Reviewerly/exHarmony huggingface dataset:
Kern: A Labeling Environment for Large-Scale, High-Quality Training Data

The lack of large-scale, high-quality training data is a significant bottleneck in supervised learning. We introduce kern, a labeling environment used by machine learning experts and subject matter experts to create training data and find manual labeling errors powered by weak supervision, active transfer learning, and confident learning. We explain the current workflow and system overview and showcase the benefits of our system in an intent classification experiment, where we reduce the labeling error rate of a given dataset by an absolute 4.9% while improving the F $$_1$$ score of a baseline classifier by a total of 9.7%.
A Parallel Declarative Framework for Mining High Utility Itemsets

One of the most active research topics in data mining is pattern discovery involving the well-known task of enumerating interesting patterns from databases. The problem of mining high utility itemsets is to find the set of items with the highest utility values based on a given minimum utility threshold. However, due to the advancement of big data technologies, finding all itemsets is much more harder due to the huge number of patterns and the large required resources. Parallel processing is an effective way to efficiently address the problem of mining patterns from large databases. Based on classical propositional logic, we propose in this paper a parallel method to handle efficiently the problem of discovering high utility itemsets from transaction databases. To do this, a decomposition technique is used to splitting the original problem of mining high utility itemsets into smaller and independent sub-problems that can be handled easily in a parallel manner. Then, empirical evaluations on different real-world datasets show that the proposed method is very efficient while being flexible enough to handle additional user constraints when discovering closed high utility itemsets.
A language independent approach to multilingual document representation including Arabic

Arabic language is of increasing interest in the field of Multilingual Information Retrieval (MIR). We deal in this work with the problem of multilingual document representation including Arabic. The proposed approach combines a surface analysis and a Latent Semantic Analysis (LSA) algorithm in a new way to break down the terms of LSA into units which correspond more closely to morphemes. These morphemes are the variable length character N-gram candidates extracted from different fragments separated by borders. The length of the character N-gram candidates is variable because each language has its own properties. This strategy brings an interesting performance for languages such as Arabic in which the words are not explicitly defined and different words are not separated by spaces. The obtained results are encouraging and variability shows that they are perfectible.
Noise in Spectral Music

The experience of computer music grounded in information theory introduces a kernel aspect for spectral music: a means for the control of processes of transformation, which express the formal organization of a work, according to a specific degree of predictability or in Grisey’s term ‘pre-audibility’. The question of new language, new syntax and new rules which permeates the theoretical horizon of the first spectral generation will inform many composers of later generations and will emerge in various forms in their music and theoretical writings. The saturated energy is not oriented towards dissipation, but, on the contrary, it transforms into a generative, iterative gesture by double bass and trombone, from which the opening low E harmonic spectrum of Partiels grows. From a historical distance, the early spectral period appears as an opening towards a new horizon of an ‘age of timbre’, grounded in scientific research on acoustics and psychoacoustics.
Investigation of geochemical characteristics and hydrothermal alteration zone mapping supported by remote sensing of a porphyry copper prospect, southeastern Iran: integrated applications to exploration

The area discussed in this exploration methodology case study is situated along the Sistan Suture Zone where the granitoid suite is a mantle-derived multiphase intrusive complex. One of the characteristics of this region is the presence of large areas of sulfide-bearing, quartz-rich stockwork and pyritic veins. Geochemical findings show that it is limited to the calc-alkaline to shoshonitic series intrusions and is associated with a volcanic arc (I-type) formed within an active continental margin subduction setting. The associated intrusive complex has characteristics consistent with Cu productive porphyries, supported by high K-adakitic Sr/Y, La/Yb, Y and Al 2 O 3 /TiO 2 geochemical signatures. The stockwork mineralization includes the hypogene (chalcopyrite and bornite), with locally superimposed supergene (covellite, malachite, goethite and hematite) zones. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images (visible-near infrared (VNIR), and shortwave infrared (SWIR) bands) and mixture tuned matched filtering (MTMF) algorithm enable identification of each hydrothermal alteration type, especially where pictures were innovatively classified using a concentration–number (C–N) fractal method. Four alteration types are evident (i.e. phyllic, argillic and propylitic), as well as secondary (supergene) jarosite, that are associated with gossans, which are an indicator of the hypogene pyritic shell. The propylitic alteration envelopes the phyllic and argillic varieties, forming a belt around the pyritic shell; alteration assemblages were confirmed by X-ray powder diffraction (XRD) analysis. Finally, all results show a mineralization alteration pattern within the case study region that is similar to those of known porphyry copper and associated molybdenum- and gold-bearing systems in this region of Iran and worldwide.
Calculational Proofs

