Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images

Automatic defect detection of tire has become an essential issue in the tire industry. However, it is challenging to inspect the inner structure of tire by surface detection. Therefore, an X-ray image sensor is used for tire defect inspection. At present, detection of defective tires is inefficient because tire factories commonly conduct detection by manually checking X-ray images. With the development of deep learning, supervised learning has been introduced to replace human resources. However, in actual industrial scenes, defective samples are rare in comparison to defect-free samples. The quantity of defective samples is insufficient for supervised models to extract features and identify nonconforming products from qualified ones. To address these problems, we propose an unsupervised approach, using no labeled defect samples for training. Moreover, we introduce an augmented reconstruction method and a self-supervised training strategy. The approach is based on the idea of reconstruction. In the training phase, only defect-free samples are used for training the model and updating memory items in the memory module, so the reproduced images in the test phase are bound to resemble defect-free images. The reconstruction residual is utilized to detect defects. The introduction of self-supervised training strategy further strengthens the reconstruction residual to improve detection performance. The proposed method is experimentally proved to be effective. The Area Under Curve (AUC) on a tire X-ray dataset reaches 0.873, so the proposed method is promising for application.
The Parameterized Complexity of the Survivable Network Design Problem

For the well-known Survivable Network Design Problem (SNDP) we are given an undirected graph $G$ with edge costs, a set $R$ of terminal vertices, and an integer demand $d_{s,t}$ for every terminal pair $s,t\in R$. The task is to compute a subgraph $H$ of $G$ of minimum cost, such that there are at least $d_{s,t}$ disjoint paths between $s$ and $t$ in $H$. If the paths are required to be edge-disjoint we obtain the edge-connectivity variant (EC-SNDP), while internally vertex-disjoint paths result in the vertex-connectivity variant (VC-SNDP). Another important case is the element-connectivity variant (LC-SNDP), where the paths are disjoint on edges and non-terminals. In this work we shed light on the parameterized complexity of the above problems. We consider several natural parameters, which include the solution size $\ell$, the sum of demands $D$, the number of terminals $k$, and the maximum demand $d_\max$. Using simple, elegant arguments, we prove the following results. - We give a complete picture of the parameterized tractability of the three variants w.r. t. parameter $\ell$: both EC-SNDP and LC-SNDP are FPT, while VC-SNDP is W[1]-hard. - We identify some special cases of VC-SNDP that are FPT: * when $d_\max\leq 3$ for parameter $\ell$, * on locally bounded treewidth graphs (e.g., planar graphs) for parameter $\ell$, and * on graphs of treewidth $tw$ for parameter $tw+D$. - The well-known Directed Steiner Tree (DST) problem can be seen as single-source EC-SNDP with $d_\max=1$ on directed graphs, and is FPT parameterized by $k$ [Dreyfus&Wagner 1971]. We show that in contrast, the 2-DST problem, where $d_\max=2$, is W[1]-hard, even when parameterized by $\ell$.
Managing the Aging Workforce: Challenges and Solutions

Introduction. PART I: THE CHALLENGE AHEAD. Chapter 1. Critical Challenges of the Aging Workforce for Enterprise Sustainability and Survival. Chapter 2. How Demographic Trends will Impact Economies, Industries and Enterprises. PART II: MANAGERIAL OBJECTIVES. Chapter 3. Company Objectives to Increase the Value (V) of the Aging Workforce for an Enterprise. Chapter 4. The 5V-Framework to Increase both Productivity and Creativity of the Aging Workforce. PART III: THE FIVE VALUE-ADDING FIELDS. Chapter 5. The First V-Field: Mindset Change Approaches for the Aging Workforce. Chapter 6. The Second V-Field: Knowledge Management (KM) Approaches for the Aging Workforce. Chapter 7. The Third V-Field: Health Management Approaches for the Aging Workforce. Chapter 8. The Fourth V-Field: Work Environment Approaches for the Aging Workforce. Chapter 9. The Fifth V-Field: Human Resources Management (HRM) Approaches for Managing the Aging Workforce. PART IV: PUTTING IT ALL TOGETHER. Chapter 10. The 5V-Scorecard for Measuring the Performance of an Aging Workforce. Glossary of Major Terms. Notes. Index.
Building Back Better – Democratization of Performance Monitoring with Open Data

