Reviewerly/exHarmony huggingface dataset:
High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning

Abstract Background The detection of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer is critical for tyrosine kinase inhibitor therapy. EGFR detection requires tissue samples, which are difficult to obtain in some patients, costing them the opportunity for further treatment. To realize EGFR mutation prediction without molecular detection, we aimed to build a high-accuracy deep learning model with only haematoxylin and eosin (H&amp;E)-stained slides. Methods We collected 326 H&amp;E-stained non-small cell lung cancer slides from Beijing Chest Hospital, China, and used 226 slides (88 with EGFR mutations) for model training. The remaining 100 images (50 with EGFR mutations) were used for testing. We trained a convolutional neural network based on ResNet-50 to classify EGFR mutation status on the slide level. Results The sensitivity and specificity of the model were 76% and 74%, respectively, with an area under the curve of 0.82. When applying the double-threshold approach, 33% of the patients could be predicted by the deep learning model as EGFR positive or negative with a sensitivity and specificity of 100.0% and 87.5%. The remaining 67% of the patients got an uncertain result and will be recommenced to perform further examination. By incorporating adenocarcinoma subtype information, we achieved 100% sensitivity in predicting EGFR mutations in 37.3% of adenocarcinoma patients. Conclusions Our study demonstrates the potential of a deep learning-based EGFR mutation prediction model for rapid and cost-effective pre-screening. It could serve as a high-accuracy complement to current molecular detection methods and provide treatment opportunities for non-small cell lung cancer patients from whom limited samples are available.
Time-bin phase-encoding quantum key distribution using Sagnac-based optics and compatible electronics

In this work, we present a new time-bin phase-encoding quantum key distribution (QKD), where the transmitter utilizes an inherently stable Sagnac-type interferometer, and has comparable electrical requirements to existing polarization or phase encoding schemes. This approach does not require intensity calibration and is insensitive to environmental disturbances, making it both flexible and high-performing. We conducted experiments with a compact QKD system to demonstrate the stability and secure key rate performance of the presented scheme. The results show a typical secure key rate of 6.2 kbps@20 dB and 0.4 kbps@30 dB with channel loss emulated by variable optical attenuators. A continuous test of 120-km fiber spool shows a stable quantum bit error rate of the time-bin basis within 0.4%∼0.6% over a consecutive 9-day period without any adjustment. This intrinsically stable and compatible scheme of time-bin phase encoding is extensively applicable in various QKD experiments, including BB84 and measurement-device-independent QKD.
Camouflaged object detection with counterfactual intervention

Camouflaged object detection (COD) aims to identify camouflaged objects hiding in their surroundings, which is a valuable yet challenging task. The main challenge is that there are ambiguous semantic biases in the camouflaged object datasets, which affect the results of COD. To address this challenge, we design a counterfactual intervention network (CINet) to mitigate the influences of ambiguous semantic biases and obtain accurate COD. Specifically, our CINet consists of three key modules, i.e., texture-aware interaction module (TIM), context-aware fusion module (CFM), and counterfactual intervention module (CIM). The TIM is designed to extract the refined textures for accurate localization, the CFM is proposed to fuse the multi-scale contextual features to enhance the detection performance, and the CIM is presented to learn more effective textures and make unbiased predictions. Unlike most existing COD methods that directly capture contextual features through the final loss function, we develop a counterfactual intervention strategy to learn more effective contextual textures. Extensive experiments on four challenging benchmark datasets demonstrate that our CINet significantly outperforms 31 state-of-the-art methods.
Daily Prediction of PV Power Output Using Particulate Matter Parameter with Artificial Neural Networks

Renewable energy sources play a critical role in meeting the increasing energy demand. Among them, solar energy stands out with the advantages of being environmentally friendly and protecting the ecosystem. However, its variable structure requires predicting the energy to be produced, properly. In this study, the impact of PM10 parameter on the power output prediction of photovoltaic (PV) energy plants was analyzed in a detailed manner. By the developed prediction model based on artificial neural networks (ANNs), lower root mean squared error and mean absolute percentage error were achieved. As a result, PM10 parameter has seemed to be an efficient input for the daily PV power prediction.
Virtual Laboratory Learning Environment: VLLE on Metaverse for University in Thailand

