Reviewerly/exHarmony huggingface dataset:
High Resolution Explanation Maps for CNNs using Segmentation Networks

Recent developments have resulted in multiple techniques trying to explain how deep neural networks achieve their predictions. The explainability maps provided by such techniques are useful to understand what the network has learned and increase user confidence in critical applications such as the medical field or autonomous driving. Nonetheless, they typically have very low resolutions, severely limiting their capability of identifying finer details or multiple subjects. In this paper we employ an encoder-decoder architecture with skip connection known as U-Net, originally developed for segmenting medical images, as an image classifier and we show that state of the art explainable techniques applied to U-Net can generate pixel level explanation maps for images of any resolution.
Web-Based Recycle Waste Management for E-commerce

Garbage is a severe problem for Indonesia in the millennial era, especially in Jakarta, the capital city. In addition, the amount of garbage in DKI Jakarta continues to increase every day, which in the end, the garbage that is clogged in waterways will cause puddles and floods during the rainy season. This paper discusses the development of a Website to involve the community in a waste recycling management system, where the community can make recycled waste an economic opportunity by sorting and collecting recyclable waste that can be transacted through this Website. This Website is built using personal home pages (PHP) and uses MySQL to store database tables. The system built is modeled using a use case diagram to describe the proposed business process for a recycling waste management system, and the database table model is modeled using a class diagram.
Implementation Path of Intelligent Rehabilitation under the Background of Healthy China Construction

Improving rehabilitation service capacity is an important part of healthy China construction, and intelligent technology is a powerful mechanism for the development of rehabilitation. This paper reviews the background of national policies for the construction of healthy China, analyzes and summarizes the shortcomings that restrict the improvement of rehabilitation service capacity, and recommendations of the implementation path of intelligent rehabilitation. By elaborating the service process of intelligent rehabilitation, the intelligent technical means suitable for integration are analyzed in detail from the four key links of real-time health detection, remote home intelligent rehabilitation intervention, health classification and evaluation standard system and health intervention standard system in order to build the general framework of the implementation path of intelligent rehabilitation. At the same time, taking hypertension rehabilitation as an example, this paper introduces the intelligent rehabilitation practice exploration and reference mode of hypertension intelligent equipment, hypertension rehabilitation clinical research and hypertension rehabilitation database. Finally, in combination with the concept of intelligent Internet of Everything, the definition of Rehabilitation Internet of Things is proposed, and the timing of intelligent rehabilitation in the context of healthy China construction is summarized.
LevelUp – Automatic Assessment of Block-Based Machine Learning Projects for AI Education

Although artificial intelligence (AI) is increasingly involved in everyday technologies, AI literacy amongst the general public remains low. Thus many AI education curricula for people without prior AI experience have emerged, often utilizing graphical programming languages for hands-on projects. However, there are no tools that assist educators in evaluating learners&#x2019; AI projects or provide learners with contemporaneous feedback on their work. We developed LevelUp, an automatic code analysis tool to support these educators and learners. LevelUp is built into a block-based programming platform and gives users continuous feedback on their text classification projects. We evaluated the tool with a crossover user study where participants completed two text classification projects, once where they could access LevelUp and once when they could not. To measure the tool&#x2019;s impact on participants&#x2019; understanding of text classification, we used pre-post assessments and graded both of their projects against LevelUp&#x2019;s rubric. We saw a significant improvement in the quality of participants&#x2019; projects after they used the tool. We also used questionnaires to solicit participants&#x2019; feedback. Overall, participants said that LevelUp was useful and intuitive. Our investigation of this novel automatic assessment tool can inform the design of future code analysis tools for AI education.
Defense Against Adversarial Attacks Using Chained Dual-GAN Approach

There are many classification models used in image recognition but neural networks play a significant role to classify images into discrete classes. When an input image is modified by adversarial attacks, the changes are imperceptible to the human eye, but it still leads to misclassification of the images. In this paper, we propose an SR-GAN inspired iterative approach dubbed Chained Dual-GAN (CD-GAN) which tries to tackle the defense against adversarial attacks by minimizing the perturbations of the adversarial image using iterative undersampling and oversampling.
Searching Personalized $k$-wing in Bipartite Graphs

