Reviewerly/exHarmony huggingface dataset:
Few-shot Domain-Adaptative Visually-fused Event Detection from Text

Incorporating auxiliary modalities such as images into event detection models has attracted increasing interest over the last few years. The complexity of natural language in describing situations has motivated researchers to leverage the related visual context to improve event detection performance. However, current approaches in this area suffer from data scarcity, where a large amount of labelled text-image pairs are required for model training. Furthermore, limited access to the visual context at inference time negatively impacts the performance of such models, which makes them practically ineffective in real-world scenarios. In this paper, we present a novel domain-adaptive visually-fused event detection approach that can be trained on a few labelled image-text paired data points. Specifically, we introduce a visual imaginator method that synthesises images from text in the absence of visual context. Moreover, the imaginator can be customised to a specific domain. In doing so, our model can leverage the capabilities of pre-trained vision-language models and can be trained in a few-shot setting. This also allows for effective inference where only single-modality data (i.e. text) is available. The experimental evaluation on the benchmark M2E2 dataset shows that our model outperforms existing state-of-the-art models, by up to 11 points.
PENGARUH MODEL ROLE PLAYING TERHADAP KETERAMPILAN MEMBACA BAHASA INDONESIA (PENELITIAN QUASI EXPERIMENT DI KELAS III SDN 020 LENGKONG BESAR KOTA BANDUNG)

The study aims to determine whether there is an influence on the use of Role Playing models in reading skills Indonesian elementary school and differences in students' reading skills before and after the Role Playing model is given. The subjects of this study were class III-A students as an experimental class and class III-B as a control class at SDN 020 Lengkong Besar Bandung. Types of quasi-experimental research with data collection techniques are pretes, posttes and non-test techniques. The results showed: (i) There is an influence on the reading skills of students, which can be seen based on the average data of posttes results which show higher scores than the average pretes scores, with an average posttes score of 83.33 while the average pretes score is 57.08. The results showed: (i) There is an influence on the reading skills of students, which can be seen based on the average data of posttes results which show higher scores than the average pretes scores, with an average posttes score of 83.33 while the average pretes score is 57.08. As the end of the study, the advice that can be given to school principals is expected to make these results as material for learning evaluation. Teachers are expected to be able to choose and develop appropriate and varied learning models and methods. For students, it is expected to be more active in learning. For further researchers, it is expected to be able to conduct further research by developing other aspects in it that affect the reading skills of students.
Opportunities and challenges of explainable artificial intelligence in medicine

Artificial intelligence (AI) has been successful in a wide variety of domains. However, the opacity of AI makes it hard for users to understand why a certain decision has been reached by the model, which can decrease user trust. Explainable AI, which addresses the implementation of transparency and interpretation of black box models, is especially crucial in the medical domain. In addition to mapping explainability to causability to deliver understandable explanations to users, we argue that the purpose and the presentation style of the explanation should depend on the stakeholder. In this chapter, we discuss the explanatory requirements of physicians, patients, and developers in the clinical flow. We also propose an explainable medical knowledge base diagnosis system to facilitate further development. To protect patient privacy, we present an innovative framework of explainable federated learning. Finally, we briefly mention the limitations and future directions of medical AI.
HUBUNGAN ANTARA KEPERCAYAAN DIRI SISWA DENGAN KETERAMPILAN BERBICARA BAHASA INGGRIS SISWA KELAS V SDN PERUMNAS 1 KOTA TANGERANG

The purpose of this study is to answer the problem of how the relationship between self-confidence and the ability to speak English in elementary school students. This research took place in class V SDN Perumnas 1 Tangerang City. This study used a quantitative approach using a survey research method used through the SPSS statistical test. The population in this study were all fifth grade students at SDN Perumnas 1 Kota Tangerang, totaling 45 students. The sample used was 30 students with the sampling technique used was Simple Random Sampling. The results of this study indicate that there is a relationship between students' self-confidence and their ability to speak English. The results of statistical analysis using the product moment correlation formula show that the value of rcount is equal to 0.478 and rtable at a significant level of 0.05 is 0.301, so rcount&lt;rtable. This shows that students' self-confidence is one of the components that determines the mastery of speaking English. &#x0D; Keywords: Confidence, Speaking Skills, English.
UNISOUND System for VoxCeleb Speaker Recognition Challenge 2023