The virtual laboratory learning environment on metaverse, or VLLE on metaverse, is a kind of learning media in the virtual world, which can be accessed through a virtual laboratory in the form of metaverse. To enter VLLE on metaverse, users must simulate themselves in the form of 3D avatar, and then they are able to perform any tasks and interact with any objects or surrounding environments therein, which are all virtual images. The study question of this research focuses on the steps and methods related to the development of media for use in VLLE on metaverse and on what design features are satisfactory and would be well accepted by participants. In this study, the researchers have applied concepts and new teaching innovations that correspond to the current situations and can support the learning formats that can be done anywhere and anytime. This practice is believed to promote self-directed learning experiences and other skills while allowing students to evaluate their activities on their own as well. The research results are in line with the researchers’ expectations: it was found that VLLE on metaverse can be used as a public interaction tool through mobile applications for the university population and the general public outside. This is because new technologies have been developed and employed in the media and the existing technologies in such a way that an environment close to the real learning conditions can be produced, which can create a learning society without borders.
Tableaux for the Logic of Strategically Knowing How

The logic of goal-directed knowing how proposed in [5] extends the standard epistemic logic with an operator of knowing how.The knowing how operator is interpreted as that there exists a strategy such that the agent knows that the strategy can make sure that ϕ.This paper presents a tableau procedure for the multi-agent version of the logic of strategically knowing how and shows the soundness and completeness of this tableau procedure.This paper also shows that the satisfiability problem of the logic can be decided in PSPACE.
What is the potential value of tokens and token engineering for the architecture, engineering, and construction industry? A positional paper

What is the value of tokens for blockchain applications in Architecture, Engineering, and Construction (AEC)?How can token engineering be contextualized in AEC?This positional paper instigates the tackling of these largely unexplored questions.Following a literature review and a visiting of token engineering fundamentals, the paper's position is that tokens can indeed hold potential value for AEC.This value can be direct, utilitarian, security-related, and/or pegged, and reflected in technical and economic terms.For this value to be realized, the token must be systematically embedded in the AEC ecosystem -therefore dependent on sociotechnical parameters in AEC.
A data screening approach to confirming a target mineral is chlorite using <scp>EPMA</scp> and <scp>LA‐ICPMS</scp> data

Abstract Applying machine learning techniques to large datasets of in situ analyses has been proven to be a powerful tool in Earth Sciences. However, problems may arise when dealing with minerals such as chlorite, that exist as a solid solution rather than a single, stoichiometric ideal. It can be difficult to determine whether the variations in major element concentrations are due to compositional difference in the mineral of interest or due to sampling of the surrounding mineral phases in addition to the mineral of interest during the analyses. If the latter, interpretations of the results would be complicated, misled or even spurious. Here we present a method to identify chlorite based on the major and minor element content, from both LA‐ICPMS and EPMA data. Further we present a dataset of 3,317 analyses of chlorite and have shown that 7.4% of these analyses include significant quantities of non‐chlorite material.
Sign Language Recognition using Deep CNN with Normalised Keyframe Extraction and Prediction using LSTM

Sign Language Recognition (SLR) targets interpreting the signs so as to facilitate communication between hearing or speaking disabled people and normal people. This makes communication between normal people and signers effective and seamless. The scarcely available key information regarding the gestures is the key to recognise the signs. To implement continuous sign language gesture recognition, gestures are identified from the video using Deep Convolutional Neural Network. Recurrent Neural Network- Long Short-Term Memory verifies the semantics of the gesture sequence, which eventually will be converted into speech. The problem of constructing meaningful sentences from continuous gestures inspired the proposed system to develop a model based on it. The model is designed to increase the effectiveness of the classification by processing only the principal elements. The keyframes are identified and processed for classification. Validation of sentences can be done O(N). The sentences are converted into voiceover to have elegant communication between impaired and normal people. The model obtained an accuracy of 89.24% while training over Convolutional Neural Network to detect gestures and performed better than other pre-trained models and an accuracy of 89.99% while training over Recurrent Neural Network- Long Short-Term Memory to predict the next word using grammar phrases. This keyframe-to-voice conversion, forming proper sentences, enthrals people to have harmonious communication.
USING PROGRAMMABLE DRONE IN EDUCATIONAL PROJECTS AND COMPETITIONS

The mainstream of educational robotics platforms orbits the various versions of versatile robotics sets and kits, while interesting outliers add new opportunities and extend the possible learning situations. Examples of such are reconfigurable robots, rolling sphere robots, humanoids, swimming, or underwater robots. Another kind within this category are flying drones. While remotely controlled drones were a very attractive target for hobby model makers for quite a long time already, they were seldom used in educational scenarios as robots that are programmed by children to perform various simple tasks. A milestone was reached with the introduction of the educational drone Tello, which can be programmed even in Scratch, or some general-purpose languages such as Node.js or Python. The programs can even have access to the robot sensors that are used by the underlying layers of the controller. In addition, they have the option to acquire images from the drone camera and perform actions based on processing the frames applying computer vision algorithms. We have been using this drone in an educational robotics competition for three years without camera, and after our students have developed several successful projects that utilized a camera, we prepared a new competition challenge that requires the use of the camera. In the article, we summarize related efforts and our experiences with educational drones, and their use in the student projects and competition.
The Data Carving - The Art of Retrieving Deleted Data as Evidence

