Reviewerly/exHarmony huggingface dataset:
Mining High Utility Itemsets Using Ant Colony Optimization

Mining high utility itemsets (HUIs) is one of the most important research topics in data mining. Algorithms based on evolutionary computation are attracting increasing attention, as they have the advantage of avoiding the combinatorial explosion of the HUI search space. In this paper, we propose an HUI mining algorithm based on ant colony optimization (HUIM-ACO) that generates new candidate in a constructive way. In the HUIM-ACO algorithm, we use a search path to represent candidate HUIs. Each search path is initialized proportionally, and incremented by one item per step. A pheromone matrix is proposed to store the pheromone values of a pair of items, allowing the local and global values to be updated. Furthermore, an ant-path quad is designed for the efficient enumeration of HUIs. Experimental comparisons with three state-of-the-art HUI mining algorithms show that HUIM-ACO can discover more HUIs in a shorter period of time.
Challenges and Opportunities for Educational Actors in light of the COVID-19 Pandemic in Paraguay / Desafíos y oportunidades para los actores educativos ante la pandemia de COVID-19 en Paraguay

The measures implemented in response to COVID-19 have affected education systems around the world, generating significant disruptions. This study examines the main challenges and opportunities presented to the different educational actors in Paraguay considering the health emergency and the need to give continuity to the educational processes in the country from the last week of March until the first days of May 2020. A total of 2501 people participated, including teachers, students, parents of non-university students, and managers from educational institutions at all levels and from all over the country. It follows a mixed-quan-qual explanatory approach and data collection was conducted through online questionnaires. The study showed changes and strategies implemented by educational actors for the development of classes mediated by digital tools. The results reflect challenges related to access to technological resources, training in the use of ICT, and difficulties in carrying out school activities. Among the opportunities mentioned is the possibility of continuing with studies, learning about technology, and transforming the educational system. These show evidence of the need to improve access to technology to guarantee equal educational opportunities in the country.
Blockchain Based Accounts Payable Platform for Goods Trade

Goods trade is a supply chain transaction that involves shippers buying goods from suppliers and carriers providing goods transportation. Shippers are issued invoices from suppliers and carriers. Shippers carry out goods receiving and invoice processing before payment processing of bills for suppliers and carriers, where invoice processing includes tasks like processing claims and adjusting the bill payments. Goods receiving involves verification of received goods by the Shipper's receiving team. Invoice processing is carried out by the Shipper's accounts payable team, which in turn is verified by the accounts receivable teams of suppliers and carriers. This paper presents a blockchain-based accounts payable system that generates claims for the deficiency in the goods received and accordingly adjusts the payment in the bills for suppliers and carriers. Primary motivations for these supply chain organizations to adopt blockchain-based accounts payable systems are to eliminate the process redundancies (accounts payable vs. accounts receivable), to reduce the number of disputes among the transacting participants, and to accelerate the accounts payable processes via optimizations in the claims generation and blockchain-based dispute reconciliation.
Artificial Intelligence in Music Composition

Technology has had a remarkable influence on music. As society advances technologically, the music industry does as well. An example that illustrates the use of technology in music is the use of artificial intelligence (AI) as a creative and inspiring tool. Music helps shape emotional responses, creates a rhythm, and comments on the action. It is often a very crucial element to any experience. However, music, like any form of art, is an extremely challenging field to tackle using AI. The amount of information in a musical structure can be overwhelmingly large. If we factor in the different and unpredictable nuances invoked by human imperfection and emotion, it becomes clear why, even though AI excels at handling large amounts of data, generating good music can be very challenging, especially when it comes to Jazz and similarly complex genres.
Regime change in the recorder: Using Navier–Stokes modeling to design a better instrument

Blowing a recorder at a low to moderate blowing speed with the toneholes all closed yields the lowest note in the range of the instrument. As the blowing speed is increased, the tone abruptly changes to the tone an octave higher. This “jump” in the frequency of the dominant spectral component of the tone is referred to as “regime change.” Interestingly, in conversations with recorder players, several have mentioned that regime change seems to occur at a significantly lower blowing speed for bass recorders than for instruments that sound an octave or more higher. In this paper we study regime change in the recorder and use Navier–Stokes modeling to confirm and study differences in the behavior of different instruments in the recorder family. We show, using modeling, how the regime change threshold in a model of the bass recorder can be increased by changing the geometry in the vicinity of the labium. These results are then confirmed through experimental studies of real recorders with designs inspired by the modeling results. The insights gained from these results suggest new recorder designs that may produce instruments that in some respects are more playable than current instruments.
Cherry Picking, HARKing and P-hacking