There are extensive studies focusing on the application scenario that all the bipartite cohesive subgraphs need to be discovered in a bipartite graph. However, we observe that, for some applications, one is interested in finding bipartite cohesive subgraphs containing a specific vertex. In this paper, we study a new query dependent bipartite cohesive subgraph search problem based on $k$-wing model, named as the personalized $k$-wing search problem. We introduce a $k$-wing equivalence relationship to summarize the edges of a bipartite graph $G$ into groups. Therefore, all the edges of $G$ are segregated into different groups, i.e. $k$-wing equivalence class, forming an efficient and wing number conserving index called EquiWing. Further, we propose a more compact version of EquiWing, EquiWing-Comp, which is achieved by integrating our proposed $k$-butterfly loose approach and discovered hierarchy properties. These indices are used to expedite the personalized $k$-wing search with a non-repetitive access to $G$, which leads to linear algorithms for searching the personalized $k$-wing. Moreover, we conduct a thorough study on the maintenance of the proposed indices for evolving bipartite graphs. We discover novel properties that help us localize the scope of the maintenance at a low cost. By exploiting the discoveries, we propose novel algorithms for maintaining the two indices, which substantially reduces the cost of maintenance. We perform extensive experimental studies in real, large-scale graphs to validate the efficiency and effectiveness of EquiWing and EquiWing-Comp compared to the baseline.
Personalized News Recommendation Algorithm for Event Network

In order to improve the level of personalized news recommendation efficiently and accurately, an event network-oriented personalized news recommendation algorithm is proposed. First, the event network is used to analyze, predict users’ interests and preferences, and actively push information content to meet users’ personalized needs, so as to build a personalized news recommendation model. Under the mobile Internet technology, combined with the characteristics of the Internet, through the position and title similarity of sentences in the document and other features, the combined features are formed to calculate the sentence weight. Finally, the sentences are extracted according to the weight ranking to generate the news summary, so as to realize the research on personalized news recommendation algorithm for event network. The experimental results show that the proposed algorithm has high recall and coverage, short time, good recommendation effect, and strong recommendation performance.
A back-diffusion median integrated evolutionary optimization algorithm

To accelerate the convergence speed and enhance robustness, a back-diffusion median integrated evolutionary algorithm (BMIEA) is proposed combining the advantages of particle swarm optimization (PSO) and differential evolution algorithm (DE) in this paper. The BMIEA includes three mainly optimization strategies. (1) Firstly, a new mean-median velocity updating formula is proposed to control optimal path of individuals. It can accelerate the convergence speed via reducing adverse effects of outliers on the population. (2) Secondly, a random differential mutation (RDM) inspired by the DE is devised to avoid the individuals trapping into local optimum via getting one more chance to explore optimal position while exploiting local region in each evolution. (3) Thirdly, a targeted exploration method i.e., back-diffusion operation, is proposed inspired by the duality principle to further accelerate the convergence rate and enhance robustness of algorithm. A series of simulation experiments have verified that BMIEA algorithm has revealed competitiveness compared with 13 state-of-art GOBL-based optimization algorithms.
Discrete Search Photometric Stereo for Fast and Accurate Shape Estimation

We consider the problem of estimating surface normals of a scene with spatially varying, general bidirectional reflectance distribution functions (BRDFs) observed by a static camera under varying distant illuminations. Unlike previous approaches that rely on continuous optimization of surface normals, we cast the problem as a <i>discrete</i> search problem over a set of finely discretized surface normals. In this setting, we show that the expensive processes can be precomputed in a scene-independent manner, resulting in accelerated inference. We discuss two variants of our Discrete Search Photometric Stereo (DSPS), one working with continuous linear combinations of BRDF bases and the other working with discrete BRDFs sampled from a BRDF space. Experiments show that DSPS has comparable accuracy to state-of-the-art exemplar-based photometric stereo methods while achieving 10&#x2013;100x acceleration.
An Image Processing Approach for Real-Time Safety Assessment of Autonomous Drone Delivery

The aim of self-driving drones has attracted many re- searchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic way to verify the safety of the delivery stage. One of the primary steps in delivery operation is to ensure that the dropping zone is a safe area on arrival and during the dropping process. This paper proposes an image processing-based classifica- tion approach for the delivery drone dropping process at a predefined destination. It employs live streaming via a sin- gle onboard camera and Global Positioning System (GPS) information. A two-stage processing procedure is proposed. based on image segmentation and classification. Relevant parameters such as camera parameters, light parameters, dropping zone dimensions, and drone height from the ground are taken into account in the classification. This paper is, to the best of our knowledge, the first to address the problem of delivery-by-drone. The experimental results indicate that the proposed approach provides reliable accuracy based on low-demand calculations.
Product Review Analysis by Web Scraping Using NLP