This paper proposes an approach to extract hidden information as sensitive data can be hidden by the criminal in free space or slack space. But there might be cases when there exists no file system metadata information for file recovery. For this purpose, Data Carving techniques are used by forensic examiners. If a file is carved in a forensically sound manner, it is then acceptable in a court of law. Many automated tools exist to carve data out of a hard drive. In this paper, we looked at how to carve data in an old-fashioned way followed by carving data using tools.
Faster RCNN for multi-class Foreign Objects detection of Transmission Lines

In the long-term operation of power grids, foreign objects on transmission lines can affect the safe and stable operation of transmission lines and even lead to large-scale power outages, which bring incalculable losses to social and economic development. In this paper, based on the characteristics of the acquired transmission line images, we construct a transmission line foreign object dataset, improve the loss calculation and network structure of the basic Faster RCNN based on the RCNN network model target detection method, and detect foreign objects on transmission lines, such as plastic film, kites, bird nests, ice, etc., by training the model. The final experimental results show that compared with the traditional object recognition method, the improved Faster R-CNN not only overcomes the instability of manually extracted features, but also has the ability to resist complex environmental interference and improves the accuracy of foreign object detection on transmission lines.
Augmented Reality-Based Learning: The Efficacy on Learner‘s Motivation and Reflective Thinking

This paper describes a new experiment in the context of the Palestinian higher education based on Augmented Reality (AR) technology. The main objective is to investigate the efficacy of AR-based learning on motivation and reflective thinking, which are important measures of students’ learning and achievement. The experiment was carried out on a sample of 24 students enrolled in digital communication course in their third and fourth years at telecommunication engineering department of An Najah National University. The sample was selected using purposive sampling method. Except for confidence dimension, results indicate a positive effect of integrating AR technology in teaching and learning on all dimensions of motivation (attention, relevance, satisfaction, and volition) between experiment and control groups. Similarly, AR-based learning has a positive effect on all dimensions of the reflective thinking scale between the experiment and control groups except for the reflection dimension. Furthermore, results indicate no significant differences between the experiment and control groups due to gender in favor of the experiment group. In contrast, there were significant differences between pre-test and post-test on motivation and reflective thinking scales and their dimensions in favor of the post-test. However, the research was limited by the small sample size and the novelty of the AR technology, which requires the instructors and students to be familiar with this emerging technology.
Pemanfaatan Media Pembelajaran Aplikasi Quizizz dalam Meningkatkan Motivasi dan Hasil Belajar Siswa SMK Migas Bumi Melayu Riau

Education is one aspect that is very influential in the quality of human resources in a country. Improving the quality of education will improve the quality of human resources in the country. Success in carrying out learning activities can be seen from the results achieved by students. One of the efforts made to see the success of learning activities is to use learning methods that can attract the attention of students. Learning methods that utilize current technological developments can be used to increase student motivation and learning outcomes, one of the methods that can be used is by using the Quizizz application as a learning medium that can attract students' attention. Quizizz is an application that can be used as a medium for learning activities. This application combines education, games and technology. The purpose of this study is to describe how the use of the Quizizz application affects student motivation and learning outcomes. This study uses observation and descriptive methods. The approach used is a qualitative approach with descriptive data. Descriptive data, namely data collected from research results using words. The results of research using Quizizz are very effective in the process of learning activities at SMK MIGAS Bumi Melayu Riau.
No-reference shadow detection quality assessment via reference learning and multi-mode exploring

Shadow detection is a common computer vision task that identifies the location of shadows. It has been used as a key primitive for many high-level tasks such as object segmentation and video tracking. Due to the complexity and variety of shadows, it is challenging to design a one-size-fits-all shadow detection algorithm in real-world scenarios. Instead, it is often necessary in practice to pick the best shadow detection algorithm for a particular vision application. While shadow detection algorithms can be assessed by comparing the detected shadow with the ground truth, such ground truth is often unavailable in real-world vision systems consistently generating new data. In this paper, we propose a no-reference shadow detection quality assessment network (NSDQA-Net) to rank shadow detection algorithms without needing the ground truth. For a given image, the proposed assessment network first learns a reference map that compensates the false positives and false negatives of multiple input shadow detection algorithms. The reference map, as well as the original image and the input shadow detection results, are then used to regress the quality scores of the shadow detection methods. Our framework supports the assessment of one, two, or multiple algorithms in a single run, making it easier for deployment in practical scenarios. Experiments results show that the proposed network achieves optimal accuracy in no-reference shadow detection assessment. Relying on it for adaptive algorithm selection also improves the performance of shadow detection using a fixed algorithm on benchmark datasets.
Platform regulation and “overblocking” – The NetzDG discourse in Germany

