Reviewerly/exHarmony huggingface dataset:
Detecting red flags of corporate financial statement frauds using Beneish m score model in Sri Lanka

Financial statement fraud is one of the major concerns in the modern business world. Detecting financial statement fraud is very challenging task and requires thorough knowledge about the nature of fraud, how it can be committed and concealed. The purpose of the study is to identify the red flags of financial statement frauds applying Benish M Score model. It is found that, in each year (2013-2019) considered for the study, there are companies with a Beneish M Score of greater than -2.22, which can be gauged for possible manipulations on those financial statements. Paired sample t-test analysis is showen that, DSRI (Days’ Sales in Receivables Index), GMI (Gross Margin Index), SGI (Sales Growth Index) LVGI (Leverage Index), TATA (Total Accruals to Total Assets) out of eight ratios occupied in the Beniesh model are significantly different between manipulated and non-manipulated firms. Therefore, it is recommended that Benish M Score model can be used as a risk assessment tool to recognize the red flags of possible fiancial statemet frauds in business entities in Sri Lanka. This paper aims at broadening knowledge of External Auditors, Forensic Accountants, Accountants, Senior Managers, Regulators and other stakeholders in detecting red flags of financial statement frauds in Sri Lanka.
Unsupervised Multi-Target Domain Adaptation for Acoustic Scene Classification

It is well known that the mismatch between training (source) and test (target) data distribution will significantly decrease the performance of acoustic scene classification (ASC) systems. To address this issue, domain adaptation (DA) is one solution and many unsupervised DA methods have been proposed. These methods focus on a scenario of single source domain to single target domain. However, we will face such problem that test data comes from multiple target domains. This problem can be addressed by producing one model per target domain, but this solution is too costly. In this paper, we propose a novel unsupervised multi-target domain adaption (MTDA) method for ASC, which can adapt to multiple target domains simultaneously and make use of the underlying relation among multiple domains. Specifically, our approach combines traditional adversarial adaptation with two novel discriminator tasks that learns a common subspace shared by all domains. Furthermore, we propose to divide the target domain into the easy-to-adapt and hard-to-adapt domain, which enables the system to pay more attention to hard-to-adapt domain in training. The experimental results on the DCASE 2020 Task 1-A dataset and the DCASE 2019 Task 1-B dataset show that our proposed method significantly outperforms the previous unsupervised DA methods.
Text-Conditioned Character Segmentation for CTC-Based Text Recognition

Segmentation-free text recognition achieves successful performance because it can accurately recognize overlapping and touching characters, which is difficult for over-segmentation approaches. In contrast, character segmentation is helpful for explanations, posterior document layout analysis, and other applications. Some methods have been proposed to balance the capability of character segmentation and accurate recognition, but they cannot predict segmentation candidates for characters that can be differently segmented, which bottlenecks segmentation accuracy. In this paper, we propose Text-conditioned Character Segmentation (TCSeg) to improve segmentation accuracy. TCSeg segments characters differently according to each text candidate prediction by segmentation-free text recognition without affecting recognition accuracy. We also propose Overlap and Skip Error Suppression (OSESup) to suppress unintuitive errors using the estimated segmentation. An experiment on text recognition of handwritten Chinese characters shows that TCSeg segments characters more accurately than an existing segmentation method and that OSESup improves the recognition accuracy.
Evaluating a Tool for Creating Bug Report Assignment Recommenders (S)

Large software development projects that use bug tracking systems can become overwhelmed by the number of reports filed.To assist in reducing the workload of project members, researchers have proposed the use of bug report assignment recommenders.To assist project members with the creation of assignment recommenders, we proposed a web-based tool called the Creation Assistant for Supporting Triage Recommenders (CASTR).This paper presents the results of both a laboratory and field study of CASTR.We found that CASTR can create assignment recommenders with accuracy as high as 95%, 80%, and 70% for Top-1, Top-3 and Top-5, respectively.The field study showed that 60% of the participants found CASTR easy to use, whereas the remaining participants found CASTR moderately or slightly easy to use.
Dynamic Instance-Wise Classification in Correlated Feature Spaces