Among the most essential NLP applications is sentiment analysis, often known as opinion mining. In recent years, sentiment analysis has received a lot of attention. It is concerned with text classification in order to establish the intent of the text’s final user. End-user feedback becomes the most important factor in determining the quality of a product’s content. Online product reviews are regarded as one of the most important sources of customer feedback. In the current environment, people may discover on products and make selections based on online review sites. Hence, extracting the precise product reviews from the dataset wherein we have preprocessed the dataset using sentimental analysis, counter vectorizer is used for feature extraction in bag of words and the classification is done through SVM, KNN, RF, DT, NB to predict the review status whether it is positive, negative, or neutral. The model is tuned with grid search, and the confusion matrix is plotted for the above-mentioned algorithms. The final aim of the work is to provide users with their wanted products. Thus, we have tested our categorization approach through amazon reviews.
Understanding Similar Code through Comparative Comprehension

Any problem in code may have multiple solutions that differ in details large and small. Because modern software development is characterized by an abundance of searchable and reusable code, effective developers must be able to judge not only the meaning of new algorithms but also the differences between alternatives. Therefore, we use a multi-method study to explore how developers perform comparative comprehension&#x2014; the cognitive activity of understanding how algorithms behave relative to each other.To explore how developers compare code, we performed a controlled experiment with 16 developers in a mixed think-aloud and interview format and another 95 developers in a survey format. In this experiment, participants investigated whether a pair of code snippets would demonstrate equivalent behavior when run, controlling for differences in behavior, programming languages, algorithmic structures, and meaningful names. Overall, our results describe how comparison fits into learning, reviewing, and reusing code. Our task observations shed light on how developers move between code similarities at different levels&#x2014;textual, structural, and schematic&#x2014;when simultaneously inspecting multiple snippets. In our experiment, developers made more accurate conclusions about behavior given similar languages and structures, with names acting as additional evidence in interaction with other cues, but they also overestimated whether behavior is equivalent in many cases. From this, we identify challenges developers face in comprehending alternatives and we highlight opportunities to better support developers in comparison activities.
A Crowdsourced Study of Visual Strategies for Mitigating Confirmation Bias

Confirmation bias is a type of cognitive bias that involves seeking and prioritizing information that conforms to a pre-existing view or hypothesis that can negatively affect the decision-making process. We investigate the manifestation and mitigation of confirmation bias with an emphasis on the use of visualization. In a series of Amazon Mechanical Turk studies, participants selected evidence that supported or refuted a given hypothesis. We demonstrated the presence of confirmation bias and investigated the use of five simple visual representations, using color, positional, and length encodings for mitigating this bias. We found that at worst, visualization had no effect in the amount of confirmation bias present, and at best, it was successful in mitigating the bias. We discuss these results in light of factors that can complicate visual debiasing in non-experts.
ML Blocks: A Block-Based, Graphical User Interface for Creating TinyML Models

This paper describes ML Blocks, https://tinyurl.com/ml-blocks, a novel interface for training, evaluating, and deploying Tiny Machine Learning (TinyML) models. TinyML is a fast-growing field that incorporates powerful machine learning algorithms into everyday technologies such as activity trackers and Internet of Things devices. Although TinyML-capable microcontrollers are popular in computer science education, few students have had the opportunity to learn about the field because of a lack of novice-friendly ML interfaces. With ML Blocks, users assemble data sets, define, and train neural network classifiers, within one unified block interface. Users can quickly evaluate their classifiers using built-in visualization tools and then export them for use in microcontroller projects. ML Blocks makes the end-to-end development of TinyML models easier for physical computing students and tinkerers at all levels.
Predicting Data Scientist Stuckness During the Development of Machine Learning Classifiers

The success of data scientists in developing machine learning models is contingent on an iterative development process for detecting patterns in data, finding and extracting useful features, and maximizing their model&#x2019;s performance. However, it is often the case that they struggle during model development and become stuck and unable to make significant progress. We collected qualitative and quantitative data from the workflow of data scientists that allow us to learn from and examine such moments of stuckness. We used this data to develop a model for predicting stuckness based on real-time indicators, such as code artifacts, and then used the model to develop an innovative algorithm that determines precisely when a potential stuckness intervention should occur: as close as possible to the beginning of actual stuckness. Our algorithm&#x2019;s performance indicates the potential efficacy of predicting data scientist stuckness algorithmically under real-world circumstances and for real-world needs.
Tools for Creating UI Automation Macros

