Reviewerly/exHarmony huggingface dataset:
Interpretable neural networks: principles and applications

In recent years, with the rapid development of deep learning technology, great progress has been made in computer vision, image recognition, pattern recognition, and speech signal processing. However, due to the black-box nature of deep neural networks (DNNs), one cannot explain the parameters in the deep network and why it can perfectly perform the assigned tasks. The interpretability of neural networks has now become a research hotspot in the field of deep learning. It covers a wide range of topics in speech and text signal processing, image processing, differential equation solving, and other fields. There are subtle differences in the definition of interpretability in different fields. This paper divides interpretable neural network (INN) methods into the following two directions: model decomposition neural networks, and semantic INNs. The former mainly constructs an INN by converting the analytical model of a conventional method into different layers of neural networks and combining the interpretability of the conventional model-based method with the powerful learning capability of the neural network. This type of INNs is further classified into different subtypes depending on which type of models they are derived from, i.e., mathematical models, physical models, and other models. The second type is the interpretable network with visual semantic information for user understanding. Its basic idea is to use the visualization of the whole or partial network structure to assign semantic information to the network structure, which further includes convolutional layer output visualization, decision tree extraction, semantic graph, etc. This type of method mainly uses human visual logic to explain the structure of a black-box neural network. So it is a post-network-design method that tries to assign interpretability to a black-box network structure afterward, as opposed to the pre-network-design method of model-based INNs, which designs interpretable network structure beforehand. This paper reviews recent progress in these areas as well as various application scenarios of INNs and discusses existing problems and future development directions.
Black-Box Unsupervised Domain Adaptation for Medical Image Segmentation

Unsupervised Domain Adaptation (UDA) is one of the key technologies to solve the problem of obtaining ground truth labels needed for supervised learning. In general, UDA assumes that information about the source model, such as its architecture and weights, and all samples from the source domains are available when a target domain model is trained. However, this is not a realistic assumption in applications where privacy and white-box attacks are a concern, or where the model is only be accessible through an API. To overcome this limitation, UDA without source model information and source data, called Black-Box Unsupervised Domain Adaptation (BBUDA), has recently been proposed. Here, we propose an improved BBUDA method for medical image segmentation. Our main contribution is the introduction of a mean teacher algorithm during the training of the target domain model. We conduct experiments on datasets containing different types of source-target domain combinations to demonstrate the versatility and robustness of our method. We confirm that our method outperforms the state-of-the-art on all datasets.
Toward Deep Digital Contact Tracing: Opportunities and Challenges

During the COVID-19 pandemic , digital contact tracing using mobile devices has been widely explored, with many proposals from academia and industry highlighting the benefits and challenges. Most approaches use Bluetooth low energy signals to learn and trace close contacts among users. However, tracing only these contacts can mask the risk of virus exposure in scenarios with low detection rates. To address this issue, we propose fostering users to exchange information beyond close contacts, particularly about prior “deep” contacts that may have transmitted the virus. This presents new opportunities for controlling the spread of the virus, but also poses challenges that require further investigation. We provide directions for addressing these challenges based on our recent work developing a technological solution using this approach.
Research on Knowledge Graph Platform of Logistics Industry Based on Big Data

China's logistics industry started late, with the rapid development of the national economy, China's logistics industry maintains a fast growth rate, and the data generated is growing explosively. Data and knowledge are the basis for the deep integration of new-generation information technology and intelligent manufacturing. How to efficiently and accurately analyze multi-source and heterogeneous data in various systems of the logistics industry is a major problem faced by people in the logistics industry. The knowledge graph is a new type of accurate knowledge representation method, which has gradually begun to land in medical, financial, and other industries. For the characteristics of the logistics industry, this paper conducts special research on five aspects, including knowledge acquisition, knowledge processing, knowledge graphing, knowledge application, and knowledge reasoning. It aims to promote the application of the knowledge graph platform in the logistics industry and provide decision-making support for auxiliary event-driven and industry-important events.
Code Switching in The Learning Process at Putra Kaili Permata Bangsa Superior Elementary School