chapter Share on Calculational Proofs Author: Vladimir Lifschitz View Profile Authors Info & Claims Edsger Wybe Dijkstra: His Life,Work, and LegacyJuly 2022 Pages 231–246https://doi.org/10.1145/3544585.3544598Published:13 July 2022Publication History 0citation1DownloadsMetricsTotal Citations0Total Downloads1Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Edsger W. Dijkstra as a Lecturer in Marktoberdorf

chapter Share on Edsger W. Dijkstra as a Lecturer in Marktoberdorf Author: Manfred Broy View Profile Authors Info & Claims Edsger Wybe Dijkstra: His Life,Work, and LegacyJuly 2022 Pages 493–510https://doi.org/10.1145/3544585.3544616Published:13 July 2022Publication History 0citation1DownloadsMetricsTotal Citations0Total Downloads1Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Instance-based dynamic label assignment for object detection

Existing label assignment strategies have achieved promising performance for providing learning targets, typically rely on statistical information and location characteristics. However, classical label assignment strategies usually lack a comprehensive quality evaluation criterion. As a result, the quality of positive samples is not reliable, which weakens the performance of detectors. We propose a dynamic label assignment strategy to provide higher quality positive samples for detection models. Specifically, we propose a quality assessment criteria of candidate samples, which uses a joint representation guided by intersection over union (IoU). Consequently, the supervised information of both branches is included using only one metric. Beside, we propose a partitioning approach to eliminate local redundant sampling, allowing the selected positive sample points to focus more on the overall information of the target. Tests on the COCO dataset show that our work improves the baseline by 2.2% AP without additional modeling and supervisory information. In addition, extensive experiments on the MS COCO test-dev dataset using different backbones demonstrate that our best model outperforms most of the existing representative methods.
A Design and Application of Software Liberal Arts Course based on CT-CPS Model for Developing Creative Problem-Solving Ability and Learning Motivation of Non-software Majors

As the importance of computing education for nurturing computational thinking skills is emphasized in preparation for the era of the 4th industrial revolution, computing education for non-majors is also expanding in liberal arts education at universities. In this study, a software liberal arts course based on the CT-CPS model was designed and applied to non-software majored university students, and the effect on creative problem-solving ability and learning motivation were analyzed. The CT-CPS (computational thinking-based creative problem solving) model is an instructional model devised by fusing each element of computational thinking ability to the creative problem-solving stages. Creative problem-solving ability test paper and learning motivation test paper were used as test tools. Moreover, quantitative analysis through independent sample t-test and paired sample t-test and qualitative analysis through subjective responses were conducted. As a result of the study, it was verified that the software class applied with the CT-CPS model had a statistically significant effect on the creative problem-solving ability and learning motivation of non-software majors. In particular, compared to the control group, the experimental group showed significant changes in the motivational elements among the sub-factors of creative problem-solving ability and the self-efficacy factor among the sub-factors of learning motivation. In addition, it was confirmed through qualitative analysis that the software class to which the CT-CPS model was applied helped develop the problem-solving ability and learning motivation based on computational thinking through the process of discovering and solving problems on their own in real life.
A Method for Research in Korean History - Focusing on a Quantitative Analysis of Digital History -

Since around 2000 years, plenty of historical materials have been digitized in various formats in the field of Korean history. Due to the structural transition from the era with limited access to an era of infinite information access, Korean history research faces challenges in many ways. The purpose of this paper is to find a way to actively cope with the challenges Korean history research faces in the big data and the digital era. It is intended to present a new research paradigm that converges digital technology while maintaining the value of existing qualitative research in Korean history research. This paper presents the quantitative research methodology based on digital history that the AIRG(Ajou Interdisciplinary Research Group) has tried so far as called it ‘digital history’.
Design of Data Interchange Regulation for Regional ICT Office

The rapid development of e-government systems raises issues of the importance of data integration and interoperability. Recently, most government offices provide data interchange services through web services or using database direct-link (db-link), and the process usually runs without referring to certain regulations, standards, or procedures.Â It can cause some problems such as lack of a standard for data interchange services, lack of procedure for building, deploying, and monitoring the services, duplicate services, problems in tracing and maintaining the services, and much more.Â The research aims to provide the practical method for designing the regulation for supporting data interchange and propose a draft of the regulation package that consists of policy, procedure, and technical guide.Â The research is located in a regional ICT government office and the process for designing the regulation is building through a design thinking approach. Outputs of this research are the conceptual map of the issues that should be covered by the regulation, the structure of the policy, the draft of standard and procedure for supporting data interchange mechanism, and the sample of the technical guide. The draft of the regulation is then tested against the actual problem to see how the regulation, procedure, and guide can resolve it.Â The result shows that it can fill the needs of regulation in the organization and can address some needs on data interchange mechanisms.
Research on the Application of Data Mining in Teaching System

