Reviewerly/exHarmony huggingface dataset:
Framing the fallibility of Computer-Aided Detection aids cancer detection

Abstract Computer-Aided Detection (CAD) has been proposed to help operators search for cancers in mammograms. Previous studies have found that although accurate CAD leads to an improvement in cancer detection, inaccurate CAD leads to an increase in both missed cancers and false alarms. This is known as the over-reliance effect. We investigated whether providing framing statements of CAD fallibility could keep the benefits of CAD while reducing over-reliance. In Experiment 1, participants were told about the benefits or costs of CAD, prior to the experiment. Experiment 2 was similar, except that participants were given a stronger warning and instruction set in relation to the costs of CAD. The results showed that although there was no effect of framing in Experiment 1, a stronger message in Experiment 2 led to a reduction in the over-reliance effect. A similar result was found in Experiment 3 where the target had a lower prevalence. The results show that although the presence of CAD can result in over-reliance on the technology, these effects can be mitigated by framing and instruction sets in relation to CAD fallibility.
Application of Lightning Protection Measures for Outdoor Instruments in Nuclear Power Plants

Taking the reactor dome flow monitoring instrument as an example, analysis and comparison is made on the routine lightning protection measures such that lightning protection devices are added in the design of signal acquisition circuit to improve the lightning protection availability of equipment and circuits and reduce the effect on DCS (digital control system). The practical application shows that this approach brings about minor effect on the signal acquisition circuit, features a flexible layout, convenient maintenance, and strong resistance to multiple lightning strikes, and is available for offering reference in the lightning protection of other equipment and apparatus.
PENGARUH PENERAPAN METODE PEMBELAJARAN SAS (STRUKTURAL ANALITIK SITETIK)TERHADAP PENINGKATAN KEMAMPUAN MEMBACA PADA SISWA KELAS I DI SD NEGERI CIKUMBUEUN 3

The background of this research is that there are problems that experience the influence of the application of the SAS method on students' reading abilities at SDN Cikumbueun 3 due to a lack of motivation for students to read and the lack of methods used and there are several other factors that can influence students' interest in learning to read. This study aims to analyze the effect of applying the SAS (Synthetic Structural Analytical) learning method to improving reading skills in first grade students at SD Negeri Cikumbueun 3. The approach used in this research is to use a quantitative approach using an experimental method. The results of this study were carried out using the SAS (Synthetic Structural Analytical) learning method which can be applied in beginning reading to grade I students at SD Negeri Cikumbueun 3. After applying this method, it can be seen that students' reading abilities can be categorized as adequate and almost all students are able to obtain grades a good one is above 70 or 86, 35%. It is also known that based on the calculation of the t-test, the comparison of the results of the pretest and posttest abilities shows that tcount is 17.63 &gt; ttable = 3.819.
Deep Learning based Intelligent Alert System for Visually Impaired People

According to a study of World Health Organization, there are about 39 million people in the world who are completely visually impaired. These individuals often face significant challenges in navigating and managing their daily life. They always need to be dependent on assistance from other people or any assistance system even for a short distance. So, the main objective of this work is to design a feedback system which acknowledges the person about the obstacles with their names. The frame in front of the user is given as the input to the system through a wearable device. Further, it detects the obstacles in that frame and the user is alerted with the name of the obstacle through a headphone or earphone connected. This can be integrated with a smart cane and any assistance system which improves the safety and mobility of the user. This is achieved through deep learning. Accuracy is the main cause for using deep learning. Better results can be attained by deep learning models. The system has shown 96% accuracy. The two main stages of the system, object detection & classification and the text to speech conversion are achieved through a deep learning technique and speech synthesizer respectively.
QDN: A Quadruplet Distributor Network for Temporal Knowledge Graph Completion