In a typical supervised machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training. However, using a different subset of features that is most informative for each test instance individually may improve not only the prediction accuracy but also the overall interpretability of the model. At the same time, feature selection methods for classification have been known to be the most effective when many features are irrelevant and/or uncorrelated. In fact, feature selection ignoring correlations between features can lead to poor classification performance. In this work, a Bayesian network is utilized to model feature dependencies. Using the dependence network, a new method is proposed that sequentially selects the best feature to evaluate for each test instance individually and stops the selection process to make a prediction once it determines that no further improvement can be achieved with respect to classification accuracy. The optimum number of features to acquire and the optimum classification strategy are derived for each test instance. The theoretical properties of the optimum solution are analyzed, and a new algorithm is proposed that takes advantage of these properties to implement a robust and scalable solution for high-dimensional settings. The effectiveness, generalizability, and scalability of the proposed method are illustrated on a variety of real-world datasets from diverse application domains. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact statement—</i> The ability to rationalize which features to use to classify each data instance is of paramount importance in a wide range of application domains, including but not limited to medicine, criminal justice, and cybersecurity. Correlations between features, and the need for variable selection at the same stage as classification, in such application domains present additional challenges to machine learning related to classification accuracy and computationally intractability. The proposed framework presents, to the best of our knowledge, the first practical solution that balances between classification accuracy and sparsity at the instance level, by dynamically choosing the most informative features, relative to each instance, from a set of potentially <i>correlated</i> features, for classifying each <i>individual</i> instance. The proposed framework achieves reductions up to 82% in the average number of features used by state-of-the-art methods without sacrificing accuracy and is robust for up to 10% of missing features. Broad positive societal implications include: fast, accurate, and cost-efficient inference in complex dynamic settings; and ease of interpretation of and trust in machine learning outcomes by domain experts (e.g., doctors and lawyers).
New self-dual codes from $ 2 \times 2 $ block circulant matrices, group rings and neighbours of neighbours

<p style='text-indent:20px;'>In this paper, we construct new self-dual codes from a construction that involves a unique combination; <inline-formula><tex-math id="M1">\begin{document}$ 2 \times 2 $\end{document}</tex-math></inline-formula> block circulant matrices, group rings and a reverse circulant matrix. There are certain conditions, specified in this paper, where this new construction yields self-dual codes. The theory is supported by the construction of self-dual codes over the rings <inline-formula><tex-math id="M2">\begin{document}$ \mathbb{F}_2 $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M3">\begin{document}$ \mathbb{F}_2+u \mathbb{F}_2 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M4">\begin{document}$ \mathbb{F}_4+u \mathbb{F}_4 $\end{document}</tex-math></inline-formula>. Using extensions and neighbours of codes, we construct <inline-formula><tex-math id="M5">\begin{document}$ 32 $\end{document}</tex-math></inline-formula> new self-dual codes of length <inline-formula><tex-math id="M6">\begin{document}$ 68 $\end{document}</tex-math></inline-formula>. We construct 48 new best known singly-even self-dual codes of length 96.
SUSTAINABLE DEVELOPMENT IN EDUCATION – AUTOMATING CURRICULUM ASSESSMENT

The perpetual need for developing a sustainable economic environment places the education policies at the foundation of social adaptability. Creating and maintaining curriculum content that meets the demands of a continuously changing society, and the challenges that such a rapid evolution put on the labour market, is one of the top priorities for any education system and institution involved in education at any level. This paper proposes a cognitive computing solution for assessing, in a programmatic manner, large corpora of curriculum content created by teachers from lower secondary education environment for Informatics instruction in Romanian schools. The result of this initiative at the national level is corpora of curricular content that must be evaluated to verify the degree to which the material meets the requirements of the national curriculum. We addressed this crucial yet tedious process by designing and implementing a solution for automating curriculum assessment through cognitive computing. The paper outlines a sustainable framework to evaluate curriculum content in an automated fashion, and for providing critical feedback timely to both content creators, and to policy makers responsible for creating economically viable and future adaptable education strategies.
Measuring performances and footprint of blockchains with BCTMark: a case study on Ethereum smart contracts energy consumption

A rich ecosystem of blockchain-based projects has emerged since the introduction of Bitcoin in 2008. New protocols seek to improve the performances of blockchain systems. In particular, the energy consumption of blockchains has been particularly decried. Unfortunately, those new proposals are often evaluated with ad hoc tools and experimental environments. Therefore, reproducibility and comparison of these new contributions with the state of the art of blockchain technologies are complicated. To the best of our knowledge, only a few tools partially address the design of a generic benchmarking of blockchain technologies (e.g., load generation). This paper introduces BCTMark, a generic framework for benchmarking blockchain technologies on an emulated network in a reproducible way. Based on this novel framework, we studied a key aspect of modern blockchains’ energy consumption: smart-contract execution. Based on experiments and the analysis of one year of real-world Ethereum transactions, we measured and modeled smart-contracts’ energy consumption on Ethereum. Furthermore, this study details how the replication of contract calls execution can impact their energy cost. In particular, we give insights on the energy consumed by smart-contracts on Ethereum over one year.
Exploring the association between occupational complexity and numeracy