The COVID-19 pandemic accelerated the use, sharing, and distribution of data on a global basis. Higher levels of transparency were achieved with continual updates of pandemic related information. The air transportation sector – while by definition an information rich industry – is a notable exception. While different organizations offered aggregated data on air traffic developments on national or airport level, complementary data on air traffic movements for further analysis are not available publicly. This creates a deadlock between addressing the societal needs of monitoring how aviation recovers from the COVID-19 pandemic and addresses the aspirational environmental goals. This paper investigates the feasibility of utilizing open data for the operational performance monitoring at airports. The exploratory work focusses on a subset of the indicators proposed under ICAO’s Global Air Navigation Plan used to assess the operational performance in the arrival phase. A novel approach to characterize and assess the arrival flow management and level of traffic synchronization is presented. This will allow to evaluate on-going air traffic recovery and identify operational bottlenecks. The study is performed as a use-case analysis for three major European airports by comparing the observed performance in the months of March and May for the successive years 2019, 2020, and 2021. The results demonstrate the general feasibility and utility of open data for operational performance monitoring. The classical performance measure for the arrival flow are determined based on the open trajectory data. A geospatial-temporal evaluation support the tracking of traffic synchronisation effort. A higher level of transparency therefore available to the interested public, policy decision-makers and strategic planners with direct feedback on the recovery and actual operational performance. The suitability of the traffic synchronization measure and its parameterization requires further validation across a wider set of airports and will be iteratively refined.
Reply on CC1

We are not sure if we understood the first major comment correctly: The interaction of rainfall intensities and numerical errors is one of the main points in this manuscript. However, we fully acknowledge that this should be made clearer in the introduction. Indeed, the main problem with numerical errors of Richards’ equation in the context of weather and climate models is precisely that precipitation intensities increase in highresolution setups. This is a consequence of three main factors: First, increasing horizontal resolution decreases the spatial smoothing of precipitation intensities. Second, shorter time steps are required in order to ensure numerical stability as implied by the CourantFriedrichs-Lewy (CFL) condition, this in turn decreases the temporal smoothing of precipitation intensities. Third, high resolution setups are often run without a parameterization for convective processes in the atmosphere, which also typically leads to higher precipitation intensities (see e.g. Ban et al., 2014). In that sense the ‘numerical demons’ described in the relatively new paper by la Follette et al. (2021) are also summoned by the recent trend towards kilometer-scale modelling in weather and climate models and there are some interesting parallels between the work of la Follette et al. (2021) and our manuscript. As Ms de Haan mentions, the scope of the paper by la Follette et al. (2021) is on conceptual hydrological models while our manuscript is solely on numerical errors of the Richards equation. We choose this relatively narrow scope because the one-dimensional Richards’ equation is extensively used in land surface modelling. We acknowledge that the relationship between numerical errors and precipitation intensities could be investigated more systematically.
Improving Medical Q&A Matching by Augmenting Dual-Channel Attention with Global Similarity

The emergence of online medical question-answer communities has helped to balance the supply of medical resources. However, the dramatic increase in the number of patients consulting online resources has resulted in a large number of repetitive medical questions, significantly reducing the efficiency of doctors in answering these questions. To improve the efficiency of online consultations, a large number of deep learning methods have been used for medical question-answer matching tasks. Medical question-answer matching involves identifying the best answer to a given question from a set of candidate answers. Previous studies have focused on representation-based and interaction-based question-answer pairs, with little attention paid to the effect of noise words on matching. Moreover, only local-level information was used for similarity modeling, ignoring the importance of global-level information. In this paper, we propose a dual-channel attention with global similarity (DCAG) framework to address the above issues in question-answer matching. The introduction of a self-attention mechanism assigns a different weight to each word in questions and answers, reducing the noise of “useless words” in sentences. After the text representations were obtained through the dual-channel attention model, a gating mechanism was introduced for global similarity modeling. The experimental results on the cMedQA v1.0 dataset show that our framework significantly outperformed existing state-of-the-art models, especially those using pretrained BERT models for word embedding, improving the top-1 accuracy to 75.6%.
Dynamic management of integrated residential energy systems