This report describes the UNISOUND submission for Track1 and Track2 of VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC 2023). We submit the same system on Track 1 and Track 2, which is trained with only VoxCeleb2-dev. Large-scale ResNet and RepVGG architectures are developed for the challenge. We propose a consistency-aware score calibration method, which leverages the stability of audio voiceprints in similarity score by a Consistency Measure Factor (CMF). CMF brings a huge performance boost in this challenge. Our final system is a fusion of six models and achieves the first place in Track 1 and second place in Track 2 of VoxSRC 2023. The minDCF of our submission is 0.0855 and the EER is 1.5880%.
CHALLENGES OF TEACHERS TO INTEGRATE ESD DESIGN ACTIVITIES IN TECHNOLOGY EDUCATION IN JAPANESE PUBLIC JUNIOR HIGH SCHOOLS

The Japanese national syllabus has put ESD (Education for Sustainable Development) into the latest TE (Technology Education) curriculum guideline. Although many TE teachers are willing to implement ESD, few have done so. One of the reasons is that many do not know how ESD can be implemented in TE. ESD is integrated into TE curricula in some countries through product design-related activities, such as Europe, North America, and Australia. This study refers to such product design-related activities as ESD design activities. ESD design activities require students to find, think and solve the issues related to sustainability through the design thinking process. However, in Japan, TE as a form of general education is mainly conducted in junior high school, focusing on teaching technological knowledge and skills, with almost no elements of design learning activities. The study aims to clarify the challenges TE teachers faced in Japanese public junior high schools when implementing ESD design activities in TE. The study will first clarify the current situation of ESD design activities in TE. Next, the study will identify the barriers TE teachers faced that prevent them from conducting ESD design activities. The current study employed a qualitative research approach, using 4 public junior high schools in Nara as case studies. The reasons are 1) selected schools are typical public junior high schools: 2) teachers who are willing to implement ESD design activities. In this case study, quantitative and qualitative data are collected and used to analyze each case. The main participants of this study are four TE teachers who are teaching in the target schools. Each target school has only one TE teacher. According to the findings, TE teachers are not doing any ESD-related design activities in lessons. The main barriers faced by teachers are 1) limited lesson time: 2) high teaching workload, and 3) low understanding of ESD and the design process. Several suggestions can be proposed in this study to counter the barriers faced by teachers. Firstly, ESD design activities do not need to be implemented as new learning content. ESD can be integrated into existing problem-solving activities by setting the problem tasks to evolve around sustainability issues. Integrating sustainability issues in existing activities may not require additional lesson time and will help to manage teachers’ workload. Secondly, the teachers may also redesign the curriculum to integrate several teaching objectives into a series of sustainability-related design projects. More importantly, teachers need to be trained to have a clear and accurate understanding of ESD and the design process, to facilitate ESD design activities.
Experimental Evaluation of Delayed-Based Detectors Against Power-off Attack

Embedded systems are vulnerable to significant security threats from Fault Injection Attacks (FIAs), which allow attackers to gain access to confidential information. While various attack detectors have been proposed in the literature to detect different types of FIAs, these detectors themselves are susceptible to such attacks and can be compromised. Hence, the robustness of these detectors is critical in maintaining the security of embedded systems. The focus of this study is to evaluate the robustness of digital circuits and delay-based digital detectors against a new type of FIA called Power-Off Attack (POA). POA occurs when the power to the chip is turned off, and the detectors are not active. Following a POA attack, the circuit or its detectors may not function properly when the power is turned back on, which can allow other attacks to be applied without being detected if the detectors are less sensitive. This study implements two detectors on Xilinx Artix-7 FPGAs and examines the impact of heating cycles on detector characteristics when the FPGA is in various states, including power-off, power-on, and inactive states (such as clock-freezing mode). Our experiments reveal that heating cycles in power-off mode can alter the FPGA component delays and the accuracy of its detectors, which highlights the vulnerability of these systems to POA and potential issues for embedded system security.
On Support for Dyslexic Students in Senior-High School and Higher Education

It is very difficult to provide dyslexic students in senior-high school or higher education with all necessary textbooks in ordinary accessible format such as multimedia DAISY. Here, a new approach to provide them with a new type of accessible textbooks named “Fixed-Layout DAISY” is shown. In it, the whole page is treated as a multi-layer picture, the front layer of which has the same form as the original PDF. A DAISY (EPUB3) player can read out any texts together with highlighting them. It does not have the reflow function. The page layout is always kept as same as the original. It does not have information either in which order texts on the page should be read out, and readers need to click a text block on a page where they want to read. Dyslexic people can see and click a place where they want to read, and obviously, Fixed-Layout DAISY should work for them. Fixed-Layout DAISY can be produced almost automatically from an original “e-born PDF” by making use of our OCR system, and it should be very helpful for the dyslexic students to get accessible version of their textbooks.
Business Scenario Driven Reinforcement Learning Testing Method

