Reviewerly/exHarmony huggingface dataset:
Lesion Guided Explainable Few Weak-Shot Medical Report Generation

Medical images are widely used in clinical practice for diagnosis. Automatically generating interpretable medical reports can reduce radiologists' burden and facilitate timely care. However, most existing approaches to automatic report generation require sufficient labeled data for training. In addition, the learned model can only generate reports for the training classes, lacking the ability to adapt to previously unseen novel diseases. To this end, we propose a lesion guided explainable few weak-shot medical report generation framework that learns correlation between seen and novel classes through visual and semantic feature alignment, aiming to generate medical reports for diseases not observed in training. It integrates a lesion-centric feature extractor and a Transformer-based report generation module. Concretely, the lesion-centric feature extractor detects the abnormal regions and learns correlations between seen and novel classes with multi-view (visual and lexical) embeddings. Then, features of the detected regions and corresponding embeddings are concatenated as multi-view input to the report generation module for explainable report generation, including text descriptions and corresponding abnormal regions detected in the images. We conduct experiments on FFA-IR, a dataset providing explainable annotations, showing that our framework outperforms others on report generation for novel diseases.
Multi-task Video Enhancement for Dental Interventions

A microcamera firmly attached to a dental handpiece allows dentists to continuously monitor the progress of conservative dental procedures. Video enhancement in video-assisted dental interventions alleviates low-light, noise, blur, and camera handshakes that collectively degrade visual comfort. To this end, we introduce a novel deep network for multi-task video enhancement that enables macro-visualization of dental scenes. In particular, the proposed network jointly leverages video restoration and temporal alignment in a multi-scale manner for effective video enhancement. Our experiments on videos of natural teeth in phantom scenes demonstrate that the proposed network achieves state-of-the-art results in multiple tasks with near real-time processing. We release Vident-lab at https://doi.org/10.34808/1jby-ay90 , the first dataset of dental videos with multi-task labels to facilitate further research in relevant video processing applications.
The Utilization of Text Summary Techniques to Improve Students’ Reading Comprehension Skills

This study aims to improve students’ reading comprehension skills using the text summarization technique in the 5th grade of SDN Kasunyatan. The subjects of this classroom action research (CAR) involved forty students. The data was collected using learning observation, reading comprehension tests, and documentation. The analysis of these data uses both qualitative and quantitative descriptive approaches. This study uses a text summarization technique as an action. The text summarization technique has four syntaxes. First is monitoring, which means that students read the text until they understand the reading material. Next is trimming down to take keywords from each paragraph, then combining these keywords and creating mind mapping. The last is calling; it means that students rewrite all things they remember about the reading text. This research shows that the text summarization techniques improved students’ reading comprehension skills, indicating an increase for each indicator.
Exploration on the Methods of Teaching Material Construction of General Education Courses in Colleges and Universities in the New Media Era——Take the Course of " Appreciation of Fine Arts " as an Example

In the era of rapid development of the Internet, the general education courses in colleges and universities use new media as the carrier to carry out teaching work, which has an important impact on mobilizing the enthusiasm of college students and improving the teaching effect.This article combines the "Internet plus" strategy, taking the course of "Appreciation of Fine Arts" as an example, from the educational concept of general education courses in colleges and universities, the selection of general courses textbooks, the readability of general courses textbooks, the format of general courses textbooks and the functions of general courses textbooks on five levels, discuss the construction methods of general education curriculum materials in colleges and universities to better meet the needs of general education courses construction and talent training in colleges and universities in the new era.
Multi-Level Cycle-Consistent Adversarial Networks with Attention Mechanism for Face Sketch-Photo Synthesis

The synthesis between face sketches and face photos has important application values in law enforcement and digital entertainment. In cases of a lack of paired sketch-photo data, this paper proposes an unsupervised model to solve the problems of missing key facial details and a lack of realism in the synthesized images of existing methods. The model is built on the CycleGAN architecture. To retain more semantic information in the target domain, a multi-scale feature extraction module is inserted before the generator. In addition, the convolutional block attention module is introduced into the generator to enhance the ability of the model to extract important feature information. Via CBAM, the model improves the quality of the converted image and reduces the artifacts caused by image background interference. Next, in order to preserve more identity information in the generated photo, this paper constructs the multi-level cycle consistency loss function. Qualitative experiments on CUFS and CUFSF public datasets show that the facial details and edge structures synthesized by our model are clearer and more realistic. Meanwhile the performance indexes of structural similarity and peak signal-to-noise ratio in quantitative experiments are also significantly improved compared with other methods.
An Attendance Checking System on Mobile Devices Using Transfer Learning

