Reviewerly/exHarmony huggingface dataset:
Utilization of artificial intelligence-assisted histopathological detection in surveillance of oral squamous cell carcinoma staging: A narrative review

Background: Oral squamous cell carcinoma (OSCC) is defined as an oral malignancy with worldwide prevalence of 90%. In 2018, the number of cases observed is 354.864 with 177.384 deaths globally. Early diagnosis for determining OSCC stage due to histopathological examination is required to sustain prognosis and minimize mortality. Determining the stage is mostly done manually and highly dependent on skill and experiences of the pathologist thus having a high tendency of misdiagnosis. Artificial intelligence (AI) is a technology that modifies machines with human-like intelligence thus making them able to solve the tasks. Utilization of AI in analyzing histopathological samples is known to give such a precision analysis then diagnosing the OSCC stage accurately Purpose: This study describes utilization of AI-assisted histopathological detection in determining OSCC staging. Review: Developmental process of OSCC begins with gene damage causing disruption of cell regulation, manifesting in impaired differentiation and proliferation of keratinocytes in the epithelium which is characterized by keratin pearl formation. AI-assisted histopathological detection is able to identify the percentage of keratinization and keratin pearls in histopathological images by convolutional neural network (CNN). CNN is a deep learning architecture specifically designed to recognize two-dimensional visual patterns with minimal preprocessing. CNN works by analyzing input in the form of visual images from histopathological images and producing output as keratinization percentage in related samples then being used to determine the staging of OSCC. Conclusion: AI-assisted histopathological detection may potential to be used in determining OSCC staging.
Research on the Teaching Reform of Engineering Drawing for Electrical Majors

In order to adapt to the training objective of applied talents in higher vocational colleges, this paper embodies the law of knowledge and helps students to build the knowledge structure of engineering drawing courses. This paper discusses a teaching model of engineering drawing course suitable for online and offline blended teaching, which is constantly applied and explored in teaching practice, perfected teaching model and deepened course network resources, so that blended teaching model is constantly improved and promoted in practice, and provides a new idea for teaching reform in colleges and universities.
Research on The Promotion of College Students' Employment by We Media Based on Big Data

Presently, the fissional development of the network and digital technology has brought about profound adjustments in the media landscape and major changes in the ecology of public opinion, and the rapid development and coverage of new media are beyond imagination. Under the new situation, the employment of college graduates has shown new characteristics, and it is particularly important to carry out systematic and professional employment guidance. In the new era, the employment situation of university student in colleges and universities has become more and more severe, the phenomenon of students "graduating is unemployed" is serious, and the pressure on students to find employment and start a business is greater. With the gradual development and maturity of big data and cloud computing technology, the era of we media has arrived, providing a new employment environment for today's university student. Use big data and information technology to assess careers, collect student behavior data, academic data, etc., explore the potential value of information, achieve accurate guidance, and lay the foundation for students' better development. The impact of we media media on the employment of university student In the we media environment, the corresponding strategy of colleges and universities to carry out employment services provides new employment opportunities for university student, but what follows is that the platform construction is not perfect, actively improving the employment environment, and providing students with more professional employment guidance to promote the accurate employment of contemporary university student.
Research on Autopilot Trajectory Planning Based on Deep Learning

Vehicle trajectory is usually used in driving behavior modeling and traffic safety. In order to obtain vehicle tracks in traffic video, an automatic acquisition method based on depth learning is proposed. In this paper, the YOLOV5 detector is used to detect vehicles in the region of interest in continuous frames, and the DEEPSORT tracker is used to track robust and fast vehicles. This tracker can effectively reduce the jump of target ID, improve tracking stability, obtain the real-world coordinates of vehicles through perspective transformation, and use a locally weighted regression algorithm to smooth the track. The experimental results show that the average multi-target tracking accuracy of the method in the expressway monitoring data set is 96.1%, and the average accuracy of trajectory acquisition is more than 93%. The smoothing algorithm can effectively remove the trajectory noise.
Security During Transmission Of Data Using Web Steganography

