Reviewerly/exHarmony huggingface dataset:
The Impact of Customer Firm Data Breaches on the Audit Fees of Their Suppliers

This study investigates how customer firms’ data breaches can affect their suppliers' audit fees within the supply chain relationship. Based on the economic bond that exists through the customer firms’ and supplier firms' supply chains, we examine whether data breach incidents of customer firms are positively associated with their suppliers' audit fees. Consistent with the customer breaches increasing the audit risk to the supplier, we observe a positive association between disclosures of customer firm breaches and audit fees of the supplier. We further find that this association exists for both internal and external data breaches. We conduct two additional tests to provide additional evidence that the supply chain relationship drives our observed association. First, we find that the association varies predictably based on the strength of the underlying supply chain relationship. Second, we find that audit fees are higher when more customers disclose breaches in the supply chain in a given year. Interestingly, we find that while supplier auditors appear to price the risks associated with customer breaches, the breaches do not appear to increase audit delay or affect audit quality. The findings of this study are timely and relevant to academics, practitioners, and regulators.
Kmclib: Automated Inference and Verification of Session Types from OCaml Programs

Abstract Theories and tools based on multiparty session types offer correctness guarantees for concurrent programs that communicate using message-passing. These guarantees usually come at the cost of an intrinsically top-down approach, which requires the communication behaviour of the entire program to be specified as a global type. This paper introduces : an OCaml library that supports the development of correct message-passing programs without having to write any types. The library utilises the meta-programming facilities of OCaml to automatically infer the session types of concurrent programs and verify their compatibility ( k -MC [15]). Well-typed programs, written with , do not lead to communication errors and cannot get stuck.
A Path Tracking Method of a Wall-Climbing Robot towards Autonomous Inspection of Steel Box Girder

This paper proposes an autonomous inspection method for steel box girders that uses a wall-climbing robot instead of human workers. According to the 3D operating environment inside the steel box girder, the method proposes a 3D path for the robot to traverse positions to be inspected. The path comprises two alternate sections of the lane’s centerline and U-shaped steering. To realize the robot’s tracking of the desired path, kinematics analysis based on different poses during the inspection was carried out. Corresponding path tracking algorithms were adopted to ensure that the robot moves accurately and efficiently. In addition, for the smooth transition of the two path sections, this method adopts an algorithm of cooperatively controlling the lifting mechanism and the wheel speeds to achieve stable crossing of a 90° concave corner. Finally, experiment results show that the robot can steadily cross 90° concave corner and can steer to the adjacent lane and complete lane inspection along the desired path. The method can realize autonomous inspection for steel box girders using the wall-climbing robot.
Opinion Analysis and Emotion Prediction Based on Review Mining of New Energy Vehicle Industry

Social media public opinion contains a hot topic in the new energy automobile industry. Python is used to crawl the microblog, Today's headlines and other platform texts, and LDA model is constructed to classify the three themes of automobile parameters, listed brands and market information. Then, the consumer review data of automobile homes are crawled to dig out the preferences of consumers as appearance, endurance and motivation, and the pain points of consumers as charging, space and back row. Finally, a random forest model is constructed for sentiment analysis.
SP-GPT2: Semantics Improvement in Vietnamese Poetry Generation

Automatic text generation has garnered growing attention in recent years as an essential step towards computer creativity. Generative Pre-training Transformer 2 (GPT2) is one of the state-of-the-art approaches that have excellent successes. In this paper, we took the first step to investigate the power of GPT2 in traditional Vietnamese poetry generation. In the earlier time, our experiment with base GPT2 was quite good at generating the poem in the proper template. Though it can learn the patterns, including rhyme and tone rules, from the training data, like almost all other text generation approaches, the poems generated still has a topic drift and semantic inconsistency. To improve the cohesion within the poems, we proposed a new model SPGPT2 (semantic poem GPT2) which was built on the top GPT2 model and an additional loss to constrain context throughout the entire poem. For better evaluation, we examined the methods by both automatic quantitative evaluation and human evaluation. Both automatic and human evaluation demonstrated that our approach can generate poems that have better cohesion without losing the quality due to additional loss. At the same time, we are the pioneers of this topic. We released the first computational scoring module for poems generated in the template containing the style’s rule dictionary. Additionally, we are the first to publish a Luc-Bat dataset, including 87609 Luc Bat poems, which is equivalent to about 2.6 million sentences, combined with about 83579 poems in other styles was also published for further exploration. The code is available at https://github.com/fsoftailab/Poem-Generator
Minimizing Viral Transmission in COVID-19 Like Pandemics: Technologies, Challenges, and Opportunities