IoT applications have been used in many contexts, especially applications on mobile devices. This work presents an attendance checking system by identifying and recognizing human faces on mobile devices using a transfer learning approach. This system includes a mobile application and a web application. These two applications are communicated by using APIs. The mobile application detects human faces by using the camera on that mobile device, then, the face features are extracted using the FaceNet model, and finally, the attendees are identified by computing similarity with existing faces in the database. The proposed system was tested on a dataset with 358 images of 52 employees in our office. Results show that the accuracy is about 93.46% on the test set and 97.06% in the real environment. Thus, this system could be used for checking attendances in several real contexts.
A forensic analysis of the Google Home: repairing compressed data without error correction

This paper provides a detailed explanation of the steps taken to extract and repair a Google Home's internal data. Starting with reverse engineering the hardware of a commercial off-the-shelf Google Home, internal data is then extracted by desoldering and dumping the flash memory. As error correction is performed by the CPU using an undisclosed method, a new alternative method is shown to repair a corrupted SquashFS filesystem, under the assumption of a single or double bitflip per gzip-compressed fragment. Finally, a new method to handle multiple possible repairs using three-valued logic is presented.
A Fuzzy Multistage Control Model for Stable Sustainable Agricultural Regional Development

We propose a further extension of a multistage fuzzy control model of stable sustainable regional agriculture development that involves an additional capacity to reflect a stability requirement which addresses a clear preference of the stakeholders for a limited variability of crucial development indicators and parameters. We presented the use of fuzzy dynamic programming for solving the problem in which many crucial aspects, in particular life quality indicators, are subject to objective, by the authorities, and subjective, by the inhabitants, evaluations which are closely related to human perception and cognitive abilities. This model is then augmented with a requirement of a limited variability of crucial development indicators, parameters, etc. For illustration, we have shown a simple example in which the problem is to determine the best (optimal) investment policy under different development scenarios, and subject to objective and subjective evaluations.
Concentration and Bioaccumulation of Toxic Metals and Polycyclic Aromatic Hydrocarbons in Soil and Lumbricus Terrestris in Kolo Creek, Niger Delta, Nigeria

The concentration of toxic metals and polycyclic aromatic hydrocarbons (PAHs) in soil, and their bioaccumulation in Lumbricus terrestris in areas around Kolo Creek, Niger Delta, Nigeria were evaluated in this study. 12 soil samples were collected from 4 sites, while Lumbricus terrestris were also collected from areas where soil samples were obtained. Toxic metals were extracted using aqua regia and quantified with a flame atomic absorption spectrophotometer. Extraction of PAHs was achieved by liquid-liquid soxhlet extraction, and quantified with a gas chromatograph-mass spectrometer. The bioaccumulation factor (BAF) for both toxic metals and PAHs were calculated. The results obtained in this study showed that the concentration of all studied toxic metals were within their respective regulatory limits in soil. The BAF for all toxic metals were less than 1 in all sites, indicating minimal bioaccumulation in Lumbricus terrestris. The Ʃ16 PAHs ranged from 7.30 to 77.7 mg kg-1 and 1.10 to 4.75 mg kg-1 in soil and Lumbricus terrestris across the 4 sites, respectively. The BAF values for individual PAHs were less than 1 in all sites, which indicated that PAH congeners were either not bioaccumulated or accumulated PAH congeners were metabolized. Hence, analysis of PAHs metabolites in Lumbricus terrestris is recommended.
Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN by Introducing CBAM

Sentiment analysis is a common scenario in natural language processing, which plays a key role in guiding product updates and iterations. Recently, with the diversification of the web, images have gradually become an important data form in the social media domain. Most of the previous visual sentiment analysis of social media have only considered the influence of image factors but ignored item factors and user factors. In this case, useful sentiment feature information can not be deeply explored, and which would limit the effectiveness and stability of the system. In this paper, we propose a iCVS-CNN based on item factors and a uCVS-CNN model based on user factors for the visual analysis of social media comment images. The models focus on learning feature representations in different subspaces by sequentially inferring the attention graph along two independent dimensions (channel and space) through a convolutional block attention module (CBAM). It is shown experimentally that the CBAM module can help to focus more on the sentiment features, which verifies the effectiveness of the model.
Maintenance of Self-Care Activities During COVID-19 Lockdown in Patients with Type 2 Diabetes That Received a Comprehensive Care Program Training

