Reviewerly/exHarmony huggingface dataset:
Innovation of Financial Management Teaching Mode Based on Big Data

Abstract As we all know, the development of social innovation is inseparable from science and technology. Nowadays, the emergence of Big Data(BD) has a significant impact on society. In this data age, the traditional Teaching Mode(TM) of financial management can not meet the needs of the times, and the teaching of financial management needs to be reformed and innovated. In this context, this paper proposes the financial management professional innovation research based on BD. In order to adapt to the trend of the development of the times, this paper proposes an accurate TM of financial management based on BD. In teaching, we use BD technology to accurately mine students’ learning needs and learning characteristics, and give targeted teaching plans through data analysis and decision-making. In order to verify the effectiveness of the method proposed in this paper, we conducted a control experiment. In the experiment, we selected two classes of students with little difference in financial management major in our school as the experimental objects to carry out the research. After the experiment, we investigated the performance and satisfaction of the two classes of students, and evaluated the teaching scheme and teaching effect of the two classes. The results show that after the experiment, only 40% of the students in the control class use the traditional TM, while 63.33% of the students in the class use the TM proposed in this paper, which is far higher than the control class. It can be seen that the financial management precision TM based on BD proposed in this paper is effective and has great advantages.
Evaluation of a technique to simulate LiDAR image datasets for training a machine learning-based image enhancement algorithm

Acquiring field data for machine learning-based image enhancement techniques can be challenging. These techniques often use a supervised learning architecture that requires pairs of degraded and target images that would be unfeasible to acquire in the field. One alternative approach is to employ simulation models that can accurately capture the unique characteristics of a degraded visual environment (DVE). The fidelity or accuracy of these models determines the effectiveness of the fully trained image enhancement algorithm. This paper explores the benefits and deficits of utilizing simulation software to properly portray underwater LiDAR capture in DVEs. This is accomplished by employing 3DSMAX Studios to generate 3D renderings of underwater targets. The Image Systems Engineering Toolbox for Cameras (ISETCAM) is then used to synthetically generate a training dataset. Subjective and objective metrics are devised to measure the effectiveness of these approaches in training a GAN-based underwater LiDAR image enhancement algorithm.
Self-supervised Learning from Semantically Imprecise Data

Learning from imprecise labels such as animal or bird, but making precise predictions like snow bunting at test time is an important capability when expertly labeled training data is scarce. Contributions by volunteers or results of web crawling lack precision in this manner, but are still valuable. And crucially, these weakly labeled examples are available in larger quantities for lower cost than high-quality bespoke training data. CHILLAX, a recently proposed method to tackle this task, leverages a hierarchical classifier to learn from imprecise labels. However, it has two major limitations. First, it is not capable of learning from effectively unlabeled examples at the root of the hierarchy, e.g. object. Second, an extrapolation of annotations to precise labels is only performed at test time, where confident extrapolations could be already used as training data. In this work, we extend CHILLAX with a self-supervised scheme using constrained extrapolation to generate pseudo-labels. This addresses the second concern, which in turn solves the first problem, enabling an even weaker supervision requirement than CHILLAX. We evaluate our approach empirically and show that our method allows for a consistent accuracy improvement of 0.84 to 1.19 percent points over CHILLAX and is suitable as a drop-in replacement without any negative consequences such as longer training times.
Can You Distinguish Truthful from Fake Reviews? User Analysis and Assistance Tool for Fake Review Detection

Customer reviews are useful in providing an indirect, secondhand experience of a product. People often use reviews written by other customers as a guideline prior to purchasing a product. Such behavior signifies the authenticity of reviews in e-commerce platforms. However, fake reviews are increasingly becoming a hassle for both consumers and product owners. To address this issue, we propose You Only Need Gold (YONG), an essential information mining tool for detecting fake reviews and augmenting user discretion. Our experimental results show the poor human performance on fake review detection, substantially improved user capability given our tool, and the ultimate need for user reliance on the tool.
An improved Henry gas solubility optimisation-based feature selection approach for histological image taxonomy

Classification of histopathological images is one of the important areas of research in the field of medical imaging. However, the complexities available in histopathological images make the classification process difficult. For such complex images, selection of prominent features for image classification is also a challenging task and is still an open research area for computer vision researchers. Therefore, an effective method for the selection of prominent features of images has been introduced in this work. For the same, an improved Henry gas solubility optimisation has been introduced in which a new position update equation has been used to balance the global and local search. The selected features are then input to classifiers to identify histopathological images. For the performance analysis of improved Henry gas solubility optimisation, 23 benchmark functions are used. The proposed feature selection method has been analysed over two datasets, namely breast cancer cell dataset and ICIAR grand challenge dataset. The proposed feature selection method eliminates the maximum 60% average features from both the datasets. To validate usefulness of selected features, results of different classifiers are compared. Experimental results show that the presented method outperforms other methods.
Leveraging synthetic imagery to train deep learning algorithms for the detection of objects of interest in radiant energy imagery