In the learning process at Unggulan Putra PutraKailiPermataBangsa Elementary School, many code-flipping techniques are used. This research tries to define these techniques. Descriptive analysis is the method applied in this study. The speech of the teachers at Putra KailiPermataBangsa Primary School is the data processed. Teachers at Putra KailiPermataBangsa Primary School provided the data for this study. The observation method is the one used to collect the data. Research data analysis is done using the Miles Huberman model. According to the study, teachers and pupils at Unggulan Putra KailiPermataBangsa Elementary School used standard Indonesian and switched between several dialects when speaking. Code swapping can in a variety of forms, including internal code-switching, external code switching, and multiple code switching. There are fifty-four data relating to code-switching. Internal code-switching is eighteen data, external code-switching is thirty-two data, and multi code-switching is 4 data. Code-switching functions to translate, explain, emphasize, answer, praise, ask, order, and test.
Usability Evaluation of an Augmented Reality System for Collaborative Fabrication between Multiple Humans and Industrial Robots

Semi-automated timber fabrication tasks demand the expertise and dexterity of human workers in addition to the use of automated robotic systems. In this paper, we introduce a human-robot collaborative system based on Augmented Reality (AR). To assess our approach, we conducted an exploratory user study on a head-mounted display (HMD) interface for task sharing between humans and an industrial robotic platform (N=16). Instead of screen-based interfaces, HMDs allowed users to receive information in-situ, regardless of their location in the workspace and the need to use their hands to handle tools or carry out tasks. We analyzed the resulting open-ended, qualitative user feedback as well as the quantitative user experience data. From the results, we derived challenges to tackle in future implementations and questions that need to be investigated to improve AR-based HRI in fabrication scenarios. The results also suggest that some aspects of human-robot interaction, like communication and trust, are more prominent when implementing a non-dyadic scenario and dealing with larger robots. The study is intended as a prequel to future work into AR-based collaboration between multiple humans and industrial robots.
FontFusionGAN: Refinement of Handwritten Fonts by Font Fusion

Handwritten fonts possess unique expressive qualities; however, their clarity often suffers because of inconsistent handwriting. This study introduces FontFusionGAN (FFGAN), a novel method that enhances handwritten fonts by mixing them with printed fonts. The proposed approach leverages a generative adversarial network (GAN) to synthesize fonts that combine the desirable features of both handwritten and printed font styles. Training a GAN on a comprehensive dataset of handwritten and printed fonts enables it to produce legible and visually appealing font samples. The methodology was applied to a dataset of handwriting fonts, showing substantial enhancements in the legibility of the original fonts, while retaining their unique aesthetic essence. Unlike the original GAN setting where a single noise vector is used to generate a sample image, we randomly selected two noise vectors, z1 and z2, from a Gaussian distribution to train the generator. Simultaneously, we input a real image into the fusion encoder for exact reconstruction. This technique ensured the learning of style mixing during training. During inference, we provided the encoder with two font images, one handwritten and the other printed font, to obtain their respective latent vectors. Subsequently, the latent vector of the handwritten font image was injected into the first five layers of the generator, whereas the latent vector of the printed font image was injected into the last two layers to obtain a refined handwritten font image. The proposed method has the potential to improve the readability of handwritten fonts, offering benefits across diverse applications, such as document composition, letter writing, and assisting individuals with reading and writing difficulties.
SHARED CONTRIBUTIONS OF COMMON GENETIC VARIANTS TO RISK OF PSYCHOSIS AMONG INDIVIDUALS OF AFRICAN, LATINO, AND EUROPEAN ANCESTRY

This paper presents a novel dimensionality reduction technique based on ELM and SOM: ELM-SOM+. This technique preserves the intrinsic quality of Self-Organizing Map (SOM): it is nonlinear and suitable for big data. It also brings continuity to the projection using two Extreme Learning Machine (ELM) models, the first one to perform the dimensionality reduction and the second one to perform the reconstruction. ELM-SOM+ is tested successfully on nine diverse datasets. Regarding reconstruction error, the new methodology shows considerable improvement over SOM and brings continuity.
Prospects for Functional Address Translation

Address translation fundamentally embodies a translation function that maps from virtual to physical addresses. In current systems, the translation function is encoded by the kernel in an in-memory radix tree structure (the page table hierarchy) which is then interpreted by the hardware (the pagewalker, pagewalk-caches, and TLBs). We consider implementing the translation function itself as reconfigurable hardware-does this make any sense? To study this question, we collected numerous in-situ Linux page tables for a wide range of workloads, including those from HPC, to serve as example translation functions. We then prototyped several potential mechanisms to implement the translation function, including inverted page tables with function-specific perfect hashing, translation functions directly implemented using Espresso-minimized PLAs, translation functions genetically-evolved in a language suitable for FPGA-like synthesis, and translation functions based on recovered/manufactured region (segment/mmap) lookup using multiplexor trees. Each mechanism was then evaluated using the Linux page tables, primarily for space and lookup speed. We report our findings and try to address the question.
The Challenge of Protein Domain Annotation with Supervised Learn-ing Approach: A Systematic Review