Reinforcement learning has been successfully applied in software testing, but the existing testing methods cannot perform effective testing according to the characteristics of applications, and using outdated interactive experience during training, resulting in inefficient testing. In this paper, we propose BSDRTesting. Firstly, the demonstration experience of human users is collected according to the functional scenarios and business logic of each application, and combining reinforcement learning and imitation learning to maximize rewards while imitating user behavior, experience replay aims to sample experiences from the agent's self-exploration and expert demonstrations to improve sampling efficiency. At the same time, according to the input rules, the black-box testing method is used to fully test the input events, and finally an experience filtering mechanism is proposed, and the reward value and TD-Error are used as the basis for priority sampling. The experimental results on 10 open source applications show BSDRTesting has achieved significant improvements in code coverage and branch coverage compared with existing methods.
Multipartite Entanglement Distribution Using Separable States

Entanglement distribution among the nodes of a quantum network is possible without having to send any entanglement. We extend this phenomenon to a multipartite setting, using one separable carrier to entangle multiple qubits. It is shown that postselective measurements on the carrier at the end of the protocol can be used to enhance the entanglement produced. We consider the impact of sending the carrier through noisy channels. An experimental proposal is put forward to demonstrate how this could be realised using photons.
BALoo: First and Efficient Countermeasure Dedicated to Persistent Fault Attacks

Persistent fault analysis is a novel and efficient cryptanalysis method. The persistent fault attacks take advantage of a persistent fault injected in a non-volatile memory, then present on the device until the reboot of the device. Contrary to classical physical fault injection, where differential analysis can be performed, persistent fault analysis requires new analyses and dedicated countermeasures. Persistent fault analysis requires a persistent fault injected in the S-box such that the bijective characteristic of the permutation function is not present anymore. In particular, the analysis will use the non-uniform distribution of the S-box values: when one of the possible S-box values never appears and one of the possible S-box values appears twice. In this paper, we present the first dedicated protection to prevent persistent fault analysis. This countermeasure, called BALoo for Bijection Assert with Loops, checks the property of bijectivity of the S-box. We show that this countermeasure has a 100% fault coverage for the persistent fault analysis, with a very small software overhead (memory overhead) and reasonable hardware overhead (logical resources, memory and performance). To evaluate the overhead of BALoo, we provide experimental results obtained with the software and the hardware (FPGA) implementations of an AES-128.
Crowd Counting via Joint SASNet and a Guided Batch Normalization Network

Recent studies on crowd counting have achieved promising results by using convolutional neural network (CNN) architectures. However, due to the large variation in scene distribution in real-world crowd datasets, it remains a challenge to achieve high performance using standard CNN methods. Such methods often suffer from performance drops. To address this challenge, this paper proposes a new crowd-counting approach that combines three state-of-the-art methods: Guided-Batch-Normalization, which adapts the model using unseen dataset normalization parameters; the Scale Adaptive Selection Network, which uses a multi-level network to obtain variation feature representations; and Distribution-Matching-Count, which uses a new loss function between prediction and ground truth maps. Combining these methods results in improved performance. Extensive experiments across multiple datasets have demonstrated that the proposed approach outperforms state-of-the-art methods.
On the Opportunities and Risks of Large Models for the Field of Medical and Health Care: Keynote Address

A Large Model is any model that is trained on broad data that can be fine-tuned to a wide range of downstream tasks; current examples include SAM, GPT-3, GPT-4, ChatDoctor, etc.. From a technological point of view, large models are not new, they are based on deep neural networks and self-supervised learning, both of which have existed for decades. However, the sheer scale and scope of large models from the last few years have stretched our imagination of what is possible; for example, GPT-3 has 175 billion parameters and can be adapted via natural language prompts to do a passable job on a wide range of tasks despite not being trained explicitly to do many of those tasks. Large models acquire linguistic and visual capabilities to perform reasoning and search, and interact with humans. The capabilities of large models indicate that they have the potential to transform various sectors and industries, extending the role AI plays in society. At the same time, existing large models have the potential to accentuate harms, and their characteristics are in general poorly understood. In this talk, She will first overviews the emerging paradigms for building artificial intelligence (AI) systems based on a general class of models which we term large models, in the field of medical and health care. In addition, She will discuss the opportunities and challenges for the application of large models in this field.
Deepfake Detection via Combining Channel and Spatial Attention

Today, as the widespread use of deepfake technologies weakens the credibility of digital media content, deepfake detection of digital content has become an important issue. Detection of fake content is critical in order to prevent the risk of disinformation that may occur with the rapid spread of manipulated content produced with this technology over the internet. This study proposes a neural network that uses channel and spatial attention mechanisms for the detection of deepfake images. This proposed network is trained with a common dataset by combining DeepfakeTIMIT and VidTIMIT datasets. Compared with models such as InceptionV3, ResNet50 and VGG19, higher accuracy, precision, recall and F1 scores were obtained. This network with attention mechanisms has classified the detection of deepfake images with up to 99% success. The findings of this study will provide an important step in the detection of deep forged images and offer a potential solution for a wider range of applications.
Creation of Annotated Synthetic UAV Video Dataset for Object Detection and Tracking