Radiant energy object detection deep learning algorithms require large training sets with site-specific images, often from locations that are difficult to access, while also remaining diverse enough to encourage a robust model. Of particular interest is the detection of buried and partially buried objects which have a widely varying behavior profile dependent on factors such as depth, soil composition, time of day, moisture level, target composition, etc. The variety associated with these variables increases the difficulty of acquiring an adequately diverse data set. Synthetic imagery offers a potential solution to limited accessibility to data as images can be created on demand with diversity limited only by the parameters of the simulation. The goal of this study is to create custom models using SSD (Single Shot MultiBox Detector), YOLOv3 (You Only Look Once), and Faster R-CNN (Region- based Convolutional Neural Networks) to detect buried objects in real images by leveraging synthetic radiant energy imagery. Custom training is done on a synthetic data set (made in-house) using pre-trained models from Tensor ow's model zoo and ImageAI's YOLOv3 pre-trained model. Model training leverages high performance computing (HPC) resources and utilizes GPU to optimize training speed. Proof-of-concept models for SSD, YOLOv3, and Faster R-CNN have been trained on preliminary synthetic imagery and analyzed. Preliminary results for these models will be discussed.
Adversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus.

Cross-target generalization constitutes an important issue for news Stance Detection (SD). In this short paper, we investigate adversarial cross-genre SD, where knowledge from annotated user-generated data is leveraged to improve news SD on targets unseen during training. We implement a BERT-based adversarial network and show experimental performance improvements over a set of strong baselines. Given the abundance of user-generated data, which are considerably less expensive to retrieve and annotate than news articles, this constitutes a promising research direction.
Pembelajaran Menulis Cerpen dengan Menggunakan Metode Sugesti-Imaji pada Siswa Kelas XI SMK PGRI Subang

Basically everyone is capable of writing, it's just that writing skills need to be trained on an ongoing basis. Apart from needing to be trained on an ongoing basis, the selection of learning methods also needs to be considered, considering that students' ability to write can be categorized as still weak. The learning method used in this research is the suggestion-imagination method. The formulation of the problems in this study are: (1) How is the ability of class XI students of SMK PGRI Subang in writing short stories? (2) How is the ability of class XI students of SMK PGRI Subang in writing short stories using the suggestion-imagination method? (3) How to improve the ability of class XI students of SMK PGRI Subang in learning to write short stories using the suggestion-imagination method? The main objective in this study is how to improve student learning outcomes after using the suggestion-imagination method. The research method used was pre-experimental with a research design in the form of experimental one group pretest-posttest design. The research was conducted at SMK PGRI Subang with the research subjects of class XI TKJ 1 as the experimental class. Sampling in this study using purposive sampling technique or sample aims. Based on statistical data with the help of the SPSS 23 program, the initial test average value was 56.54 and the final test obtained an average value of 75.28. Based on these calculations, it can be seen that there is an increase in the average value of the posttest compared to the pretest before using the suggestion-imaginary method. Thus the use of the suggestion-imagination method in learning to write short stories is able to provide significant changes and this is proven to be effectively applied in learning to write short stories.
[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias

Singh et al. (2020) point out the dangers of contextual bias in visual recognition datasets. They propose two methods, CAM-based and feature-split, that better recognize an object or attribute in the absence of its typical context while maintaining competitive within-context accuracy. To verify their performance, we attempted to reproduce all 12 tables in the original paper, including those in the appendix. We also conducted additional experiments to better understand the proposed methods, including increasing the regularization in CAM-based and removing the weighted loss in feature-split. As the original code was not made available, we implemented the entire pipeline from scratch in PyTorch 1.7.0. Our implementation is based on the paper and email exchanges with the authors. We found that both proposed methods in the original paper help mitigate contextual bias, although for some methods, we could not completely replicate the quantitative results in the paper even after completing an extensive hyperparameter search. For example, on COCO-Stuff, DeepFashion, and UnRel, our feature-split model achieved an increase in accuracy on out-of-context images over the standard baseline, whereas on AwA, we saw a drop in performance. For the proposed CAM-based method, we were able to reproduce the original paper's results to within 0.5$\%$ mAP. Our implementation can be found at this https URL
Ontology Clustering with OWL2Vec