Questionable research practices in the presentation and statistical analysis of data include cherry picking, HARKing, and P-hacking. Cherry picking data results in the fallacy of incomplete evidence by selecting evidence in support and suppressing evidence against a hypothesis. It may not be intentional, but rather may represent a fallacy of selective attention, as in the case of confirmation bias. A common example is relying on anecdotal evidence that supports one's bias; another example is the one-sided argument which only presents evidence supporting a hypothesis.
Less is Often More: Header Whitelisting as Semantic Gap Mitigation in HTTP-Based Software Systems

The web is the most wide-spread digital system in the world and is used for many crucial applications. This makes web application security extremely important and, although there are already many security measures, new vulnerabilities are constantly being discovered. One reason for some of the recent discoveries lies in the presence of intermediate systems—e.g. caches, message routers, and load balancers—on the way between a client and a web application server. The implementations of such intermediaries may interpret HTTP messages differently, which leads to a semantically different understanding of the same message. This so-called semantic gap can cause weaknesses in the entire HTTP message processing chain. In this paper we introduce the header whitelisting (HWL) approach to address the semantic gap in HTTP message processing pipelines. The basic idea is to normalize and reduce an HTTP request header to the minimum required fields using a whitelist before processing it in an intermediary or on the server, and then restore the original request for the next hop. Our results show that HWL can avoid misinterpretations of HTTP messages in the different components and thus prevent many attacks rooted in a semantic gap including request smuggling, cache poisoning, and authentication bypass.
Colon Cancer Detection Using Hybrid Features and Genetically Optimized Neural Network Classifier

Computer-assisted colon cancer detection on the histopathological images has become a tedious task due to its shape characteristics and other biological properties. The images acquired through the histopathological microscope may vary in magnifications for better visibility. This may change the morphological properties and hence an automated magnification independent colon cancer detection system is essential. The manual diagnosis of colon biopsy images is subjective, sluggish, laborious leading to nonconformity between histopathologists due to visual evaluation at various microscopic magnifications. Automatic detection of colon across image magnifications is challenging due to many aspects like tailored segmentation and varying features. This demands techniques that take advantage of the textural, color, and geometric properties of colon tissue. This work exhibits a segmentation approach based on the morphological features derived from the segmented region. Gabor Wavelet, Harris Corner, and DWT-LBP coefficients are extracted as it should not be dependent on the spatial domain with respect to the magnification. These features are fed to the Genetically Optimized Neural Network classifier to classify them as normal and malignant ones. Here, the genetic algorithm is used to learn the best hyper-parameters for a neural network.
Validity of the Flipped Classroom Learning System Based on Guided Inquiry on Molecular Forms Using Augmented Reality for Class X SMA/MA Students

The era of the industrial revolution 4.0 or the era of disruption caused many changes in people's lives, including in the field of education. The Covid-19 pandemic is one of the characteristics of the era of disruption, namely volatility and uncertainty where the Covid-19 pandemic occurred and caused changes that were fast and unpredictable (volatility) and there was uncertainty. Changes that occur in education are the learning process from face-to-face in class to online learning. This research develops a learning system and learning media that can be used in certain situations such as the Covid-19 pandemic. This learning system combines flipped classroom learning with guided inquiry learning models. And the development of learning media based on augmented reality to visualize 3D objects in molecular shapes. The research method used Research and Development (R&D) with a ploomp research model. The validity were carried out with the values obtained for the validity of the learning system of 1.08 and the value of the validity of the media at 0.87 in the high valid category. And for the value of the validity of learning media materials BEMO-AR 0.82 and the validity value of learning media 0.86 with high valid categories.
Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion (Student Abstract)

Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs. However, these methods fail to take full advantage of both the contextual information of entity and relation. Here, we extract contexts of entities and relations from the triplets which they compose. We propose a model named AggrE, which conducts efficient aggregations respectively on entity context and relation context in multi-hops, and learns context-enhanced entity and relation embeddings for knowledge graph completion. The experiment results show that AggrE is competitive to existing models.
A Blockchain-based platform for reliably tracing political contacts