Temporal knowledge graph completion (TKGC) is an extension of the traditional static knowledge graph completion (SKGC) by introducing the timestamp. The existing TKGC methods generally translate the original quadruplet to the form of the triplet by integrating the timestamp into the entity/relation, and then use SKGC methods to infer the missing item. However, such an integrating operation largely limits the expressive ability of temporal information and ignores the semantic loss problem due to the fact that entities, relations, and timestamps are located in different spaces. In this article, we propose a novel TKGC method called the quadruplet distributor network (QDN), which independently models the embeddings of entities, relations, and timestamps in their specific spaces to fully capture the semantics and builds the QD to facilitate the information aggregation and distribution among them. Furthermore, the interaction among entities, relations, and timestamps is integrated using a novel quadruplet-specific decoder, which stretches the third-order tensor to the fourth-order to satisfy the TKGC criterion. Equally important, we design a novel temporal regularization that imposes a smoothness constraint on temporal embeddings. Experimental results show that the proposed method outperforms the existing state-of-the-art TKGC methods. The source codes of this article are available at https://github.com/QDN for Temporal Knowledge Graph Completion.git.
Quality Evaluation Model of Automatic Machine Translation Based on Deep Learning Algorithm

The quality evaluation model of automatic machine translation based on deep learning algorithm is a method to evaluate the quality of machine translation by using deep neural network. The main purpose of this method is to measure the accuracy and precision in terms of human-machine translation. The following are some advantages: It can be used for evaluating various kinds of translations, such as English-Chinese or Chinese-English; It can also be used for evaluating other languages with different characteristics; It can be applied to any language pairs, including monolingual and bilingual ones; It will not only provide an accurate measurement but also help us understand how well our system works. At the same time, because the evaluation index itself can be quantified, the model can be applied to the automatic evaluation standard of machine translation system to improve the evaluation accuracy and help continuously improve the translation quality of machine translation.
Nontraditional Deterministic Remote State Preparation Using a Non-Maximally Entangled Channel without Additional Quantum Resources

In this paper, we have reinvestigated probabilistic quantum communication protocols and developed a nontraditional remote state preparation protocol that allows for deterministically transferring information encoded in quantum states using a non-maximally entangled channel. With an auxiliary particle and a simple measurement method, the success probability of preparing a d-dimensional quantum state is increased to 1 without spending additional quantum resources in advance to improve quantum channels, such as entanglement purification. Furthermore, we have designed a feasible experimental scheme to demonstrate the deterministic paradigm of transporting a polarization-encoded photon from one location to another using a generalized entangled state. This approach provides a practical method to address decoherence and environmental noises in actual quantum communication.
Building beginners� self-confidence in speaking at private high school in Makassar

Teaching spoken English has been a critical issue in the EFL context. The researchers are focusing on how to improve students' public speaking abilities. Several studies, however, have focused on how teachers build EFL learners' self-confidence before focusing on their speaking skills. Lack of self-confidence makes it difficult for beginners to speak, and it also makes it difficult for teachers to design activities that foster students' self-confidence. In response to this growing demand, this article provides an empirical account of how scripted role-plays are used as an innovative learning design in a private high school setting with a majority of students who are beginners. This research applied the Classroom Action Research study. The scripted role-play intervention was carried out for eight meetings following an action research protocol. Classroom observations and semi-structured interviews with students and the teacher were used to collect data. Interpretative Phenomenological Analysis (IPA) was used to analyze these data. According to the study's findings, the students had finally gained confidence in public speaking. Being confident allowed students to practice more, which improved their speaking skills. The study's findings have three practical implications. First, increasing opportunities for students to practice speaking should be prioritized in EFL classes. Second, providing scripted role-play for beginner students allows them to easily use the language. Finally, before asking students to improve their speaking skills, teachers should focus on developing their students' self-confidence.
Combining CNNs and Markov-like Models for Facial Landmark Detection with Spatial Consistency Estimates

The accurate localization of facial landmarks is essential for several tasks, including face recognition, head pose estimation, facial region extraction, and emotion detection. Although the number of required landmarks is task-specific, models are typically trained on all available landmarks in the datasets, limiting efficiency. Furthermore, model performance is strongly influenced by scale-dependent local appearance information around landmarks and the global shape information generated by them. To account for this, we propose a lightweight hybrid model for facial landmark detection designed specifically for pupil region extraction. Our design combines a convolutional neural network (CNN) with a Markov random field (MRF)-like process trained on only 17 carefully selected landmarks. The advantage of our model is the ability to run different image scales on the same convolutional layers, resulting in a significant reduction in model size. In addition, we employ an approximation of the MRF that is run on a subset of landmarks to validate the spatial consistency of the generated shape. This validation process is performed against a learned conditional distribution, expressing the location of one landmark relative to its neighbor. Experimental results on popular facial landmark localization datasets such as 300 w, WFLW, and HELEN demonstrate the accuracy of our proposed model. Furthermore, our model achieves state-of-the-art performance on a well-defined robustness metric. In conclusion, the results demonstrate the ability of our lightweight model to filter out spatially inconsistent predictions, even with significantly fewer training landmarks.
Car make and model recognition system using rear-lamp features and convolutional neural networks