Using data mining technology, based on relevant scientific theories, optimize the school teaching system. Taking full advantage of the huge data stored in the teaching system of colleges and universities for many years, through decision analysis and decision tree pruning method to carefully analyze the data, discover the messages and useful knowledge to promote the teaching quality from a great many data in teaching system, and be used in teaching practice. This paper establishes data mining system to monitor and control teaching quality. Among them, data mining algorithms such as association rules and decision trees are applied. From the perspective of data analysis, the main body, process and other factors affecting teaching quality and their mutual relations are studied, and a rule discovery method suitable for teaching evaluation is proposed.
Joint Semantic Segmentation and Edge Detection for Waterline Extraction

Automatic water gauge reading is very important for cargo weighting in ocean transportation. In this process, accurate waterline extraction is an important yet challenging step. Waterline extraction is subjected to many environmental interference factors, e.g., bad illumination, bad weather conditions, ambiguous contours of water stain, etc. In this paper, we propose a joint multitask based deep model for accurate waterline extraction. The proposed model consists of two main branches. One branch is used to extract high-level contextual information. The other branch rooted in shallow layers is used to extract low-level detail features. The two branches are later coupled with each other to co-supervise the estimation of waterline. Our model works well on various conditions, such as uneven light, serious reflections, etc. We also introduce a new benchmark dataset for waterline extraction. This dataset consists of 360 pictures extracted from 69 videos collected in several actual ports. Furthermore, sufficient experiments show that our model is effective on the introduced dataset and outperforms the state-of-the-art methods.
Framework for Faster Index Search

In the age of the web the quantity of archives is developing every day, every hour and every moment. In such a climate the recovery of the connected data is of main issue. In this paper the focal point of the idea of the full text search engine with the reference of Lucene and furthermore examine in respect of the indexing and framework plan. In the framework, we have used the concept of the wordnet for the formation of the lexical chains, in order to find the related words and also to include the image search, we will be using the Tesseract API for extracting the text from the images, and then perform the search on that. This paper is only the conceptual explanation of the proposed work and the final implementation and result analysis we will include in the next research work.
Novel Lagrange interpolation polynomials for dynamic access control in a healthcare cloud system

&lt;abstract&gt; &lt;p&gt;The authority of user personal health records (PHRs) is usually determined by the owner of a cloud computing system. When a PHR file is accessed, a dynamic access control algorithm must be used to authenticate the users. The proposed dynamic access control algorithm is based on a novel Lagrange interpolation polynomial with timestamps, mainly functioning to authenticate the users with key information. Moreover, the inclusion of timestamps allows user access within an approved time slot to enhance the security of the healthcare cloud system. According to the security analysis results, this healthcare cloud system can effectively resist common attacks, including external attacks, internal attacks, collaborative attacks and equation-based attacks. Furthermore, the overall computational complexity of establishing and updating the polynomials is O(&lt;italic&gt;n&lt;/italic&gt;*&lt;italic&gt;m&lt;/italic&gt;* (log &lt;italic&gt;m&lt;/italic&gt;)&lt;sup&gt;2&lt;/sup&gt;), which is a promising result, where &lt;italic&gt;m&lt;/italic&gt; denotes the degree of $ polynomial~G\left(x, y\right) $ and &lt;italic&gt;n&lt;/italic&gt; denotes the number of secure users in the hierarchy.&lt;/p&gt; &lt;/abstract&gt;
A Transferable Framework for Few-Shot Human Activity Recognition

With the progress of sensor technology and the vigorous development of deep learning methods, using deep learning methods to complete sensor-based human activity recognition (HAR) tasks has become a research area of concern. Researchers have proposed various accurate, efficient, and low-cost methods to realize sensor-based HAR. In practical application scenarios, the frequency of the human body performing various movements often varies greatly, which causes great difficulties to the task of action recognition. However, most existing models can achieve better performance in the case of balanced category distribution and ignore the impact of data imbalance. In the field of HAR, few studies have been done on few-shot. Aiming at the low accuracy of the network in the case of imbalanced sample distribution, this paper proposed a transferable HAR framework. This framework can be superimposed on the existing deep network to improve the accuracy of recognition. Experiments of this framework on two kinds of deep networks were completed, and the accuracy is greatly improved, which verifies the portability of the model. Three different datasets were used in the experiment, including Opportunity, UniMiB-SHAR, and PAMAP2, to verify the effectiveness of the framework on different data sets. The experimental results are discussed in-depth and the contribution points of the proposed framework are analyzed.
APER-DDQN: UAV precise airdrop method based on deep reinforcement learning