The enormous improvements in communications and related technologies, such as the Internet of Things (IoT) and its web-based and mobile applications, have changed our way of life. We are also noticing a trend in which internet-connected electronic devices are using technology more frequently and providing customers with services of a higher standard. However, there are several complications and issues that arise when sending and receiving information via the internet. The security, privacy, and preservation of sent information are the most critical of them all. The security of information has grown to be a significant problem with the expansion of data transfer across computer networks. There are numerous ways to safeguard data and prevent unwanted users from accessing it. Steganography and cryptography are two different data hiding and protection techniques. Steganography conceals communications within some other digital media. On the other side, cryptography obscures the message's content and makes it challenging for readers to decipher. To safeguard data, steganography can be combined with cryptographic methods. Combining steganography and cryptography for hidden data transmission is the best suggested method for protecting data transferred over the internet. A data carrier that is an image can be used for the transmission. With regard to capacity, security, and robustness for secure data transmission over an open channel, this combinational methodology will be sufficient. The purpose of this paper is to explain the steganographic security measures used to protect data while it is being transmitted across a network. Key words: Security, Transmission, Data, Web Steganography
Incorporating remote sensing techniques to the DRASTIC index to assess groundwater vulnerability at mining areas: application to Sonora river aquifer, northwestern Mexico

Groundwater metal pollution is a major concern in mining areas. This study proposes a new addition to the DRASTIC method (DRASTIC+Lu) to assess groundwater vulnerability to metal pollution in mining areas by combining remote sensing (to locate metal pollution sources) and the DRASTIC index. The study was performed in a mining area in northwestern Mexico showing current and historical mining activities. The proposed methodology allowed locating known and unknown sources of metal pollution (mining tailings, active/inactive mines, and areas with exposed natural geochemical anomalies). Generally, the addition of the land use (Lu) parameter causes not only a decrease in vulnerability but also highlights very high vulnerable areas and identifies new ones in the vicinity of metal pollution sources. This result is relevant to focus stewardship efforts in very high vulnerable areas. Results allowed to identify the need to implement protection and restauration measures in the Sonora river channel and its vicinity. The proposed method could be implemented in other mining areas around the world –at a low cost– to locate unknown metal pollution sources and clearly identify very high vulnerable areas that play a key role in the protection of groundwater resources.
Survey on Rainfall Prediction using Artificial Neural Network

Abstract: One of the most significant and difficult tasks in the modern world is rainfall forecasting. Generally speaking, climate and rainfall are extremely complex and non-linear phenomena those that demand sophisticated computer modelling and simulation for making an accurate forecast. Asynthetic neural network (ANN) can be used to forecast how such nonlinear systems will behave. Most of the industries have adopted ANN with success in this subject for the past 25 years as researchers. This paper offers a review of the literature on some study methods used by many scholars to use ANN for predicting rainfall. Additionally, the study notes that The ANN approach is more suitable for predicting rain than standard numerical and statistical techniques.
Personal Value Orientation, Expectation Confirmation and Choice Behavior: A Perspective of Sustainable Higher Vocational Development

The sustainable development of higher vocational colleges meets the demand for technical skills talents in the market, and is also a great positive factor for the quality improvement of China's manufacturing industry. As the main force in the technical skill talent market, higher vocational college students are also the most important group in need of attention and support in the education system. Based on the real situation of sustainable higher vocational in China, the SEM equation structure model is used to study the relationship among choice behaviors of the students, the economic value orientation, social value orientation, independent choice ability, expectation and perceived value. The results of this research can put forward some enlightenment and suggestions for the development of higher vocational colleges. The empirical research results are of great practical significance and academic value, which can provide theoretical reference and basis for the future strategic decision-making of higher vocational education and government departments.
Feasibility and efficacy of TouchCare system using application for older adults living alone: a pilot pre-experimental study

Abstract Background With the number of older people living alone continuously rising, health-monitoring systems using information and communication technology (ICT) have been developed to manage their health issues. Life logging and human body communication sensor, types of ICT, have been adapted to manage and monitor health status of the elderly. However, its feasibility and efficacy remain unclear. This study aimed to examine the feasibility of TouchCare system which combined life logging with human body communication technology and its effect on the physical and psychological status of older adults living alone. Methods The TouchCare system, which consisted of a wearable watch, touchpad sensors, TouchCare application, and context-aware artificial intelligence, was developed by DNX Co. Ltd and used by the participants for 5 months. Out of the 111 selected participants, 91 replied to the satisfaction survey, and 22 participated in further investigation regarding their physical and psychological status. Finally, health assessment from 14 participants and sensor data from 13 participants (mean age = 77.4; SD = 3.8) were analyzed to compare their health status and health-related behaviors before and after use of the system. Results Out of the 91 participants who took the survey, 51.6% were satisfied with the system. Nutritional status (pre-intervention (10.6 ± 2.0) vs. post-intervention (11.8 ± 1.9), P = 0.04) and fall efficacy (pre-intervention (89.2 ± 15.3) vs. post-intervention (99.9 ± 0.5), P = 0.001) significantly improved after use of the system. Chronic pain (pre-intervention (4.8 ± 2.5) vs. post-intervention (4.4 ± 3.7), P = 0.78) and depressive symptoms (pre-intervention (5.7 ± 3.9) vs. post-intervention (5.4 ± 3.1), P = 0.60) reduced, while cognitive function (pre-intervention (4.1 ± 1.4) vs. post-intervention (4.6 ± 1.1), P = 0.15) and physical performance related to walking improved (pre-intervention (3.9 ± 0.2) vs. post-intervention (4.0 ± 0), P = 0.35), but were not significant. Behaviors related to physical activity and gait improved after use of the system; touch counts of refrigerator and microwave also increased with a decrease in night touch counts. Conclusions The TouchCare system was acceptable to older people living alone, and it efficiently managed their daily living while promoting their health-related behaviors. Further experimental studies are required to verify the effectiveness of the system, and to develop the system which meet the individualized needs of older people living alone.
Minimal robot to foster well-being: the HIRO project