Abstract Recognizing cars based on their features is a difficult task. We propose a solution that uses a convolutional neural network (CNN) and image binarization method for car make and model classification. Unlike many previous works in this area, we use a feature extraction method combined with a binarization method. In the first stage of the pre-processing part we normalize and change the size of an image. The image is then used to recognize where the rear-lamps are placed on the image. We extract the region and use the image binarization method. The binarized image is used as input to the CNN network that finds the features of a specific car model. We have tested the combinations of three different neural network architectures and eight binarization methods. The convolutional neural network with parameters of the highest quality metrics value is used to find the characteristics of the rear lamps on the binary image. The convolutional network is tested with four different gradient algorithms. We have tested the method on two data sets which differ in the way the images were taken. Each data set consists of three subsets of the same car, but is scaled to different image dimensions. Compared to related works that are based on CNN, we use rear view images in different position and light exposure. The proposed method gives better results compared to most available methods. It is also less complex, and faster to train compared to other methods. The proposed approach achieves an average accuracy of 93,9% on the first data set and 84,5% on the second set.
Automated Analysis of Communication Strategies of Telegram Channels in the Area of Psychology

Based on automated content analysis, the paper considered the specifics of the semantic-pragmatic organization of communicative strategies of psychological telegram channels with different popularity ratings. With the help of machine learning methods, it was determined which topic in the field of psychology the post in the form of text belongs to, the thematic structure of the content was described, statistical information on publication activity and the average volume of characters used in published posts was analyzed. The leading communication strategies implemented by channels with different ratings have been identified. Groups of communicative tactics that have a direct or indirect impact on the addressee are reconstructed. It is shown that the communication strategies of psychological telegram channels, regardless of their rating, are complemented by typical “network” methods of influencing the audience's opinion to attract traffic.
Application of innovative teaching methods as a condition for forming a secondary language personality

The article highlights innovative teaching methods that contribute to the formation of a secondary language personality. These methods in foreign language lessons combine individual, group, oral, and written forms of work and are designed for the use of all four main types of speech activities. The authors provide practical examples of innovative methods’ application with a description of their structural components. The article defines the requirements for teachers’ preparation for lessons using innovative teaching methods. Particular attention is paid to work on students’ feedback: the impact of each innovative method on students is checked by conversation, survey. The authors consider it expedient while organizing project activities to form a secondary language personality, to adhere to the following directions in pedagogical activity: historical, cultural, linguistic. Language portfolio creation and its managing are effective means that contribute to the formation of a secondary language personality and allow to monitor the increase of knowledge level about the history and culture of the target language country. The authors set the goal to test the effectiveness of innovative teaching methods’ use in the formation of a secondary linguistic personality in foreign language classes. To achieve this goal, the following methods were used: analysis of scientific and pedagogical literature on the research problem, pedagogical experiment, survey, generalization, etc. The study describes the experience of using innovative teaching methods in the practice of foreign language teaching. The effectiveness of innovative teaching methods’ use in the formation of a secondary language personality is confirmed by the research results given in the article.
Research on multi target tracking algorithm of double video based on YOLOv5

Object detection in real scenes has always been a research hotspot, and dual-video object detection is one of the key researches. In this paper, deep learning YOLOv5 algorithm is first proposed to detect pedestrian targets, and then the realtime stitching technology can be used to achieve image fusion, so as to achieve the dual-video target pedestrian tracking algorithm. The experimental results show that the pedestrian target obtains good continuous tracking effect in the fast moving environment.
Bridges Aalto 2022

The annual international Bridges Conference is the preeminent meeting on the connections between mathematics and art, music, architecture, education and culture. Bridges 2022 was held at Aalto University and Helsinki University in Espoo and Helsinki, Finland. This five-day conference included invited and contributed talks, workshops, a juried art exhibition, musical performances, a short film festival, a poetry reading and family day.
Denoising and Reducing Inner Disorder in Point Clouds for Improved 3D Object Detection in Autonomous Driving