In order for object detection and tracking in videos obtained from unmanned aerial vehicles (UAVs) by deep convolutional neural networks (DCNN), extensive ground truth optical flow, occlusion and segmentation datasets, of various objects or vehicles, are required during the training and testing processes. The mentioned ground truth informations are not widely available in the literature due to the difficulty of labeling or extracting them from real-life recorded UAV video images. In this study, ground truth optical flow, occlusion and segmentation datasets were produced synthetically for the first time with the UAV point of view in a novel way, so as to fill the gap in literature. The ground truth datasets were created for each vehicle by subjecting the triangles (mesh) automatically generated by the Unity engine to the homography method. With this method, 1920x1080 and 250x250 sized synthetic datasets consisting of 100 scenarios were obtained.
Sensitivity Improvement in Optical Phase Diffraction Using Empirical Mode Decomposition

Phase diffraction is a potent property used in transparent dielectric film characterization. The measured diffraction pattern on the camera is evaluated by matching numerically computed diffraction patterns to determine the optical properties of the ultra-thin films (refractive index, thickness, etc.). However, the obtained diffraction data is not only a nonlinear and non-stationary signal but also exhibits micron-scale variations, thus limiting the measurement accuracy. Therefore, it is challenging to identify shifts in minima and deviations in amplitude on diffraction data to extract information about the optical properties of phase objects. In this study, it is aimed to improve the thickness sensitivity of the system by applying Empirical Mode Decomposition (EMD) to plane wave-based near- field phase diffraction data. Since EMD is very sensitive to abrupt changes in the signal due to the spatial frequency components, the nanoscale variations in the film thickness become more observable and detectable. Experimental outputs and numerical simulations show that the decomposition increases the thickness sensitivity comparing the classical matching technique.
Breast Cancer Detection Using Transformer and BiLSTM Based Ensemble Learning

Breast cancer is one of the most common types of cancer among women, and early diagnosis is essential for effective treatment. In recent years, deep learning techniques have shown promising results in solving image classification problems. In this study, labeled image patches obtained from whole slide images in the BACH dataset were used for breast cancer classification. The performances of pre-trained deep learning models for breast cancer classification on labeled image patches were compared. Vision Transformer (ViT) gives better performance with the local features extracted by focusing on the important regions in the image due to the transformer structure and its Attention mechanism. Moreover, the model extending the Xception backbone architecture with Bidirectional Long Short-Term Memory (BiLSTM) layers, XceptionBiLSTM, significantly improved classification performance by learning the spatial relationships between image patches. Furthermore, data augmentation techniques were applied to dataset containing a limited number of image patches, which increased the models' generalization capacity and prevented overfitting. With the proposed architecture, which is the result of combining the predictions of the individual Xception, ViT, and XceptionBiLSTM models used with the ensemble learning approach, 90% accuracy in the hidden test dataset, 95% accuracy and 94.9% F1-Score in the validation dataset were obtained. The results obtained demonstrate the significant potential of the proposed ensemble learning-based architecture for breast cancer classification.
Definition of a Graph Cepstrum for Homomorphic Processing of Graph Signals

In recent years, with the increase of complex and irregular data in our everyday lives, graph signal processing has gained popularity especially among the signal processing researchers. In this manuscript, we define graph cepstrum as a new tool which could be applied for homomorphic processing of graph signals. Our motivation in defining graph cepstrum was the successful application of conventional cepstrum in speech processing for determining pitch period. In this manuscript, as an example, we also present an application of graph cepstrum using a synthetic graph signal. It is our belief that, in future applications, graph cepstrum could also be used advantageously in processing of certain real-world graph signals.
Active Domain Adaptation with Generated Images for Thermal Imagery

Thermal imaging, which performs more robustly against the visible light spectrum in real-world conditions such as low light, has limited use in deep learning due to the need for labeled and publicly available datasets. In contrast, unsupervised domain adaptation methods consider using the additional information from labeled RGB datasets in thermal tasks. Active domain adaptation aims to increase performance by labeling thermal data within a certain budget with labeling costs. Recent advances in image synthesis promise that the cost of labeling suggested by active domain adaptation may be eliminated by producing thermal images with the label ready. In this context, active domain adaptation with generated images for thermal imagery is examined in this study. Extensive experiments were performed using MSCOCO as the RGB dataset and FLIR ADAS as the thermal dataset. The proposed approach has achieved a comparable result with the state-of-the-art active domain adaptation methods, eliminating the labeling cost.