional expressions in Japanese example sentences will be automatically extracted based on the output results generated by a well-known Japanese morphological analysis tool, named MeCab. To accomplish this task, we manually annotated the POS information for the discontinuous Japanese functional expressions to train a new Conditional Random Fields (CRF) model, which can be applied to Japanese morphological analysis. The evaluation results revealed that our method works effectively on automatic of the Japanese discontinuous functional expressions. The method can also be applied to the development and utilization of new effective functions in computer-assisted language learning system (CALL) for Japanese language teaching and learning.
Methods for Evaluating Conversational Agents’ Communicability, Acceptability and Accessibility Degree

Recently, there has been an increasing interest in language-based interactions with technology. Driven by the success of intelligent personal assistants, the number of conversation-based interactions is growing in several domains. Nevertheless, the literature highlights a lack of models specifically studied to analyse communication blocks and the level of accessibility and acceptability by the user that can characterise Human-Agent dialogue. This paper aims to learn how much an agent can be accessible, how much the communication is understandable, and if it brings to a successful conclusion by extending two known models, the UTAUT2 and CEM. For both models, we defined new indicators to analyse communicability, acceptability degrees and accessibility level using WCAG guidelines. We designed two conversational agents to test our models and conducted preliminary tests. A first agent is used to assist students in following remote digital courses, and a second to help older people in their daily activities and to monitor the indexes of active life defined to control the trend of older people’s physical-cognitive state.
Changing the Culture of Data Management and Sharing: A Report on the NASEM Workshop

The National Academies of Sciences, Engineering, and Medicine (NASEM) hosted the two-day virtual workshop &lt;em&gt;“Changing the Culture of Data Management and Sharing”&lt;/em&gt; on 28th-29th April 2021 to discuss the challenges and opportunities for establishing effective data management and sharing practices and exploring the question of universal availability of scientific data.
GigaScience at Neuroinformatics 2019

The view from the Marie Sklodowska Curie Museum, where Marie lived as a child. This year’s Neuroinformatics 2019 meeting took place in the beautiful and historic city of Warszawa (Warsaw). Warszawa remains a pilgrimage city for scientists, with arguably its most famous resident Marie Sklodowska Curie being the first woman to win a Nobel Prize, and the only person ever to win the Nobel Prize twice in two different scientific fields.
Handwriting Expertise in Criminal Proceedings

The paper is a translation of the paper published in 1897 in German. The author analyses two forensic cases, where experts due to an unprofessional handwriting analysis made wrong conclusions about the author of handwritten documents. Due to these mistakes, innocent persons were accused.
Reliability of Evaluation of Handwriting Signs

Research and application areas related to handwriting, e.g. forensics, certain aspects of medical diagnostics, or handwriting psychology are based on the extraction of handwriting signs. Therefore, the reliability of their evaluation is essential for successful processing. Many authors have been investigating handwriting signs; just to mention some fundamental publications. The usage of digitizers and other registration sensors makes it possible to investigate not only the visible traces on the paper but as well, the invisible strokes, and in-the-air qualities. However, in practice, the experts are dealing mostly with offline documents, written a certain time ago, often years before the expertise. Experts evaluate handwriting signs manually or with the help of computer programs for image processing. However, such programs still cover too few handwriting signs. Therefore, the manual procedure remains the major way for sign registration and analysis, and that is why the question of the reliability of a manual assessment is very important. Yet, explicit research on reliability is out of the focus of modern studies. In the current study, we are trying to fill this gap and present certain results of the corresponding experiment.
Planning Grid for Teaching Skills and Concepts

Subject Philosophy of Music Applied Music Collection: Oxford Scholarship Online
Mutual Query Network for Multi-Modal Product Image Segmentation

Product image segmentation is vital in e-commerce. Most existing methods extract the product image foreground only based on the visual modality, making it difficult to distinguish irrelevant products. As product titles contain abundant appearance information and provide complementary cues for product image segmentation, we propose a mutual query network to segment products based on both visual and linguistic modalities. First, we design a language query vision module to obtain the response of language description in image areas, thus aligning the visual and linguistic representations across modalities. Then, a vision query language module utilizes the correlation between visual and linguistic modalities to filter the product title and effectively suppress the content irrelevant to the vision in the title. To promote the research in this field, we also construct a Multi-Modal Product Segmentation dataset (MMPS), which contains 30,000 images and corresponding titles. The proposed method significantly outperforms the state-of-the-art methods on MMPS.
Approach of Migrating SAAS Applications to Password-Less Authentication

Passwords are the most used authentication method. even after two decades of attempts to replace passwords with other alternatives, it is still dominating over other authentication mechanisms. Passwords are full of weaknesses and vulnerabilities, they are hard to remember, and users tend to reuse passwords for different services. As passwords are full of weaknesses, a more secure alternative is needed. Password-less authentication is a great alternative to passwords, as they replace something the user know with something the user has and it cannot be forgotten. Many password-less authentication method were proposed over the years, including magic link, one time password and cryptographic based. in this work we propose migrating Software as a service application-s to use password-less authentication using Fast Identity Online standards. Migrating to password-less is not easy, and it requires time and skills. in our proposal, we propose a middleware that will implement the password-less authentication and act between the user and the Software as a service application, without any modification to the Software as a service side.
Improving the Homophily of Heterophilic Graphs for Semi-Supervised Node Classification