This study combines principles of energy systems engineering and statistics to develop integrated models of residential energy use in the United States, to include residential recharging of electric vehicles. These models can be used by government, policymakers, and the utility industry to provide answers and guidance regarding the future of the U.S. energy system. Currently, electric power generation must match the total demand at each instant, following seasonal patterns and instantaneous fluctuations. Thus, one of the biggest drivers of costs and capacity requirement is the electricity demand that occurs during peak periods. These peak periods require utility companies to maintain operational capacity that often is underutilized, outdated, expensive, and inefficient. In light of this, flattening the demand curve has long been recognized as an effective way of cutting the cost of producing electricity and increasing overall efficiency. The problem is exacerbated by expected widespread adoption of non-dispatchable renewable power generation. The intermittent nature of renewable resources and their nondispatchability substantially limit the ability of electric power generation of adapting to the fluctuating demand. Smart grid technologies and demand response programs are proposed as a technical solution to make the electric power demand more flexible and able to adapt to power generation. Residential demand response programs offer different incentives
CC-Riddle: A Question Answering Dataset of Chinese Character Riddles

The Chinese character riddle is a unique form of cultural entertainment specific to the Chinese language. It typically comprises two parts: the riddle description and the solution. The solution to the riddle is a single character, while the riddle description primarily describes the glyph of the solution, occasionally supplemented with its explanation and pronunciation. Solving Chinese character riddles is a challenging task that demands understanding of character glyph, general knowledge, and a grasp of figurative language. In this paper, we construct a \textbf{C}hinese \textbf{C}haracter riddle dataset named CC-Riddle, which covers the majority of common simplified Chinese characters. The construction process is a combination of web crawling, language model generation and manual filtering. In generation stage, we input the Chinese phonetic alphabet, glyph and meaning of the solution character into the generation model, which then produces multiple riddle descriptions. The generated riddles are then manually filtered and the final CC-Riddle dataset is composed of both human-written riddles and these filtered, generated riddles. In order to assess the performance of language models on the task of solving character riddles, we use retrieval-based, generative and multiple-choice QA strategies to test three language models: BERT, ChatGPT and ChatGLM. The test results reveal that current language models still struggle to solve Chinese character riddles. CC-Riddle is publicly available at \url{https://github.com/pku0xff/CC-Riddle}.
Is the revisited hypervolume an appropriate quality indicator to evaluate multi-objective test case selection algorithms?

Multi-objective test case selection techniques are widely investigated with the goal of devising novel solutions to increase the cost-effectiveness of verification processes. When evaluating such approaches the entire Pareto-frontier of the algorithm needs to be considered. To do so, several quality indicators exist. The hyper-volume (HV) is one of the most well-known and applied quality indicator. However, in the context of test case selection, this metric has certain limitations. For instance, two different fitness function combinations are not comparable if this metric is used at the search algorithm's objective function level. Consequently, researchers proposed the revisited HV (rHV) indicator. To compute the rHV, each solution of the search algorithm is individually assessed through two external utility functions: the cost and the fault detection capability (FDC). However, this increases the risk of having dominated solutions, which in practice may lead a decision maker (DM) to select such dominated solution. In this paper we assess whether the rHV is an appropriate quality indicator to assess multi-objective test case selection algorithms. To do so, we empirically assess whether the results between the rHV and the FDC of the different DM instances hold. Long story short, the rHV is an appropriate quality indicator.
A Systematic Review on Strategies of Depression Detection from Social Media