Corruption is an evil that affects not only the most oppressive and totalitarian regimes, but also, albeit to a lesser extent, democratic regimes. The difference largely consists in the mechanisms for combating corruption and in the transparency between political relations and other economic interests. The transparency associated with the possibility of tracking "interests" is one of the mechanisms that can be implemented in democracy. This article, after a brief overview about how the transparency of political relations can guarantee less corruption, presents a blockchain-based platform for tracking political contacts. All the concepts involved in the platform and its development are covered in detail, and several examples are presented. The proposed platform excels in data consistency, security and immutability, demonstrating enormous reliability and transparency towards users.
Abstract SY01-03: The gold standard cancer diagnosis: Studies of physician variability, interpretive behavior, and the impact of AI

Abstract The current gold standard for cancer diagnoses is based on pathologists' visual inspection of tissue sections. However, our research has found concerning levels of inter-observer and intra-observer variability among pathologists. Our prior work in melanoma shows that current diagnoses within the disease spectrum from benign nevi to melanoma in situ to invasive melanoma are neither reproducible nor accurate, yielding estimates that ~17% of all diagnoses for melanocytic lesions in the US are incorrect (Elmore et al. BMJ 2017). A study conducted by our team in breast pathology quantified the magnitude of diagnostic agreement among pathologists compared with a gold standard consensus reference: among DCIS cases, 16% of interpretations were discordant, while among atypia cases 52% of interpretations were discordant (Elmore et al. JAMA 2015). While computer systems, such as computer aided detection (CAD) tools, have been widely integrated into clinical practice to aid the interpretative and diagnostic process, our work has also found that the use of CAD can be associated with increases in potential harms, including higher recall and biopsy rates for screening mammography (Fenton et al. NEJM 2007). Given the critical need to improve the quality of our current diagnostic and prognostic capabilities, our multidisciplinary research team is conducting several studies that involve the development and integration of AI/machine learning and eye-tracking across clinical contexts. The challenges and implications associated with “gold standard” definitions for diagnoses, with data sharing infrastructure and with the eventual impact of AI on the human interface will be discussed. Citation Format: Joann Elmore. The gold standard cancer diagnosis: Studies of physician variability, interpretive behavior, and the impact of AI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr SY01-03.
Dataset for toothbrushing activity using brush-attached and wearable sensors

Maintaining oral hygiene is very important for a healthy life. Poor toothbrushing is one of the leading causes of tooth decay and other gum problems. Many people do not brush their teeth properly. There is very limited technology available to help in assessing the quality of toothbrushing. Human Activity Recognition (HAR) applications have seen a tremendous growth in recent years. In this work, we treat the adherence to standard toothbrushing practice as an activity recognition problem. We investigate this problem and collect experimental data using a brush-attached and a wearable sensor when the users brush their teeth. In this paper, we extend our previous dataset [1] for toothbrushing activity by including more experiments and adding a new sensor. We discuss and analyse the collection of the dataset. We use an Inertial Measurement Unit (IMU) sensor to collect the time-series data for toothbrushing activity. We recruited 22 healthy participants and collected the data in two different settings when they brushed their teeth in five different locations using both electric and manual brushes. In total, we have recorded 120 toothbrushing sessions using both brush-attached sensor and the wearable sensor.
Data mining as a cognitive tool: Capabilities and limits

Due to the large volumes of empirical digitized data, a critical challenge is to identify their hidden and unobvious patterns, enabling to gain new knowledge. To make efficient use of data mining (DM) methods, it is required to know its capabilities and limits of application as a cognitive tool. The paper aims to specify the capabilities and limits of DM methods within the methodology of scientific cognition. This will enhance the efficiency of these DM methods for experts in this field as well as for professionals in other fields who analyze empirical data. It was proposed to supplement the existing classification of cognitive levels by the level of empirical regularity (ER) or provisional hypothesis. If ER is generated using DM software algorithm, it can be called the man-machine hypothesis. Thereby, the place of DM in the classification of the levels of empirical cognition was determined. The paper drawn up the scheme illustrating the relationship between the cognitive levels, which supplements the well-known schemes of their classification, demonstrates maximum capabilities of DM methods, and also shows the possibility of a transition from practice to the scientific method through the generation of ER, and further from ER to hypotheses, and from hypotheses to the scientific method. In terms of the methodology of scientific cognition, the most critical fact was established – the limitation of any DM methods is the level of ER. As a result of applying any software developed based on DM methods, the level of cognition achieved represents the ER level.
Balancing Public Health and Civil Liberties: Privacy Aspects of Contact-Tracing Technologies