The protein domain is still considered one of the most critical evolutionary unit in the cellular, molecular mechanism. Hence, providing the quantitative measurements of the domain contents of proteins would be essential to give insights on the dynamics of the cell’s biochemical machinery. Bioinformatics, as a quantitative science, has successfully provided several approaches to comprehend the domain contents and their dynamics. It was done mainly with supervised learning approach. In eukaryote domain, there is the tendency of transcription factor domain avoidance in the higher organism, and co-occurrence in a single cell has shed light on the complexity of domain functionality. It is widely assumed that protein domain tends to avoid each other as the organism gained more sophisticated molecular features. However, more sample organisms should be provided to obtain better insight on the domain co-occurrence in the cells. This systematic review was conducted by searching literature in Google Scholars and PubMed. To this end, automatic pipelines should be provided by software packages such as DOMOSAIC with the help of the R-based scripting to uphold the statistical significance. The latest updates on this topic are covering annotation on the orphan domain in Drosophila, and architecture plasticity in the eukaryote.
Research on Fine-Grained Sentiment Classification

Aiming at the fine-grained sentiment classification that distinguishes the emotional intensity, the commonly used dataset SST-1 is analyzed in depth. Through the analysis, it is found that the dataset has serious problems such as data imbalance and small overall scale, which seriously restricts the classification effect. In order to solve the related problems, data augmentation method is adopted to realize the optimization of the dataset. The IMDB and other data which are relatively homologous to the original dataset are annotated, and the focus is to expand the categories with fewer numbers. By this way, the problem of data imbalance is effectively alleviated and the original data scale is expanded. Then, based on the Bidirectional Encoder Representations from Transformers (BERT) model, which has good overall performance on natural language processing, the benchmark classification model is built. Through multiple comparison experiments on the original dataset and the enhanced data, the influence of the deficiency of the original dataset on the classification effect is verified. And, it is fully demonstrated that the enhanced data can effectively improve the test results and solve the problem of large differences in performance between different categories well.
Virtual Spatial Displaying of Dynamic Graphic Objects for IoT

The article deals with the hardware and software of the method of displaying graphic objects for IoT. It is shown that modern means of microelectronics allow their spatial display in dynamic mode. In this case, the virtual mapping is really voluminous, it can be viewed from different sides, using such a device for IoT.
SMT Algorithms for Indian Languages - A Case Study of Moses and MT Hub for English-Maithili Language Pair

Around 34 million people worldwide speak Maithili. Due to lack of digital content, this language is considered resource poor in the technology and internet space. A vast majority of Maithili speakers cannot access internet due to unavailability of Maithili content on internet and also due to the fact that there is no English to Maithili Machine Translation (EMMT) system available. Creating such useful resource requires sizeable aligned parallel text corpora, divergence research between the source and target language and suitable Statistical Machine Translation (SMT) algorithms. This paper while developing the required linguistic resource for a statistical EMMT, compares the two popular SMT algorithms - Microsoft Translator Hub (MTHub) and Moses for their suitability for the EMMT system, and documents the experiments carried out on these platforms.
A Lattice-Based Anonymous Distributed E-Cash from Bitcoin

Although Bitcoin was the first widely adopted cryptographic currency system, it provides a limited form of anonymity and privacy. To protect the anonymity and privacy of Bitcoin transactions, many Bitcoin-based cryptocurrency extensions were proposed. However, most of the systems with anonymity and privacy are based on traditional cryptographic algorithms, which may become insecure in the next decades due to the attack of quantum computing. In this paper, we propose a lattice-based distributed e-cash scheme protecting payer’s anonymity, which is built upon the framework of Zerocoin and lattice-based zero-knowledge argument. Firstly, payer who owes a transaction redeems it to a newly-minted coin. Secondly, to pay for the next transaction, he/she collects a set of such coins to hide his owns, which can further hide his/her identity. Thirdly, to prove that the payer has one of the coins and no attempts to double-spend have occurred, we adapt a zero-knowledge argument of membership based on a lattice-based accumulator and a commitment protocol. Finally, the security proof of the scheme are given.
Domain Adaptive Question Answering over Knowledge Base