Coronavirus (COVID-19) pandemic has incurred huge loss to human lives throughout the world. Scientists, researchers, and doctors are trying their best to develop and distribute the COVID-19 vaccine throughout the world at the earliest. In current circumstances, different tracking systems are utilized to control or stop the spread of the virus till the whole population of the world gets vaccinated. To track and trace patients in COVID-19 like pandemics, various tracking systems based on different technologies are discussed and compared in this paper. These technologies include, cellular, cyber, satellite-based radio navigation and low range wireless technologies. The main aim of this paper is to conduct a comprehensive survey that can overview all such tracking systems, which are used in minimizing the spread of COVID-19 like pandemics. This paper also highlights the shortcoming of each tracking systems and suggests new mechanisms to overcome such limitations. In addition, the authors propose some futuristic approaches to track patients in prospective pandemics, based on artificial intelligence and big data analysis. Potential research directions, challenges, and the introduction of next-generation tracking systems for minimizing the spread of prospective pandemics, are also discussed at the end.
Study Comparison of the Efficiency of a Photovoltaic System with Fixed Panels and with Solar Tracker in the Northwest of Brazil

The performance of small photovoltaic (PV) systems connected to the grid in the state of Ceará is analyzed in this study. Three systems were developed, two of them with fixed angles of 18 and 22 degrees and one with a variable angle (solar tracker), which provided different results of generation efficiency, all of them have the same power of 2310 kW installed and are located on a building slab in the city of Fortaleza, in this configuration the systems have the same inputs to compare. The variation in generation efficiency be- tween systems is quite significant, reaching 19% between fixed and 27% when compared the worse fixed system and solar tracker, the higher costs for these types of system with tracker can increase the rate of return on investment turning into 2.9 years, higher than that of the fixed system with an angle of 22, which was 2.38 years.
Evolving open complexity

Information-theoretic analysis of large, evolved programs produced by running genetic programming for up to a million generations has shown even functions as smooth and well behaved as floating-point addition and multiplication lose entropy and consequently are robust and fail to propagate disruption to their outputs. This means that, while dependent upon fitness tests, many genetic changes deep within trees are silent. For evolution to proceed at a reasonable rate it must be possible to measure the impact of most code changes, yet in large trees, most crossover sites are distant from the root node. We suggest that to evolve very large, very complex programs, it will be necessary to adopt an open architecture where most mutation sites are within 10--100 levels of the organism's environment.
Improving the Convergence and Diversity in Differential Evolution Through a Stock Market Criterion

Most of the Evolutionary Algorithms (EA) use a population of candidate solutions to explore the search space following specific rules during an iterative process. These algorithms are designed expecting a good balance between exploration and exploitation during the search process. Besides, the diversity of the population is crucial to properly explore the search space. This article introduces an improved version of the Differential Evolution (DE) algorithm, which employs the moving average (MA) to determine when the population should diversify or intensify by using additional operators. The MA is one of the most used stock market indicators, providing recommendations for selling or buying stocks based on historical data. Here, the MA of the historical fitness and dimension-wise diversity is analyzed to determine if the DE continues operating normally or should diversify or intensify the search using additional operators. An exhaustive benchmark involving 37 optimization functions with different complexity levels confirmed the effectiveness of the proposed approach.
On Detection of Out of Distribution Inputs in Deep Neural Networks

Deep neural networks (DNNs) have achieved near-human-level accuracy on many datasets across different domains. But they are known to produce incorrect predictions with high confidence on out-of-distribution (OOD) inputs. This challenge has limited the adoption of deep learning models in high-assurance systems such as autonomous driving, air traffic man-agement, and medical diagnosis. The problem of detecting when an input is outside the training distribution of a machine learning model, and hence, its prediction on this input cannot be trusted, has received significant attention recently. Several techniques based on statistical, geometric, topological, or relational signature have been developed to detect OOD inputs. In this paper, we investigate two major sources of uncertainty in a deep neural network's prediction on a given input. The first uncertainty source is aleatoric due to the ambiguity or noise in the input. The second is due to epistemic uncertainty arising due to insufficiency of training data and corresponds to the OOD inputs. We posit that the training of deep neural networks using usual objectives cannot distinguish between these two sources of uncertainty. We describe lightweight modifications to the training of deep neural networks that enable deep neural networks to learn representations that can be used to detect OODs. For evaluating our approach, we conducted experiments on CIFAR10 and SVHN as in-distribution data and Imagenet, SVHN (for CIFAR10), and CIFAR10 (for SVHN) as OOD data across different DNN architectures such as ResNet34. WideResNet, and DenseNet.
Three Mars Missions from Three Countries: Multilingual Sentiment Analysis Using VADER