Introduction: In 2020, several countries established a global emergency state. Lockdowns restricted people’s lifestyles and daily activities to prevent coronavirus spread. These measures hindered diabetes mellitus control and lifestyle changes. This study aims to evaluate if attending a multidisciplinary program before the pandemic helped maintain a good metabolic state, lifestyle modifications, and mental health in patients with diabetes mellitus during the COVID-19 lockdown. Methods: Patients included in this study attended a multidisciplinary program, with < 5 years of diagnosis of type 2 diabetes, without disabling complications, between 18– 70 years old. The complete lockdown occurred from February 27, 2020, to May 31, 2020. The first patient (non-COVID) to return to the center for face-to-face consultation was in March 2021. Consultations in 2019 were face-to-face and changed to a virtual modality during 2020. We analyzed metabolic, lifestyle, mental health, and diabetes education parameters. Results: A total of 133 patients with type 2 diabetes mellitus were included with complete information in visits before and during the lockdown. Metabolic parameters and self-care measures (nutrition plan, foot evaluation, and self-glucose monitoring) evaluated on our patients had no change during the lockdown. We found a significant increase in the time patients spent sitting during the day (p< 0.05). Barriers to exercise increased during lockdown, being joint pain (3.8% to 12.0%, p< 0.01) and lack of time to exercise (4.5% to 7.5%, p=0.33) being the most common. There was no significant difference in symptoms of anxiety and depression, quality of life, and empowerment. Conclusion: A multidisciplinary diabetes mellitus program, including diabetes education for self-care activities, positively impacts patients, maintaining good outcomes despite lockdown difficulties. Keywords: multidisciplinary care, lifestyle, self-care, mental health, type 2 diabetes, COVID-19 pandemics
RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR Prediction

Click-through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge, which either perform few-shot user representation learning or adopt optimization-based meta-learning. However, existing methods suffer from information loss or inefficient optimization process, and they fail to explicitly model global user preference knowledge, which is crucial to complement the sparse and insufficient preference information of cold users. In this article, we propose a novel and efficient approach named RESUS, which decouples the learning of global preference knowledge contributed by collective users from the learning of residual preferences for individual users. Specifically, we employ a shared predictor to infer basis user preferences, which acquires global preference knowledge from the interactions of different users. Meanwhile, we develop two efficient algorithms based on the nearest neighbor and ridge regression predictors, which infer residual user preferences via learning quickly from a few user-specific interactions. Extensive experiments on three public datasets demonstrate that our RESUS approach is efficient and effective in improving CTR prediction accuracy on cold users, compared with various state-of-the-art methods.
Few-Shot Image Classification Based on Asymmetric Convolution and Attention Mechanism

Deep learning has achieved great success in the field of image processing, but it is often difficult to achieve good results when the amount of annotation data is small, and it is prone to overfitting. Few-shot learning can effectively solve this problem. The existing few-shot image classification models usually lead to a large computational loss, and the design of the network structure is complicated and costly. In addition, they do not pay attention to the key features of the image. To solve the above problems, this paper proposes a few-shot image classification method based on asymmetric convolution and attention mechanism. Through replacing the standard $3\times3$ convolution kernel with asymmetric convolution blocks, the feature extraction ability of the model is improved without increasing additional computational consumption. Secondly, the SENet (Squeeze-and-Excitation Networks) attention mechanism is introduced to explicitly construct the correlation between feature channels, so that the salient features of the image are highlighted and emphasized. Finally, the K-Nearest Neighbor (KNN) is used to calculate the similarity between the query image and the category. A large number of experimental results show that the proposed algorithm performs well in the task of few-shot image classification, which verifies the effectiveness and superiority of the algorithm.
IGAOD: An online design framework for interactive genetic algorithms

In order to prompt the theoretical research and application of interactive genetic algorithm (IGA), the framework for IGAOD (Interactive Genetic Algorithm Online Design) is developed from the perspective of software reuse and the commonness and individuality of IGA. This framework encapsulates the basic commonality of IGA, integrates some classic evolution operators of IGA, and provides visual application scenarios and data analysis charts to help users quickly build their own IGA systems. In addition, the template design pattern can be used to construct the genetic algorithm library, which encapsulates the commonness of a series of selection, crossover and mutation operators into different abstract base classes. These classes inherit the identical abstract base class, making it easier for the algorithm to maintain and expand. In this article, the use of the frame is introduced with the example of 3D vase shape design and plate pattern design. Therefore, the framework is more suitable for the theoretical and application research of IGA.
User-Centered/User Experience Uc/Ux Design Thinking Approach for Designing a University Information Management System