An increasing number of studies in service research focuses on human-robot interaction. Only a few papers offer preliminary insights on the impact of such an interaction on actors’ well-being. Much more investigation is needed to get light on actors’ physical, psychological, and social well-being. The aim of this study is to understand how robotic solutions enhance service interactions and foster the patient’s well-being, investigating the effects of their use on the patient’s quality of life. The authors adopt an action research approach to promote an experimental project aimed at using the service robot Hiro, a Japanese minimal design service robot, in a daily care center as a research setting settled in Italy. The research process follows three phases. First, we focused on the robot pre-use analysis, i.e., the investigation of how minimal robot works. Semi-structured interviews with technology developers offered preliminary insights (on the features of the HIRO minimal robot) that impact well-being. In the second stage the focus shifts on the formal caregivers. Finally, the authors observe the interaction between HIRO and patients affected by dementia and how the robot can impact physical, psychological, and social well-being. The results show that the introduction of service robots enables patients to reduce their cognitive and socio-emotional decline and mental illness, allowing them to keep the parameters of the state of agitation under constant control and giving them the opportunity to interact with the robot in case of need (psychological wellbeing). It’s the first step to reduce stress, depression and agitation, towards the emotional involvement of the patient in a reactive and conversational interaction (social well-being). The results also show that the introduction of service robots can nudge patients to avoid a sedentary lifestyle and reach the goal of ‘daily physical activity’ more easily (physical well-being). This study contributes to research on the nascent literature on technologies and wellbeing within service research, suggesting an enhanced perspective on minimal design robots. The research offers a deeper understanding of the new dynamics that improve service interaction and impact well-being. Finally, this opens new possibilities for researchers and service providers who design service interaction involving technology.
Grammatical error detection using HPSG grammars: Diagnosing common Mandarin Chinese grammatical errors

Computational Grammars can be adapted to detect ungrammatical sentences, effectively transforming them into error detection (or correction) systems. In this paper we provide a theoretical account of how to adapt implemented HPSG grammars for grammatical error detection. We discuss how a single ungrammatical input can be reconstructed in multiple ways and, in turn, be used to provide specific, high-quality feedback to language learners. We then move on to exemplify this with a few of the most common error classes made by learners of Mandarin Chinese. We conclude with some notes concerning the adaptation and implementation of the methods described here in ZHONG, an open-source HPSG grammar for Mandarin Chinese.
Unit-log-symmetric models: Characterization, statistical properties and its use in analyzing internet access data

We present here a unit-log-symmetric model based on the bivariate log-symmetric distribution. It is a flexible family of distributions over the interval $(0, 1)$. We then discuss its mathematical properties such as stochastic representation, symmetry, modality, moments, quantile function, entropy and maximum likelihood estimators, paying particular attention to the special cases of unit log-normal, unit-log-Student-$t$ and unit-log-Laplace distributions. Finally, some empirical results and practical illustrations are presented.
Prediction of Scene Plausibility

Understanding the 3D world from 2D images involves more than detection and segmentation of the objects within the scene. It also includes the interpretation of the structure and arrangement of the scene elements. Such understanding is often rooted in recognizing the physical world and its limitations, and in prior knowledge as to how similar typical scenes are arranged. In this research we pose a new challenge for neural network (or other) scene understanding algorithms - can they distinguish between plausible and implausible scenes? Plausibility can be defined both in terms of physical properties and in terms of functional and typical arrangements. Hence, we define plausibility as the probability of encountering a given scene in the real physical world. We build a dataset of synthetic images containing both plausible and implausible scenes, and test the success of various vision models in the task of recognizing and understanding plausibility.
RGB-L: Enhancing Indirect Visual SLAM using LiDAR-based Dense Depth Maps