Automation macros enable users to perform digital tasks programmatically to save time or support hands-free interaction. For example, macros can be used to perform web scraping for a research project (e.g., scraping articles from a news site) or personal task automation via natural language (e.g., ordering food for delivery). Some kinds of automation are built into our devices and are readily available (e.g., via Siri [1] or Alexa [2] ), but this set is limited and often will not support a user&#x2019;s niche or complex needs. Users can create their own custom macros, but traditionally this requires writing program code which involves a significant amount of effort for programmers and is infeasible for non-programmers.
The Role of Expertise on Insight Generation from Visualization Sequences

Data analysts often tediously create visualization sequences to derive insights about what they see. While recent AI-driven approaches generate sequences to optimize visualization appeal and individual user preferences, extended cognitive fit theory suggests that expertise and insight type will affect the visualizations that analysts prefer. To investigate the role of expertise on insight generation from visualization sequences, we asked data scientists and accountants to report their insights as they investigated two business datasets. We found that both groups frequently followed the visualization sequences in order. However, expertise played a role in predicting the types of visualizations that each group chose to visit when they had finished the sequence but had time remaining. We also found significant interaction effects of visualization type, insight type, and expertise when assessing the numbers of insights generated per participant. Based on these results, we recommend that AI-driven data visualization tools should incorporate expertise as a feature for predicting new visualizations to produce.
Evaluating a Casual Procedural Generation Tool for Tabletop Role-Playing Game Maps

Tile maps are useful for a wide variety of games, particularly tabletop role-playing games. However, existing tools for creating them usually either require the map to be created entirely by hand, or procedurally generate the entire map with only a few parameters being controlled by the user. In previous work, we presented a mixed-initiative tool for generating tile maps that gives the user precise control over the generated map while still allowing them to take advantage of procedural generation. In this work, we empirically evaluate this tool by asking five users to complete three thinkaloud tasks and a post-task survey. We found that users found the tool fun to use and that it made map design easier. We discuss takeaways and how this tool and similar tools can be made better.
Barriers in Front-End Web Development

Developers building web applications constantly face challenges, particularly in working with complex APIs. In response, developers often turn to Stack Overflow, offering a window into the programming barriers developers face. We examined 301 posts on Stack Overflow related to front-end web development and systematically characterized the challenges present in these posts. We found that most challenges reflected not a request for new code or an explanation of an error message but a request about how a specific code snippet might be edited to make its behavior as desired. Many challenges also reflected an underlying need to gather information about how specific code idioms are implemented within a framework or library. We identified 28 barriers developers face in front-end web development. Our findings suggest opportunities for facilitating more effective interactions with complex APIs through new types of programming content and tools that better address barriers in working with code idioms.
Self-Training Enabled Efficient Classification Algorithm: An Application to Charging Pile Risk Assessment

With the continuous development of electric vehicles (EV), large-scale distributed charging piles have been deployed in the wild. Therefore, it is extremely essential to evaluate the risk state of EV charging piles efficiently and effectively. This paper aims to measure the capability of supervised and semi-supervised machine learning techniques in assessing the risk state of EV charging piles. We investigate 8 algorithms, including Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Self-Training based on SVM (ST-SVM), Self-Training based on RF (ST-RF), Self-Training based on AdaBoost (ST-AdaBoost) and Self-Training based on GBDT (ST-GBDT). We first collect data on normal and abnormal termination of charging services from an actual Internet of Vehicles platform. The dataset consists of 17,773 recordings and 7 features generated from the records, which are used for classification. According to the statistical times of 7 features, 20% of recordings are labeled by knowledgable experts into three classes: low-risk, medium-risk and high-risk. Experimental results indicate that ST-AdaBoost and ST-GBDT show more excellent overall classification performance, compared with the other traditional supervised methods. We also apply ST-GBDT to predict the risk state of the unclassified piles and produce the statistic of piles from different manufacturers.
Document Image Understanding Computational Image Processing in the Cultural Heritage Sector

Textual documents, such as manuscripts, historical newspapers etc., make up an important part of our cultural heritage. Massive digitization projects have been conducted across the globe for a better preservation of, and for providing easier access to such, often vulnerable, documents. These digital counterparts also allow to unlock the rich information contained inside and across them thanks to various types of computational models for document image understanding. In this paper we will shed a light on the document image processing pipeline, from scan to information extraction. As it turns out, human perceptual driven algorithms are amongst the most powerful approaches for generic document image understanding, required to deal with a myriad of layouts. In this context, we will in particular explain Gestalt visioning and the linked concept of text homogeneity that allows for enhanced layout analysis and even damage recognition, especially relevant in a cultural heritage setting. We conclude with a recent promising development, namely joint visual and language processing, that will take document image understanding to the next level in the future.