University Management Information Systems (UMIS) are a very essential part of a school’s ecosystem. Trying to build a functional UMIS is no longer a serious issue, these days as students interact with this system to perform tasks such as course registration, school fee payment, etc., the ease at which they do these activities is extremely important, any error or confusing experience they come in contact with can make the process dreadful for these users and demotivate them. This study would be centered on designing the User Interface (UI) and improving the UX of University Management Information Systems for web-based interfaces. User-Centered Design processes and system design thinking methodology were employed to solve the problem. Questionnaires were used to obtain the users' pain points as it relates to the existing UMIS in their schools, the responses were analyzed to understand the users’ pain and issues they face with their current UMIS and then decipher the right features to create a more usable interface. User personas and wireframes were used to make sense of the data obtained from user research. Figma, a visual design and prototyping tool was used for the prototype and interface design. The newly created interfaces were subjected to user testing using a platform called Maze. Users were able to interact with the platform and then answer certain questions as it relates to the developed system. Test data was used to measure usability parameters such as efficiency, effectiveness, learnability, ease of use and simplicity. From the testing phase, the developed system has a System Usability Score (SUS) of 87, it shows that users enjoyed using the system and could navigate through a platform they are interacting with for the first time, with little to no help. it was discovered that users prefer a simpler, responsive, and more interactive interface. Also, users were able to successfully complete tasks even though it is an interface they had never interacted with before. This study would address the usability issues students face while interacting with the UMIS platform provided for them by their institutions and also proposed a responsive and user-centered design which if implemented would improve students engagement on the platform and also reduce the constant problems that may arise from using the UMIS platform.
Recognition of compound characters in Kannada language

&lt;p&gt;&lt;span lang="EN-US"&gt;Recognition of degraded printed compound Kannada characters is a challenging research problem. It has been verified experimentally that noise removal is an essential preprocessing step. Proposed are two methods for degraded Kannada character recognition problem. Method 1 is conventionally used histogram of oriented gradients (HOG) feature extraction for character recognition problem. Extracted features are transformed and reduced using principal component analysis (PCA) and classification performed. Various classifiers are experimented with. Simple compound character classification is satisfactory (more than 98% accuracy) with this method. However, the method does not perform well on other two compound types. Method 2 is deep convolutional neural networks (CNN) model for classification. This outperforms HOG features and classification. The highest classification accuracy is found as 98.8% for simple compound character classification. The performance of deep CNN is far better for other two compound types. Deep CNN turns out to better for pooled character classes.&lt;/span&gt;&lt;/p&gt;
Mobile Forensics: Repeatable and Non-Repeatable Technical Assessments

This paper presents several scenarios where digital evidence can be collected from mobile devices, their legal value keeping untouched. The paper describes a robust methodology for mobile forensics developed through on-field experiences directly gained by the authors over the last 10 years and many real court cases. The results show that mobile forensics, digital analysis of smartphone Android or iOS can be obtained in two ways: on the one hand, data extraction must follow the best practice of the repeatability procedure; on the other hand, the extraction of the data must follow the best practice of the non-repeatability procedure. The laboratory study of the two methods for extracting digital data from mobile phones, for use as evidence in court trials, has shown that the same evidence can be obtained even when the procedure of unavailability of file mining activities has been adopted. Indeed, thanks to laboratory tests, the existence of multiple files frequently and continuously subjected to changes generated by the presence of several hashes found at forensic extractions conducted in very short moments of time (sometimes not exceeding 15 min) has been proven. If, on the other hand, the examination of a device is entrusted to a judicial police officer in order to conduct a forensic analysis to acquire data produced and managed by the user (such as images, audio, video, documents, SMS, MMS, chat conversations, address book content, etc.) we have sufficient grounds to believe that such examination can be organized according to the system of repeatable technical assessments.
Implementation of Task-based Learning in Teaching English in Indonesia: Benefits and Problems

A variety of approaches exist in foreign language teaching and learning; Task-based Learning (TBL) is one of them. It is an innovative approach that is developed from the Communicative Approach. The concepts of TBL have proven successful in classrooms. In this study, the writer conducted a literature review of task-based learning in teaching English dealing with the benefits and problems in implementing this approach in Indonesia. This paper seeks to review Task-based Learning (TBL), covering the definition of 'task' and Task-based Learning (TBL), the characteristics and stages of TBL, the teacher and students' roles in Task-based Learning, and also the advantages and disadvantages of Task-based Learning. It also addresses the application, the benefits and problems of Task-based Learning in the sense of Indonesian EFL context. This will help educators and language teachers in Indonesia to attach more importance to the contextual differences when applying Task-based Learning in Indonesia.