Accuracy is the most critical factor that affects the effect of unmanned aerial vehicle (UAV) airdrop. The method to improve the accuracy of UAV airdrop based on traditional modeling has some limitations such as complex modeling, multiple model parameters and difficulty in considering all kinds of factors comprehensively when facing complex realistic environment. In order to solve the problem of UAV precision airdrop more conveniently, this paper introduces the deep reinforcement learning method and proposes an Adaptive Priority Experience Replay Deep Double Q-Network (APER-DDQN) algorithm based on Deep Double Q-Network (DDQN). This method introduces the priority experience replay mechanism based on DDQN, and adopts adaptive discount rate and learning rate to improve the decision-making performance and stability of the algorithm. Furthermore, this paper designs and builds a simulation experimental platform for algorithm training and testing. The experimental results show that our APER-DDQN has good performance and can more effectively solve the problem of UAV accurate airdrop while avoiding the complex modeling process. Firstly, in the training stage, compared with DDQN and Deep Q Network (DQN), APER-DDQN has faster convergence speed, higher reward and more stable performance. Then, in the test phase, compared with relying on human experience, our method shows higher average reward (average 3.01) and success rate (average 41%), and our method also has more advantages in performance compared with DDQN and DQN. Finally, extended experiments verify the generalization ability of APER-DDQN to different environments.
A Multiview Metric Learning Method for Few-Shot Fine-Grained Classification

Few-shot fine-grained image classification aims to solve the learning problem with few limited labeled examples. The existing methods use data augmentation to randomly transform the original examples to get new examples, and then use the new examples to train the model to improve the robustness and generalization ability of the learnt model. Due to each iteration of these methods uses a random transformation to get a new example, it will cause the unstable problem of the class center in the feature measurement stage. To solve this problem, a Multi-view Metric Learning (MML) method is proposed, which is based on a new concept (View Bag) and its effective similarity measurement method to achieve better few-shot fine-grained image classification. Firstly, a new example obtained by a kind of data augmentation is defined as a view, and a set of views generated by multiple data augmentation is defined as a view bag. Then, the view bag is sent into the model to extract the features, and a multi-view metric method with the view bag as the object is proposed to overcome the unstable problem of the class center. Finally, classification is performed by measuring the similarity between view bags. Experiments are conducted on three public datasets, CUB-2011-200, Stanford-Dogs and Stanford-Cars. The proposed method achieves 71.61±0.87%, 57.78±0.96% and 74.02±0.84% for the 5-way 1-shot classification task, and 88.72±0.51%, 76.30±0.68% and 92.94±0.37% for the 5-way 5-shot classification task, which have the state-of-the-art performances. Under the condition of the same backbone network, the proposed multi-view metric method can measure the similarity between examples more effectively, and improve the robustness and generalization ability of the model.
Morphological Galaxies Classification According to Hubble-de Vaucouleurs Diagram Using CNNs

Galaxies morphology classification is a crucial task for studying their physical properties, formation and evolutionary histories. The large-scale surveys on universe has boosted the need to develop techniques for automated galaxies morphological classification. This paper proposes a system able to classify automatically galaxies according to the Hubble De Vaucouleurs diagram. We introduce a novel CNN architectures that for the first time was trained to automatically classify galaxies according to 26-classes Hubble-De Vaucouleurs scheme. We use Galaxy Zoo dataset, using the decision tree, to extract a labeled examples containing an even amount of images of each 26-classes. We also compared different CNN Backbones in order to assess obtained galaxies classification results. We obtain a balanced multi-class accuracy (BCA) of more than 80% in classifying all 26 Hubble-De Vaucouleurs galaxy categories.
Studying Users’ Willingness to Use a Formally Verified Password Manager

Password Managers (PMs) help users manage their passwords safely but many users do not trust them. To mitigate users’ doubts, formal verification can be used. Formal verification can guarantee the absence of errors and make PMs more reliable. Nonetheless, the impact it has on the adoption of formally verified software is unknown. In previous work, we performed a preliminary user study which suggests that formal verification increases users’ willingness to use PMs. However, a large-scale study is required to confirm our findings. As such we designed and plan to deploy a large-scale study to confirm our previous work and gather further insight on users’ perceptions of formal verification in PMs.