Depression is considered as one of the severe global challenges of our current society. Millions of people suffer from depression each day. Beside feeling sad and lose of interests, depressed person may face various heath complexities or may commit suicide. Therefore, depression is said to be a “Global Burden”. Traditionally, psychiatrists conduct face to face interviews with a patient referring to depression criteria. Unfortunately, many patients feel shy to consult doctor which deteriorates their mental health even more. Additionally, everyday large number of people are sharing their thoughts and feelings on various social media platforms. Since people are spending huge amount of time online, researchers are actively linking online system with depression detection to understand the mental health of the individuals or even the entire population. So far, various efforts have been made to detect depression in users through their posts, tweets and messages on different social media platforms which generated successful outcomes. This paper reviews recent theories which were developed to detect depression from different social media. This survey aims in providing information about current depression detection methods, highlighting their core features, data source, findings and certain drawbacks, promoting further research in the field of depression detection and prevention.
Comparative Analysis of Optimizers and Transfer Learning Methods for Headgear Detection Using Convolutional Neural Network

In untapped nations, cars are quite uncommon because motorcycles have always dominated the transportation industry. Over the past few years, there has been an increase in motorbike accidents. Motorcyclists who do not use fluorescent helmets because they don't think they offer enough protection are frequently engaged in traffic accidents. Today, it is against the law to operate a motorbike without a helmet, which increases accidents and fatalities in India. Every motorcyclist must wear a helmet when operating a motorcycle by law for the protection of the public. However, the majority of people continue to disprove this by driving without wearing helmets. In order to solve this problem, we provide a method in this work that uses real-time surveillance footage to automatically identify motorcycle riders without helmets and identify license plates. When the traffic police observe motorbike riders riding without helmets in a group at intersections, they also use CCTV footage to seize control of the drivers of those cars and fine the helmet-less riders. But only human initiative and dedication can make it happen. Because of its great accuracy, Convolutional neural networks are utilized for picture categorization and recognition. The Convolutional neural network uses a hierarchical model that builds a network in the shape of a funnel and eventually produces a fully connected layer where all the neurons are connected to one another and the output is processed. The 764 photos in the helmet detection dataset that is accessible on Kaggle have bounding box annotations in the PASCAL VOC format.
Using Voice and Biofeedback to Predict User Engagement during Product Feedback Interviews

Capturing users’ engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collecting and analyzing users’ feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in contexts where online feedback is limited, as for the majority of apps, and software in general. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biometric data, in terms of physiological and voice features, to complement product feedback interviews with information about the engagement of the user on product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e., biofeedback) using an Empatica E4 wristband, and capturing their voice through the default audio-recorder of a common laptop. Our results show that we can predict users’ engagement by training supervised machine learning algorithms on biofeedback and voice data, and that voice features alone can be sufficiently effective. The best configurations evaluated achieve an average F1 \(\sim 70\% \) in terms of classification performance, and use voice features only. This work is one of the first studies in requirements engineering in which biometrics are used to identify emotions. Furthermore, this is one of the first studies in software engineering that considers voice analysis. The usage of voice features can be particularly helpful for emotion-aware feedback collection in remote communication, either performed by human analysts or voice-based chatbots, and can also be exploited to support the analysis of meetings in software engineering research.
FedDMF: Privacy-Preserving User Attribute Prediction using Deep Matrix Factorization

User attribute prediction is a crucial task in various industries. However, sharing user data across different organizations faces challenges due to privacy concerns and legal requirements regarding personally identifiable information. Regulations such as the General Data Protection Regulation (GDPR) in the European Union and the Personal Information Protection Law of the People's Republic of China impose restrictions on data sharing. To address the need for utilizing features from multiple clients while adhering to legal requirements, federated learning algorithms have been proposed. These algorithms aim to predict user attributes without directly sharing the data. However, existing approaches typically rely on matching users across companies, which can result in dishonest partners discovering user lists or the inability to utilize all available features. In this paper, we propose a novel algorithm for predicting user attributes without requiring user matching. Our approach involves training deep matrix factorization models on different clients and sharing only the item vectors. This allows us to predict user attributes without sharing the user vectors themselves. The algorithm is evaluated using the publicly available MovieLens dataset and demonstrate that it achieves similar performance to the FedAvg algorithm, reaching 96% of a single model's accuracy. The proposed algorithm is particularly well-suited for improving customer targeting and enhancing the overall customer experience. This paper presents a valuable contribution to the field of user attribute prediction by offering a novel algorithm that addresses some of the most pressing privacy concerns in this area.