In this paper, we present a novel method for integrating 3D LiDAR depth measurements into the existing ORB-SLAM3 by building upon the RGB-D mode. We propose and compare two methods of depth map generation: conventional computer vision methods, namely an inverse dilation operation, and a supervised deep learning-based approach. We integrate the former directly into the ORB-SLAM3 framework by adding a so-called RGB-L (LiDAR) mode that directly reads LiDAR point clouds. The proposed methods are evaluated on the KITTI Odometry dataset and compared to each other and the standard ORB-SLAM3 stereo method. We demonstrate that, depending on the environment, advantages in trajectory accuracy and robustness can be achieved. Furthermore, we demonstrate that the runtime of the ORB-SLAM3 algorithm can be reduced by more than 40 % compared to the stereo mode. The related code for the ORB-SLAM3 RGB-L mode will be available as open-source software under https://github.com/TUMFTM/ORB SLAM3 RGBL.
Analysis of Utterance Embeddings and Clustering Methods Related to Intent Induction for Task-Oriented Dialogue

The focus of this work is to investigate unsupervised approaches to overcome quintessential challenges in designing task-oriented dialog schema: assigning intent labels to each dialog turn (intent clustering) and generating a set of intents based on the intent clustering methods (intent induction). We postulate there are two salient factors for automatic induction of intents: (1) clustering algorithm for intent labeling and (2) user utterance embedding space. We compare existing off-the-shelf clustering models and embeddings based on DSTC11 evaluation. Our extensive experiments demonstrate that the combined selection of utterance embedding and clustering method in the intent induction task should be carefully considered. We also present that pretrained MiniLM with Agglomerative clustering shows significant improvement in NMI, ARI, F1, accuracy and example coverage in intent induction tasks. The source codes are available at https://github.com/Jeiyoon/dstc11-track2.
Training quantum neural networks using the Quantum Information Bottleneck method

We provide in this paper a concrete method for training a quantum neural network to maximize the relevant information about a property that is transmitted through the network. This is significant because it gives an operationally well founded quantity to optimize when training autoencoders for problems where the inputs and outputs are fully quantum. We provide a rigorous algorithm for computing the value of the quantum information bottleneck quantity within error $\epsilon$ that requires $O(\log^2(1/\epsilon) + 1/\delta^2)$ queries to a purification of the input density operator if its spectrum is supported on $\{0\}~\bigcup ~[\delta,1-\delta]$ for $\delta>0$ and the kernels of the relevant density matrices are disjoint. We further provide algorithms for estimating the derivatives of the QIB function, showing that quantum neural networks can be trained efficiently using the QIB quantity given that the number of gradient steps required is polynomial.
On the Discredibility of Membership Inference Attacks

With the wide-spread application of machine learning models, it has become critical to study the potential data leakage of models trained on sensitive data. Recently, various membership inference (MI) attacks are proposed to determine if a sample was part of the training set or not. The question is whether these attacks can be reliably used in practice. We show that MI models frequently misclassify neighboring nonmember samples of a member sample as members. In other words, they have a high false positive rate on the subpopulations of the exact member samples that they can identify. We then showcase a practical application of MI attacks where this issue has a real-world repercussion. Here, MI attacks are used by an external auditor (investigator) to show to a judge/jury that an auditee unlawfully used sensitive data. Due to the high false positive rate of MI attacks on member's subpopulations, auditee challenges the credibility of the auditor by revealing the performance of the MI attacks on these subpopulations. We argue that current membership inference attacks can identify memorized subpopulations, but they cannot reliably identify which exact sample in the subpopulation was used during the training.
Learning Locally, Communicating Globally: Reinforcement Learning of Multi-robot Task Allocation for Cooperative Transport

We consider task allocation for multi-object transport using a multi-robot system, in which each robot selects one object among multiple objects with different and unknown weights. The existing centralized methods assume the number of robots and tasks to be fixed, which is inapplicable to scenarios that differ from the learning environment. Meanwhile, the existing distributed methods limit the minimum number of robots and tasks to a constant value, making them applicable to various numbers of robots and tasks. However, they cannot transport an object whose weight exceeds the load capacity of robots observing the object. To make it applicable to various numbers of robots and objects with different and unknown weights, we propose a framework using multi-agent reinforcement learning for task allocation. First, we introduce a structured policy model consisting of 1) predesigned dynamic task priorities with global communication and 2) a neural network-based distributed policy model that determines the timing for coordination. The distributed policy builds consensus on the high-priority object under local observations and selects cooperative or independent actions. Then, the policy is optimized by multi-agent reinforcement learning through trial and error. This structured policy of local learning and global communication makes our framework applicable to various numbers of robots and objects with different and unknown weights, as demonstrated by numerical simulations.