Domain-specific question answering over knowledge base generates an answer for a natural language question based on a domain-specific knowledge base. But it often faces a lack of domain training resources such as question answer pairs or even questions. To address this issue, we propose a domain adaptive method to construct a domain-specific question answering system using easily accessible open domain questions. Specifically, generalization features are proposed to represent questions, which can categorize questions according to their syntactic forms. The features are adaptive from open domain into domain by terminology transfer. And a fuzzy matching method based on character vector are used to do knowledge base retrieving. Extensive experiments on real datasets demonstrate the effectiveness of the proposed method.
Object-based and multi-frame motion information predict human eye movement patterns during video viewing

Compared to low-level saliency, higher-level information better predicts human eye movement in static images. In the current study, we tested how both types of information predict eye movements while observers view videos. We generated multiple eye movement prediction maps based on low-level saliency features, as well as higher-level information that requires cognition, and therefore cannot be interpreted with only bottom-up processes. We investigated eye movement patterns to both static and dynamic features that contained either lowor higher-level information. We found that higher-level object-based and multi-frame motion information better predict human eye movement patterns than static saliency and two-frame motion information, and higher-level static and dynamic features provide equally good predictions. The results suggest that object-based processes and temporal integration of multiple video frames are essential to guide human eye movements during video viewing.
[The diagnostic screening of acute leukemia using new technologies of automated blood analysis.]

The leukemia is a neoplastic clonal disease of hematopoietic system with primary affection of bone marrow. The modern technologies of automated blood analysis permit to implement a quick primary screening of pathological samples of blood suspicious for presence of blast cells. The application of various analysis techniques (optical, cytochemical, immune phenotypical) with the purpose of detecting blast cells demonstrates their different distribution at the graphics.
Enhancing Mobile VR Immersion: A Multimodal System of Neural Networks Approach to an IMU Gesture Controller

An effect of immersion is a desirable quality in VR interfaces and its improvement has received a lot of attention in recent years. However, enhancing immersion by eliciting feelings of embodiment and presence through multimodal interactions often requires expensive and bulky dedicated tracking systems. This renders these interactions prohibitable for mobile VR platforms. Our work focused on developing a voice and motion gesture controller with tactile feedback to induce physical sensations and arouse the feeling of embodiment and presence in mobile VR platforms. A mobile virtual reality interface was designed to validate our system through a user experiment. The results indicate that the feelings of presence, embodiment and overall immersion in the mobile VR interface were increased by the use of our system.
Comparing Error Frequencies in First Year ESL Students

There has been a considerable amount of research on the role of errors in ESL students’ writing development. Recent studies generally investigated students’ improvement in writing after a relatively short duration of exposure to an English course. The purpose of the current study is to analyse the error frequencies in written discourse made by one group of diploma level students at a Malaysian university throughout their first year of studies. Two writing samples were obtained from each student based on the writing assessments during their first two semesters of English core courses. The samples were keyed in into a corpus database and an analysis of error frequencies was conducted on each sample. By comparing errors made by the same students throughout their first year of study, this error frequency provides one important way to determine if there are any changes occurred in their written discourse during the chosen period of time. Results reported in this study revealed no significant changes in errors made by the same students in their subsequent written discourse. Findings are discussed in relation to the students’ L2 proficiency development and the nature of their English learning experience.
Designing the IT Capstone Course

This talk discusses the preparation to design a newly required capstone course as part of a larger curriculum update in an IT degree program at a large Midwestern university. To ensure a quality experience for students, the course is being developed based on best practices identified through a systematic literature review. A proper review is comprised of discrete activities that ensure a rigorous process to review current evidence related to a particular research question. The review is currently in early stages. Progress in establishing methods and summarizing findings will be discussed.
Sistim Pakar Konseling Mata Pelajaran Pilihan UNBK Menggunakan Metode Forward Chaining

The choice of computer-based national exams is a choice of a student that must be adjusted to his interests and talents, so in this case the selection of majors is very important for the future of a student who will continue his studies to college. But in reality the decisions taken in choosing majors often cause problems, because the Majors taken only follow the choice of friends or on the basis of coercion from parents. Causing the large number of students who feel out of line with expectations or abilities and want to change majors. For this reason, an expert system has been made that can make it easy for students to consult early to determine elective subjects for computer-based national examinations. The method used in making this expert system is the Forward Chaining method to determine conclusions. The process of this application is to receive input in the form of types of problems experienced by students. The result of the application is that it can provide early instructions for subjects that match the talents and interests of students. With the application of the forward chaining method that is applied to the system that is governed by the rule type problem. From the accuracy of 89.29%, the system can be said to be good enough to be implemented