Abstract This paper analyzes the internet policy discourse regarding the German Network Enforcement Act (NetzDG) in different media settings. We examine the conversation about this highly controversial anti-hate speech law on IT blogs, websites, and in daily German newspapers. We compare the positions brought forward in these different media environments concerning one of the most important topics within the discussion about the NetzDG, specifically the question of whether or not the law will result in censorship, limiting users’ freedom of expression. We employ pretrained transformer-based language models to detect and quantify recurring arguments in the debate.
Deep Learning-assisted Diagnosis of Breast Lesions on US Images: A Multivendor, Multicenter Study

To evaluate the diagnostic performance of a deep learning (DL) model for breast US across four hospitals and assess its value to readers with different levels of experience.In this retrospective study, a dual attention-based convolutional neural network was built and validated to discriminate malignant tumors from benign tumors by using B-mode and color Doppler US images (n = 45 909, March 2011-August 2018), acquired with 42 types of US machines, of 9895 pathologic analysis-confirmed breast lesions in 8797 patients (27 men and 8770 women; mean age, 47 years ± 12 [SD]). With and without assistance from the DL model, three novice readers with less than 5 years of US experience and two experienced readers with 8 and 18 years of US experience, respectively, interpreted 1024 randomly selected lesions. Differences in the areas under the receiver operating characteristic curves (AUCs) were tested using the DeLong test.The DL model using both B-mode and color Doppler US images demonstrated expert-level performance at the lesion level, with an AUC of 0.94 (95% CI: 0.92, 0.95) for the internal set. In external datasets, the AUCs were 0.92 (95% CI: 0.90, 0.94) for hospital 1, 0.91 (95% CI: 0.89, 0.94) for hospital 2, and 0.96 (95% CI: 0.94, 0.98) for hospital 3. DL assistance led to improved AUCs (P < .001) for one experienced and three novice radiologists and improved interobserver agreement. The average false-positive rate was reduced by 7.6% (P = .08).The DL model may help radiologists, especially novice readers, improve accuracy and interobserver agreement of breast tumor diagnosis using US.Keywords: Ultrasound, Breast, Diagnosis, Breast Cancer, Deep Learning, Ultrasonography Supplemental material is available for this article. © RSNA, 2023.
The Effectiveness of Computer-Assisted Language Learning (CALL) by Smartphones to Increase English Proficiency of Papuan EFL Students

This study aims to find out the effectiveness of Computer-Assisted Language Learning (CALL) by smartphones to increase English proficiency of Papuan EFL students. The mixed method was implemented specifically related to the explanatory sequential design (Quantitative-Qualitative) where the research design comprised two interactive sequential stages. The first stage was carried out by collecting and analyzing quantitative data using the pre-experimental method to find out the difference before and after being given treatment. The samples were determined using purposive sampling where 30 Papuan EFL students were selected, and data were analyzed by using SPSS. The second stage was carried out by collecting and analyzing qualitative data using structured interviews. The samples were determined using purposive sampling also, and 7 Papuan EFL students were considered representative to obtain data. The results show that Computer-Assisted Language Learning (CALL) by smartphones is effective to increase English proficiency of Papuan EFL students by a significant increase of 38.70%. The effectiveness of this learning cannot be separated from the aspects of efficiency, flexibility, and accessibility supported by excellence in terms of visualization and auditory systems. Moreover, some factors that certainly become constraints of Computer-Assisted Language Learning (CALL) including internet networks, small screens, notifications and advertisements, lack of proficiency towards applications, unsupported smartphones, and battery power. By solving all these constraints, CALL can be more effective to be applied in teaching and learning. The significant implication provides better change in the world of education through learning strategies in utilizing technology especially certain context and conditions in Papua.
CoopHance: Cooperative Enhancement for Robustness of Deep Learning Systems

Adversarial attacks have been a threat to Deep Learning (DL) systems to be reckoned with. By adding human-imperceptible perturbation to benign inputs, adversarial attacks can cause the incorrect behavior of DL systems. Considering the popularity of DL systems in the industry, it is critical and urgent for developers to enhance the robustness of DL systems against adversarial attacks.