Abstract The basic cognitive skill of numeracy is a recognized form of human capital, associated with economic and social well being for individuals and for nations. In this study, we explore how occupational complexity relates to proficiency in numeracy, among adults in full-time employment. We operationalize occupational complexity by constructing three measures of task complexity: complexity with data, complexity with people and complexity with things. Data from the international OECD survey of adult skills, 2012, is employed to investigate both the distribution of these three dimensions of occupational task complexity and how these relate to numeracy in 13 countries. The analysis indicates that data occupational complexity predicts numeracy scores, when controlling for age, gender and educational level. The findings open for a hypothesis that occupational activities may enhance basic skills in adult populations. If elaborated and supported through further studies this finding has practical implications for workplace organization and contributes to theoretical understandings of the development of basic skills in adults.
Summary of the 2021 Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries

The 2021 embedded deep learning object detection model compression competition for traffic in Asian countries held in IEEE ICMR2021 Grand Challenges focuses on the object detection technologies in autonomous driving scenarios. The competition aims to detect objects in traffic with low complexity and small model size in the Asia countries (e.g., Taiwan), which contains several harsh driving environments. The target detected objects include vehicles, pedestrians, bicycles and crowded scooters. There are 89,002 annotated images provided for model training and 1,000 images for validation. Additional 5,400 testing images are used in the contest evaluation process, in which 2,700 of them are used in the qualification stage competition, and the rest are used in the final stage competition. There are in total 308 registered teams joining this competition this year, and the top 15 teams with the highest detection accuracy entering the final stage competition, from which 9 teams submitted the final results. The overall best model belongs to team "as798792", followed by team "Deep Learner" and team "UCBH." Two special awards of best accuracy award best and bicycle detections go to the same team "as798792," and the other special award of scooter detection goes to team "abcda."
Early Detection of Post-Surgical Complications using Time-series Electronic Health Records.

Models predicting health complications are increasingly attempting to reflect the temporally changing nature of patient status. However, both the practice of medicine and electronic health records (EHR) have yet to provide a true longitudinal representation of a patient's medical history as relevant data is often asynchronous and highly missing. To match the stringent requirements of many static time models, time-series data has to be truncated, and missing values in samples have to be filled heuristically. However, these data preprocessing procedures may unconsciously misinterpret real-world data, and eventually lead into failure in practice. In this work, we proposed an augmented gated recurrent unit (GRU), which formulate both missingness and timeline signals into GRU cells. Real patient data of post-operative bleeding (POB) after Colon and Rectal Surgery (CRS) was collected from Mayo Clinic EHR system to evaluate the effectiveness of proposed model. Conventional models were also trained with imputed dataset, in which event missingness or asynchronicity were approximated. The performance of proposed model surpassed current state-of-the-art methods in this POB detection task, indicating our model could be more eligible to handle EHR datasets.
Fast two-step phase-shifting method for measuring the three-dimensional contour of objects

A two-step phase-shifting method is proposed. The method realized the rapid measurement of the three-dimensional (3D) contour of some objects using mean envelope denoising based on the striped structured light. First, Butterworth low-pass pre-filtering in the frequency domain was performed on the two deformed fringe images containing the height information of the objects. It reduced the influence of higher harmonics. Then the peak and valley values of fringes with sub-pixel accuracy were obtained through numerical interpolation and point-by-point comparison. Furthermore, the mean envelope was accurately obtained and removed. After removing the mean envelope, the rectangular low-pass filtering was used to thoroughly filter out the remaining background signal. Finally, the fast reconstructions of the 3D contour of the objects were realized. The 3D contour reconstructions of the objects were simulated and experimentally measured using the method proposed. The calculation speed and accuracy were compared with the traditional four-step phase-shifting method and other two-step phase-shifting methods. The method has the advantages of fast calculation speed and high calculation accuracy. It is a valuable method for quickly measuring the 3D contour of some objects.
A Behavior-Aware Graph Convolution Network Model for Video Recommendation