In the field of autonomous driving, precise spatial positioning and 3D object detection have become increasingly critical due to advancements in LiDAR technology and its extensive applications. Traditional detection models for RGB images face challenges in handling the intrinsic disorder present in LiDAR point clouds. Although point clouds are typically perceived as irregular and disordered, an implicit order actually exists, owing to laser arrangement and sequential scanning. Therefore, we propose Frustumformer, a novel framework that leverages the inherent order of LiDAR point clouds, reducing disorder and enhancing representation. Our approach consists of a frustum-based method that relies on the results of a 2D image detector, a frustum patch embedding that exploits the new data representation format, and a single-stride transformer network for original resolution feature fusion. By incorporating these components, Frustumformer effectively exploits the intrinsic order of point clouds and models long-range dependencies to further improve performance. Ablation studies verify the efficacy of the single-stride transformer component and the overall model architecture. We conduct experiments on the KITTI dataset, and Frustumformer outperforms existing methods.
Uncovering circadian rhythms in metabolic longitudinal data: A Bayesian latent class modeling approach

Researchers in biology and medicine have increasingly focused on characterizing circadian rhythms and their potential impact on disease. Understanding circadian variation in metabolomics, the study of chemical processes involving metabolites may provide insight into important aspects of biological mechanism. Of scientific importance is developing a statistical rigorous approach for characterizing different types of 24‐hour patterns among high dimensional longitudinal metabolites. We develop a latent class approach to incorporate variation in 24‐hour patterns across metabolites where profiles are modeled with finite mixtures of distinct shape‐invariant circadian curves that themselves incorporate variation in amplitude and phase across metabolites. An efficient Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. When the model was fit separately by individual to the data from a small group of participants, two distinct 24‐hour rhythms were identified, with one being sinusoidal and the other being more complex with multiple peaks. Interestingly, the latent pattern associated with circadian variation (simple sinusoidal curve) had a similar phase across the three participants, while the more complex latent pattern reflecting diurnal variation differed across individual. The results suggested that this modeling framework can be used to separate 24‐hour rhythms into an endogenous circadian and one or more exogenous diurnal patterns in describing human metabolism.
MFBE: Leveraging Multi-field Information of FAQs for Efficient Dense Retrieval

In the domain of question-answering in NLP, the retrieval of Frequently Asked Questions (FAQ) is an important sub-area which is well researched and has been worked upon for many languages. Here, in response to a user query, a retrieval system typically returns the relevant FAQs from a knowledge-base. The efficacy of such a system depends on its ability to establish semantic match between the query and the FAQs in real-time. The task becomes challenging due to the inherent lexical gap between queries and FAQs, lack of sufficient context in FAQ titles, scarcity of labeled data and high retrieval latency. In this work, we propose a bi-encoder-based query-FAQ matching model that leverages multiple combinations of FAQ fields (like, question, answer, and category) both during model training and inference. Our proposed Multi-Field Bi-Encoder (MFBE) model benefits from the additional context resulting from multiple FAQ fields and performs well even with minimal labeled data. We empirically support this claim through experiments on proprietary as well as open-source public datasets in both unsupervised and supervised settings. Our model achieves around 27% and 23% better top-1 accuracy for the FAQ retrieval task on internal and open datasets, respectively over the best performing baseline.
Multiple Frequency Bands Temporal State Representation for Deep Reinforcement Learning

Deep reinforcement learning has achieved significant success in solving sequential decision-making tasks. Excellent models usually require the input of valid state signals during training, which is challenging to encode temporal state features for the deep reinforcement learning model. To address this issue, recent methods attempt to encode multi-step sequential state signals so as to obtain more comprehensive observational information. However, these methods usually have a lower performance on complex continuous control tasks because mapping the state sequence into a low-dimensional embedding causes blurring of the immediate state features. In this paper, we propose a multiple frequency bands temporal state representation learning framework. The temporal state signals are decomposed into discrete state signals of various frequency bands by Discrete Fourier Transform (DFT). Then, feature signals filtered out different high-frequency bands are generated. Meanwhile, the mask generator evaluates the weights of signals of various frequency bands and encodes high-quality representations for agent training. Our intuition is that temporal state representations considering multiple frequency bands have high fidelity and stability. We conduct experiments tasks and verify that our method has obvious advantages over the baseline in complex continuous control tasks such as Walker and Crawler.