In this work we present an exploratory study to apply OWL2Vec* to drive the clustering of ontology entities (i.e., ontology clustering). OWL2Vec* is a state-of-the-art system that creates embeddings, capturing the semantics of both entities and tokens that appear in the ontology.
Cybersecurity enhancement using recurrent neural networks and keystroke dynamics

Three-factor authentication is the best available option for cybersecurity enhancement, which includes ‘know’ (password), ‘have’ (username, token, or card) and ‘are’ (biometrics). Each one makes this process stronger and more secure. Imagine an international team working on a long-term project by remotely logging into a secured server. In such a context, adding keystroke biometrics to authentication will definitely improves the cybersecurity. We propose to develop a set of recurrent neural network (RNN) models of utilizing keystroke dynamics as ones’ biometrics to enhance cybersecurity. Keystroke dynamics refers to the process of measuring and assessing human’s typing rhythm on digital devices. Keystroke timing information such as di-graph, dwell time and flight time are used in our experimental datasets. We propose to apply support vector machine and recurrent neural network to keystroke dynamics. Experimental results show the proposed methods are promising in contrast with traditional methods like nearest neighbor and Manhattan distance.
Model of Peak Discharge Reduction Using Side Channel

Special Region of Jakarta Capital as downstream area of Ciliwing river encounter overflow of the river anually. The watershed area start from upstream to downstream of the Ciliwung river had encounter degradation, from landuse to the riverbank of Ciliwung river had more then 20% degradation. Ciliwung river devided into 2 area, upstream Ciliwung, middle Ciliwung area and downstream Ciliwung. This research was done in the middle section of the Ciliwung river, starting from Katulampa Weir until the bridge of Ciliwung Kepala Dua. This research purpose are to reduce the peak of the hydrograph discharge graphic in the middle area by installing Side channel with the capacity of 96, 956 m3 with the flood discharge of 538.64 m3 /sec. the analysis of the research using Nakayassu hydrograph and simulated using iRIC software. Instalation of side channel are based on the elevation of the area and the potency of a green area in the middle area therefore the Kalo Baru 2, Sugutamu, Cikumpa and Ciparigi area are choosen. Declining of the Nakayussu hydrograph graphic are 12 - 15% with the callibration using iRIC Software simulation shows 14.76% and deceleration of the flow time until 15,200 second. The acquired mathematic equation are Y = 4.723X1+0.00005X2+62.925 with Y as the declining of the flood discharge, X1 as the number of side channel and X2 as the volume of the reservoirs.
Assessing the need for a wearable sign language recognition device for deaf individuals: Results from a national questionnaire

The purpose of this study was to determine how sign language users perceive the sign language recognition (SLR) field, with a focus on gaining perspectives from members of the Canadian Deaf community. A questionnaire consisting of a series of rating and open-ended questions was used to gather perspectives and insights related to a hypothetical SLR device. The survey was distributed to members of the Deaf community, family and friends of Deaf individuals, and service providers, all of whom had some proficiency in American Sign Language (ASL). The average ratings provided by Deaf participants were distributed normally with a right-modal skew in the direction of the positive ratings. Six fundamental concerns about SLR technologies were identified from participants' responses, with the most frequently cited pertaining to the technology's feasibility. In descending order, participants ranked translation accuracy, speed, and comfort as the three most important design characteristics for potential SLR devices. Respondents identified many potential situations in which SLR devices could be used. For a SLR device to be user-centric and culturally appropriate, it is essential that future work in the field integrates perspectives from members of the Deaf community.
Word Embeddings for Constructive Comments Classification

Abstract Word embeddings are so significant today that it is common to see their application in multiple natural language tasks. Indeed, word embeddings as the first layer of a deep learning model are widely adopted and they can also be found in multiple natural language tasks such as classification of texts and named entity recognition. The focus of this paper is the identification of constructive online comments through the use of dense vector semantics such as word embeddings. We specifically explore two approaches: learning distributed word representations and the use of a pre-trained word embedding model. We evaluate these word embedding methods on a recently created constructive comments corpus comprised of 12,000 annotated news comments, intended to improve the quality of online discussions. The obtained results show how the performance of complicated architectures like recurrent and convolutional neural networks can be matched by a language model based on learning embeddings.
The HashiCorp Ecosystem

Since its inception in 2012, HashiCorp’s primary mission has been to change how cloud applications are deployed and released.
Right to Privacy and The Personal Data Protection Bill of 2019: A Critique