In February 2021, the media around the world celebrated the success of three main space missions to Mars, Emirates hope mission from United Arab Emirates, followed by Tianwen-1, from China, and finally Mars 2020 mission from NASA. The aim of this research is to conduct a sentimental analysis of these tweets using a VADER-Multi, a method that is particularly aimed at multilingual social media texts. Major finding predicts that Tweets on Mars have positive polarity, while Emirates Hope mission, Tianwen-1 and NASA Mars2020 have neutral polarity that slightly shift toward positive. suggest VADER-Multi can generate good results on multilanguage but might not be very reliable on a large online corpus. Future work can be investigating different multilingual solutions, providing a web-based tool and investigating the root cause of the system overloaded with too many requests. This is study is among the first to analyze Mars space missions. It is also among the first to examine VADER multilingual tool (VADER-Multi).
Data switching method among heterogeneous power information system databases based on knowledge graph

Abstract In order to solve the problems of long data switching time, low information utilization rate, and low switching accuracy in traditional data switching methods, a data switching method between heterogeneous power information system databases based on knowledge graphs is proposed. Based on the knowledge graph technology and its ternary structure, the data of the heterogeneous power information system is processed, and the N‐Gram model is used to realize the knowledge extraction, and the data error correction is established through the model. Through ODBC technology to complete the link between heterogeneous power information system databases, and then select the data access interface and determine the data extraction task, and finally realize the data exchange between the heterogeneous power information system databases. Experimental results show that the average communication channel bandwidth during the data switching process is 1.8 Gb/s, the highest value of data switching accuracy reaches 82%, and the information utilization rate after data switching is relatively high, indicating that the method has certain effectiveness.
That’s Good News ☹

Abstract. The use of emojis in digital communication has become increasingly popular, but how emojis are processed and integrated in reading processes remains underexplored. This study used eye-tracking to monitor university students’ ( n = 47) eye movements while reading single-line text messages with a face emoji embedded medially. Messages contained a semantically congruent emoji (e.g., That’s good news 😊 tell me more), a semantically incongruent emoji (e.g., That’s good news ☹ tell me more), or a dash (e.g., That’s good news – tell me more). Results revealed that emoji congruency did not influence early fixation measures (first fixation duration and gaze duration), nor the probability of regressions. However, there was a significant congruency effect in total reading time and trial dwell time, showing that incongruence incurred a processing cost. The present results extend previously reported semantic congruency effects in sentence reading to the processing of emojis. This result suggests that the semantic content conveyed by face emojis is integrated with sentence context late in processing. We further found that the use of congruent emojis improved the relationship between sender and receiver: Ratings collected separately suggested that message senders were liked better if they included congruent than incongruent emojis. Overall, emojis attracted attention: Participants were twice as likely to fixate on emojis than on dashes, and to fixate on emojis for longer.
Personalized stain style transfer layers for distributed histology classification

Federated learning (FL) contributed to the popularity of using multi-centric and collaborative integration of decentralized data across multiple institutions to train a robust model. Yet, data heterogeneity among the distributed clients remains a big challenge in FL. Specifically, in the case of digital histology, stain variation is commonplace. In a collaboration setting, color normalization and data augmentation are adopted to alleviate the variation. But they cannot be directly applied in the FL paradigm, as the requirement of data sharing or the availability of proxy samples. To address this issue, we propose a novel personalized federated learning (PFL) approach. Personalization stain transfer layers learn to project stain variant input images into a homogeneous space before being fed to the FL backbone. The proposed method is simple yet efficient. Empirical results on public available large patient cohorts demonstrate an observable classification accuracy improvement on popular neural network architectures including ResNet, Vgg, Wide ResNet.
MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation

We present MSeg, a composite dataset that unifies semantic segmentation datasets from different domains. A naive merge of the constituent datasets yields poor performance due to inconsistent taxonomies and annotation practices. We reconcile the taxonomies and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images, requiring more than 1.34 years of collective annotator effort. The resulting composite dataset enables training a single semantic segmentation model that functions effectively across domains and generalizes to datasets that were not seen during training. We adopt zero-shot cross-dataset transfer as a benchmark to systematically evaluate a model's robustness and show that MSeg training yields substantially more robust models in comparison to training on individual datasets or naive mixing of datasets without the presented contributions. A model trained on MSeg ranks first on the WildDash-v1 leaderboard for robust semantic segmentation, with no exposure to WildDash data during training. We evaluate our models in the 2020 Robust Vision Challenge (RVC) as an extreme generalization experiment. MSeg training sets include only three of the seven datasets in the RVC; more importantly, the evaluation taxonomy of RVC is different and more detailed. Surprisingly, our model shows competitive performance and ranks second. To evaluate how close we are to the grand aim of robust, efficient, and complete scene understanding, we go beyond semantic segmentation by training instance segmentation and panoptic segmentation models using our dataset. Moreover, we also evaluate various engineering design decisions and metrics, including resolution and computational efficiency. Although our models are far from this grand aim, our comprehensive evaluation is crucial for progress. We share all the models and code with the community.
The High Separation Probability Assumption for Semi-Supervised Learning