Around the world, different jurisdictions, workplaces, and schools have taken wildly different approaches to contact tracing in response to COVID-19, striking varying balances between public health and safety and privacy and civil liberties. This article discusses contact-tracing technologies, with a particular focus on the privacy aspects of contact tracing and exposure-notification apps. It describes the three main categories of contact-tracing apps, namely centralized location-centralized Bluetooth-, and decentralized Bluetooth-based apps. This article observes lower adoption in the United States than in Europe and briefly considers what this microcosm could teach us about the intersection between privacy and technology.
Optical Compressive Encryption via Deep Learning

The compression of the ciphertext of a cryptosystem is desirable considering the dramatic increase in secure data transfer via Internet. In this paper, we propose a simple and universal scheme to compress and decompress the ciphertext of an optical cryptosystem by the aid of deep learning (DL). For compression, the ciphertext is first resized to a relatively small dimension by bilinear interpolation and thereafter condensed by the JPEG2000 standard. For decompression, a well-trained deep neural network (DNN) can be employed to perfectly recover the original ciphertext, in spite of the severe information loss suffered by the compressed file. In contrast with JPEG2000 and JPEG, our proposal can achieve a far smaller size of the compressed file (SCF) while offering comparable decompression quality. In addition, the SCF can be further reduced by compromising the quality of the recovered plaintext. It is also shown that the compression procedure can provide an additional security level, and this may offer new insight into the compressive encryption in optical cryptosystems. Both simulation and experimental results are presented to demonstrate the proposal.
The Effect of Student Worksheets Through the Edmodo Network on Concept Understanding and Independent Learning on Hydrocarbon Materials

This study aims to determine the effect of student worksheets through the Edmodo network on understanding concepts and learning independence on hydrocarbon material. The research method used is an experiment with a quantitative approach. The research design used was a pretest-posttest control group design. The population of this study was students of class XI SMAN 5 Banda Aceh with data collection techniques by random sampling to obtain two classes that became the research sample. The research instruments used were reasoned test questions and questionnaires. Based on the results of the t-test analysis of conceptual understanding with a significant score of 0.015 &lt;0.05, it shows that there is a significant difference between the control class and the experimental class. The results of the t-test of students' learning independence showed that the significant score was 0.000 &lt;0.05, which means that the data had a significant difference between the control and experimental classes. The conclusion in this study is that student worksheets through the Edmodo network can affect students' understanding of concepts and learning independence. This research concludes that using student worksheets through the Edmodo network in chemistry learning is more effective in increasing understanding of concepts and students' learning independence for the better
AliMe DA: A Data Augmentation Framework for Question Answering in Cold-start Scenarios

Cold-start is the most difficult and time-consuming phase when building a question answering based chatbot for a new business scenario because of the collection of sufficient training data. In this paper, we propose AliMe DA, a practical data augmentation (DA) framework that consists of data production, denoising and consumption, to alleviate this problem. We show how our DA approach can be used to substantially enhance annotation productivity and also improve downstream model performance. More importantly, we provide best practices for data augmentation, including how to choose and employ appropriate methods at each stage of our framework, and share our observation on the applicable scene of data augmentation in the era of pre-trained language models.
Unsupervised Neural Rendering for Image Hazing

Image hazing aims to render a hazy image from a given clean one, which could be applied to a variety of practical applications such as gaming, filming, photographic filtering, and image dehazing. To generate plausible haze, we study two less-touched but challenging problems in hazy image rendering, namely, i) how to estimate the transmission map from a single image without auxiliary information, and ii) how to adaptively learn the airlight from exemplars, i.e., unpaired real hazy images. To this end, we propose a neural rendering method for image hazing, dubbed as HazeGEN. To be specific, HazeGEN is a knowledge-driven neural network which estimates the transmission map by leveraging a new prior, i.e., there exists the structure similarity (e.g., contour and luminance) between the transmission map and the input clean image. To adaptively learn the airlight, we build a neural module based on another new prior, i.e., the rendered hazy image and the exemplar are similar in the airlight distribution. To the best of our knowledge, this could be the first attempt to deeply render hazy images in an unsupervised fashion. Compared with existing haze generation methods, HazeGEN renders the hazy images in an unsupervised, learnable, and controllable manner, thus avoiding the labor-intensive efforts in paired data collection and the domain-shift issue in haze generation. Extensive experiments show the promising performance of our method comparing with some baselines in both qualitative and quantitative comparisons. The code is available at https://github.com/XLearning-SCU.