Right to privacy, was set in stone on 24th August 2017 as a fundamental right by Hon’ble the Supreme Court of India. It was a widely celebrated judgment and was hailed as one of the vanguards of human rights in faster than ever technological advancements. It was in the course of the hearing, Union Government placed before the Bench that a committee was being constituted in order to place a framework for the protection of the right to privacy, and data protection laws, under the chairmanship of Justice B N Srikrishna, a former Supreme Court judge. On the recommendation of that committee, a bill was introduced in Lok Sabha on 11th December 2019. This paper aims to critically analyse the bill in the background of the right to privacy, writing the critique of relevant provisions of the said bill and suggest necessary changes which should be made to the bill, before it is voted upon. This paper does so while engaging in the contents of a webinar, wherein the Justice Srikrishna gave some insights into the bill, and suggests that some not so desirable changes were made before it was tabled before Lok Sabha.
Cognitive Perspectives on Context-based Decisions and Explanations

When human cognition is modeled in Philosophy and Cognitive Science, there is a pervasive idea that humans employ mental representations in order to navigate the world and make predictions about ou ...
The Inescapable Duality of Data and Knowledge.

We will discuss how over the last 30 to 50 years, systems that focused only on data have been handicapped with success focused on narrowly focused tasks, and knowledge has been critical in developing smarter, intelligent, more effective systems. We will draw a parallel with the role of knowledge and experience in human intelligence based on cognitive science. And we will end with the recent interest in neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining data-intensive statistical AI systems with symbolic AI systems which results in more capable AI systems that support more human-like intelligence.
A Practical Study on the Reform of Safety Education under the Modern Vocational Education System

The modern education system has the characteristic such as occupational and crossover, do a good job in security education reform under the modern education system is the fundamental social applied high quality talent cultivation, safety education reform also penetration in the vocational education of the work, based on the connotation of the modern vocational education system as the main line, mainly analyzes the specific meaning of the safety education reform, put forward security strategy, through the concrete practice of the education reform of for your reference.
PEMANFAATAN MUSIK KLASIK DALAM PEMBELAJARAN MENULIS KALIMAT EFEKTIF PADA SISWA SMA

AbstrakPembelajaran menulis termasuk pembelajaran yang mampu mengemukakan ide-ide dalam pikiran yang memerlukan kemampuan rumit yang ada dalam otak. Para siswa masih menemui kendala dalam mengemukakan pikiran mereka kedalam tulisan. Hal ini memerlukan proses yang dilatih dengan baik. Namun menulis masih menjadi kemampuan yang sulit bagi siswa. Hal ini disebabkan proses belajar yang belum optimal dan media yang kurang tepat. Penelitian ini dilakukan pada siswa SMA Attaqwa kelas X IPS kabupaten Bekasi. Siswa yang menjadi responden sebanyak 34 siswa. Metode penelitian yang digunakan adalah metode eksperimen. Hasil penelitian para siswa dapat memperbaiki kemampuan menulis kalimat efektif dan proses belajar lebih dinamis dan efektif. Saran dalam penelitian ini adalah agar para guru memberikan pembelajaran yang dinamis dan tidak monoton. Variasi pembelajaran diperlukan agar pembelajaran berjalan dengan riang dan gembira.Kata Kunci : Musik klasik,Menulis, kalimat efektifAkbstracLearning to write includes learning that is able to express ideas in the mind that require complex abilities in the brain. The students still had problems expressing their thoughts in writing. This requires a well-practiced process. However, writing is still a difficult skill for students. This is due to the learning process that has not been optimal and the media is not quite right. This research was conducted on Attaqwa high school students class X IPS Bekasi district. Students who became respondents were 34 students. The research method used is the experimental method. The results of the students' research can improve the ability to write effective sentences and the learning process is more dynamic and effective. The suggestion in this research is that teachers provide dynamic learning and not monotonous. Variations in learning are needed so that learning runs cheerfully and happily.Keywords: classical music, writing, effective sentences
The Benefits of RDF and External Ontologies for Heterogeneous Data: A Case Study Using the Japanese Visual Media Graph

Across numerous fan created and curated websites, there exists a wealth of semantically rich descriptive data for a variety of Japanese visual media, such as anime, manga, and video games. The amount and granularity of these data makes it valuable for domain researchers, but the semantic heterogeneity and lack of interconnectedness makes analysis cumbersome. Seeking to address this issue, the Japanese Visual Media Graph ( JVMG ) project aims to create a type of global database built using RDF and external ontologies to better enable data-driven research in the domain. We discuss the benefits this approach has when compared to the local relational databases used at the fan-sites, such as enabling the easy creation of aggregate resources using data from multiple providers, and the ability to constantly update and alter a schema over time. This encourages the incorporation of additional data in the future, while still maintaining rich data semantics and provenance. While many of the benefits are discussed in the context of the JVMG project or the Japanese visual media domain, we discuss how this database approach may be similarly advantageous to other projects that seek to create aggregate resources, or collect heterogeneous data from diverse sources.