Interactions between users and videos are the major data source of performing video recommendation. Despite lots of existing recommendation methods, user behaviors on videos, which imply the complex relations between users and videos, are still far from being fully explored. In the paper, we present a model named Sagittarius. Sagittarius adopts a graph convolutional neural network to capture the influence between users and videos. In particular, Sagittarius differentiates between different user behaviors by weighting and fuses the semantics of user behaviors into the embeddings of users and videos. Moreover, Sagittarius combines multiple optimization objectives to learn user and video embeddings and then achieves the video recommendation by the learned user and video embeddings. The experimental results on multiple datasets show that Sagittarius outperforms several state-of-the-art models in terms of recall, unique recall and NDCG.
Fast Text vs. Non-text Classification of Images

We propose a fast method for classifying images as containing text, or with no scene text. The typical application is in processing large image streams, as encountered in social networks, for detection and recognition of scene text. The proposed classifier efficiently removes non-text images from consideration, thus allowing to apply the potentially computationally heavy scene text detection and OCR on only a fraction of the images.
Changes in studying at Russian universities in the context of the COVID-19 pandemic

The relevance of the study is due to the contradiction between the massive transition of Russian universities to a distance learning format and the insufficient readiness of participants in the educational process in the conditions of the COVID-19 pandemic. The situation with distance education before the pandemic was analyzed and the prerequisites for the emergence of problems and difficulties in the transition to online education were identified. An analysis of the difficulties faced by teachers and students of Russian higher educational institutions enabled to identify objective and subjective factors of their occurrence, as well as to develop recommendations for the further continuation of distance learning in combination with the traditional learning format. The purpose of the study is to analyze modern work experience in the conditions of the COVID-19 pandemic using the example of working with students of the Plekhanov Russian University of Economics. The analysis of successes and problems is conducted; the contradictions between distance and traditional forms of education are shown. The main models of organizing the studies are considered, which imply traditional and remote formats of studying using distance educational technologies. The works of various researchers, both Russian and foreign, on the problem of distance learning, are also analyzed. As a result of the study, it was concluded that in the modern education system of Russian higher educational institutions, distance education can be considered as a complementary and reinforcing form of the already existing traditional (“face to face”) training format with its socio-pedagogical, organizational, and psychological-didactic potential. In the work, the methods of theoretical (analysis, synthesis, systematization, comparison) and empirical (study of literature, interview, questionnaire, observation) research were used in a comprehensive manner. The combination of forms of interaction in real and virtual space in learning in modern didactics should be considered as a general didactic principle of learning.
Construction of Wireless Underground Footwork Mobile Training and Monitoring Sensor Network in Venues of Major Sports Events

At the Summer Olympics in Tokyo, technology was used extensively in major sports events. The level of foot movement ability greatly affects the performance of sports technology. Modern sports are developing in the direction of high speed, high skills, flexibility, and rapidity, and more and more reflect the important position of reasonable and accurate foot movement ability in sports. This article uses wireless sensor technology and wireless communication technology to design the overall architecture of the wireless underground footwork mobile training and monitoring network in venues of major sports events. According to the determined monitoring parameters and data transmission plan, a wireless remote monitoring data acquisition system is designed, and the hardware design, software design, and networking of the wireless monitoring node are completed, so as to realize the real-time monitoring and remote transmission of the underlying data. This paper proposes a wireless sensor network management architecture and method based on multiagent cooperation and combines active and passive wireless underground footwork mobile training and monitoring for experimental verification. A multitask allocation strategy optimized for network working life is proposed. A genetic algorithm is used to model and optimize the task data report routing of cluster head nodes. The simulation experiment results show that the wireless sensor network management method based on multiagent cooperation can effectively coordinate different monitoring sensor nodes to complete the assigned monitoring tasks; the multitask assignment strategy based on a genetic algorithm can optimize the working life of the application network.
Spirit Distillation: A Model Compression Method with Multi-domain Knowledge Transfer

Recent applications pose requirements of both cross-domain knowledge transfer and model compression to machine learning models due to insufficient training data and limited computational resources. In this paper, we propose a new knowledge distillation model, named Spirit Distillation (SD), which is a model compression method with multi-domain knowledge transfer. The compact student network mimics out a representation equivalent to the front part of the teacher network, through which the general knowledge can be transferred from the source domain (teacher) to the target domain (student). To further improve the robustness of the student, we extend SD to Enhanced Spirit Distillation (ESD) in exploiting a more comprehensive knowledge by introducing the proximity domain which is similar to the target domain for feature extraction. Persuasive experiments are conducted on Cityscapes semantic segmentation with the prior knowledge transferred from COCO2017 and KITTI. Results demonstrate that our method can boost mIOU and high-precision accuracy by 1.4% and 8.2% respectively with 78.2% segmentation variance, and can gain a precise compact network with only 41.8% FLOPs.