Graph Neural Networks (GNNs) have been applied to process the widespread graph data, including social networks and web data, etc. However, lots of GNNs can only perform well on homophilic graphs, while losing their superiority when tackling heterophilic graphs. Recent works try to use spectral theory or attention mechanism to design some more complex learning paradigms for heterophilic graphs. In this paper, we instead utilize some explored properties to construct three new graph structures of high homophily to improve the homophily of heterophilic graphs for better representation learning. Along with the original graph structure, totally four graph structures are injected into a Multi-View Graph Fusion Network (MVGFN) to learn a group of more expressive features for the semi-supervised node classification. Ablation experiments show that all three newly-constructed graph structures obtain higher homophily levels. Comparisons among several baselines indicate the superiority of our method on both homophilic and heterophilic graphs.
lab: an R package for generating analysis-ready data from laboratory records

Background Electronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test results are frequently utilized for predicting disease progression. However, processing laboratory test results often poses challenges due to variations in units and formats. In addition, leveraging the temporal information in EHRs can improve outcomes, prognoses, and diagnosis predication. Nevertheless, the irregular frequency of the data in these records necessitates data preprocessing, which can add complexity to time-series analyses. Methods To address these challenges, we developed an open-source R package that facilitates the extraction of temporal information from laboratory records. The proposed lab package generates analysis-ready time series data by segmenting the data into time-series windows and imputing missing values. Moreover, users can map local laboratory codes to the Logical Observation Identifier Names and Codes (LOINC), an international standard. This mapping allows users to incorporate additional information, such as reference ranges and related diseases. Moreover, the reference ranges provided by LOINC enable us to categorize results into normal or abnormal. Finally, the analysis-ready time series data can be further summarized using descriptive statistics and utilized to develop models using machine learning technologies. Results Using the lab package, we analyzed data from MIMIC-III, focusing on newborns with patent ductus arteriosus (PDA). We extracted time-series laboratory records and compared the differences in test results between patients with and without 30-day in-hospital mortality. We then identified significant variations in several laboratory test results 7 days after PDA diagnosis. Leveraging the time series–analysis-ready data, we trained a prediction model with the long short-term memory algorithm, achieving an area under the receiver operating characteristic curve of 0.83 for predicting 30-day in-hospital mortality in model training. These findings demonstrate the lab package’s effectiveness in analyzing disease progression. Conclusions The proposed lab package simplifies and expedites the workflow involved in laboratory records extraction. This tool is particularly valuable in assisting clinical data analysts in overcoming the obstacles associated with heterogeneous and sparse laboratory records.
Pedestrian re-identification based on attention mechanism and Multi-scale feature fusion

Existing pedestrian re-identification models generally have low pedestrian retrieval accuracy when encountering factors such as changes in pedestrian posture and occlusion because the network cannot fully express pedestrian feature information. Therefore, this paper proposes a method to address this problem by combining the attention mechanism with multi-scale feature fusion, and combining the proposed cross-attention module with the ResNet50 backbone network. In this way, the ability of the network to extract strong salient features is significantly improved; at the same time, using the multi-scale feature fusion module to extract multi-scale features from different depths of the network, achieving the complementary advantages between features through feature addition, feature concatenation and feature weight selection. In addition, a feature enhancement method and an efficient pedestrian retrieval strategy are proposed to jointly promote the accuracy of pedestrian retrieval from both the training and testing levels. When tested on the occluded pedestrian recognition datasets Partial-REID and Partial-iLIDS, the accuracy of this method reached 70.1% and 65.6% on the Rank-1 indicator respectively, and 82.2% and 80.5% on the Rank-3 indicator respectively. At the same time, it also achieved high recognition accuracy when tested on the Market1501 dataset and DukeMTMC-reid dataset, reaching 95.9% and 89.9% on the Rank-1 indicator respectively, 89.1% and 80.3% on the mAP indicator respectively, and 67% and 46.2% on the mINP indicator respectively. It can be seen that this method has achieved good results in solving the above problems.
ANALISIS KESULITAN SISWA DALAM MENULIS CERITA PADA MATA PELAJARAN BAHASA INDONESIA KELAS III SDN CIMONE 5 KOTA TANGERANG

Difficulties in writing are difficulties in the process of writing and spelling which involve physical aspects in expressing the results of expressions, thoughts, feelings, and ideas in written form. This study aims to determine the writing difficulties experienced by third grade students at SDN Cimone 5 Tangerang City. This study used a qualitative descriptive method using data collection techniques in the form of writing test tests, interviews with students and teachers, observation and documentation. Checking the validity of the data using triangulation. Data were analyzed through the steps of data collection, data reduction, data presentation and drawing conclusions. Respondents in this research amounted to 6 people, namely 1 teacher and 5 students from grade 3. Based on the results of the research that was too slow in writing, wrong direction in writing letters and numbers, too skewed, inconsistent spacing between letters, dirty writing, imprecise in following horizontal lines, the shape of letters or numbers is illegible, the pencil pressure is not right (too thick or too thin), the size of the writing is too big or too small, and the shape is upside down can affect students' difficulties in writing
CNN-based plastic waste detection system

Plastic waste has become a pressing global concern in recent decades, posing significant challenges to our environment due to its non-biodegradable nature and causing significant pollution and damage to our planet. Recycling plastic waste is one of the most effective solutions to this dilemma, which is why the aim of our project was to create a system that detects plastic waste using a large dataset with labeled data and one of the most famous deep learning neural networks, “Convolutional Neural Networks,” to classify and speed up the waste collection process and provide an easier recycling process. Thanks to our work, we have achieved 97% accuracy.