We propose a novel semi-supervised learning (SSL) assumption—the high separation probability (HSP) assumption—that is complementary to the common low density separation (LDS) assumption, and a highly accurate and efficient SSL algorithm, the transductive minimax probability machine (TMPM). Under the HSP assumption, a preferred classifier should ensure that with high probability the samples are far away from the decision boundary. Similarly, the LDS assumption states that the decision boundary should lie in a low-density region. A remarkable property of HSP is that it provides an elegant theoretical framework for analyzing the generalization performance of SSL, while it remains difficult for the LDS assumption to have a quantitative criterion on how it fits the data. Moreover, it has the same paradigm as the minimax probability machine (MPM) and yields an intractable mixed-integer optimization for solving the transductive label assignment problem. Consequently, we propose TMPM with a label-switching strategy and iterative manner to formalize our approximate solution. Theoretically, we analyze the computational complexity, convergence property, and generalization error bound of TMPM. Empirically, we show that TMPM achieves competitive performance on a wide range of datasets, while being almost one order of magnitude faster than existing SSL approaches.
WANDS: Dataset for Product Search Relevance Assessment

Search relevance is an important performance indicator used to evaluate search engines. It measures the relationship between users’ queries and products returned in search results. E-commerce sites use search engines to help customers find relevant products among millions of options. The scale of the data makes it difficult to create relevance-focused evaluation datasets manually. As an alternative, user click logs are often mined to create datasets. However, such logs only capture a slice of user behavior in the production environment, and do not provide a complete set of candidates for annotation. To overcome these challenges, we propose a systematic and effective way to build a discriminative, reusable, and fair human-labeled dataset, Wayfair Annotation DataSet (WANDS), for e-commerce scenarios. Our proposal introduces an important cross-referencing step to the annotation process which significantly increases dataset completeness. Experimental results show that this process is effective in improving the scalability of human annotation efforts. We also show that the dataset is effective in evaluating and discriminating between different search models. As part of this contribution, we also released the dataset. To our knowledge, it is the biggest publicly available search relevance dataset in the e-commerce domain.
Learning for Robust Combinatorial Optimization: Algorithm and Application

Learning to optimize (L2O) has recently emerged as a promising approach to solving optimization problems by exploiting the strong prediction power of neural networks and offering lower runtime complexity than conventional solvers. While L2O has been applied to various problems, a crucial yet challenging class of problems — robust combinatorial optimization in the form of minimax optimization — have largely remained under-explored. In addition to the exponentially large decision space, a key challenge for robust combinatorial optimization lies in the inner optimization problem, which is typically non-convex and entangled with outer optimization. In this paper, we study robust combinatorial optimization and propose a novel learning-based optimizer, called LRCO (Learning for Robust Combinatorial Optimization), which quickly outputs a robust solution in the presence of uncertain context. LRCO leverages a pair of learning-based optimizers — one for the minimizer and the other for the maximizer — that use their respective objective functions as losses and can be trained without the need of labels for training problem instances. To evaluate the performance of LRCO, we perform simulations for the task offloading problem in vehicular edge computing. Our results highlight that LRCO can greatly reduce the worst-case cost and improve robustness, while having a very low runtime complexity.
Use of ICT and Autonomous Learning of University Students During the COVID-19 Pandemic

The Covid-19 social distancing measures accelerated the digital transformation in education and presented challenges in both technological and methodological aspects in universities and schools. The main objective of this research was to determine the correlation between ICT use and students' autonomous learning during the COVID-19 pandemic. The research used a basic, quantitative approach and was descriptive and correlational with a non-experimental and cross-sectional design. The sample consisted of 152 engineering students in North Lima private universities. The data collection technique was a survey employing a questionnaire as the instrument. The results showed that the students scored at the average level for the ICT usage variable (52%). They scored at medium-level for the autonomous learning variable (48.0%). The results indicated a moderately significant correlation (0.530) at a significance level of 0.00 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{p} &lt; 0.05)$</tex> between the students' ICT use and their autonomous learning.