Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Association Rule Mining Algorithms

Abstract The field of data mining (DM) has grown rapidly in recent years. One of the most important data mining techniques is association rule mining (ARM). It is a strategy used to identify trends in the database that are normal. There has been a lot of work in the area of ARM. The paper provides a short description of the principles and algorithms of interaction, several of the implementations. To several researchers, ARM has long been and still is of concern. Data mining is one of the essential activities. This helps to identify associations between various elements in the database. The goal of this paper is to provide an outline of the fundamental concepts of the ARM methodology and the recent relevant research in this area. The paper further explains the different algorithms, methods, strategies, and benefits of the ARM areas, drawbacks. The paper also provides a minor distinction focused on the results of various algorithms related to association rules mining. The paper provides a short description of the principles and algorithms of interaction, several of the implementations. Algorithms are present and evaluate base parameters such as precision, algorithm pace, and help for data. To solve the question of apriori algorithms AprioriTid and the AprioriHybrid have been suggested. From the contrast, we infer that, since it has decreased overall pace and increased precision, AprioriHybrid is superior to Apriori and AprioriTid. We may infer that the LogElcat algorithm performs more than every other algorithm based on these parameters.
Detecting moonquakes using convolutional neural networks, a non-local training set, and transfer learning

SUMMARY The costly power requirements of delivering seismic data back to Earth from planetary missions requires the development of algorithms for lander-side signal analysis for telemetry prioritization. This is difficult to explicitly program, especially if no prior seismic data are available from the planetary body. Deep learning computer vision has been used to generalize seismic signals on Earth for earthquake early warning problems but such techniques have not yet been expanded to planetary science. We demonstrate that Convolutional Neural Networks can be used to accurately catalogue planetary seismicity without local training data by building binary noise/signal classifiers from a single Earth seismic station and applying the models to moonquakes from the Apollo Passive Seismic Experiment (PSE) and the Lunar Seismic Profiling Experiment (LSPE). In order to promote generality and reduce the amount of training data, the algorithms use spectral images instead of time-series. Two- to five-layer convolution models are tested against a subset of 200 Grade-A events from the PSE and obtained station accuracy averages of 89–96 per cent. As the model was applied to an hour trace of data (30 min before and after the Grade-A event), additional detections besides the Grade-A event are unavoidable. In order to comprehensively address algorithm accuracy, additional seismic detections corresponding to valid signals such as other moonquakes or multiples within a particularly long event needed to be compared with those caused by algorithm error or instrument glitches. We developed an ‘extra-arrival accuracy’ metric to quantify how many of the additional detections were due to valid seismic events and used it to select the three-layer model as the best fit. The three-layer model was applied to the entire LSPE record and matched the lunar day–night cycle driving thermal moonquake generation with fewer false detections than a recent study using Hidden Markov Models. We anticipate that these methods for lander-side signal detection can be easily expanded to non-seismological data and may provide even stronger results when supplemented with synthetic training data.
Sentimental study of CAA by location-based tweets

As people progressively resort to twitter to express their opinions or to disambiguate their sentiment, it's feasible to analyze the mass opinion to conclude the polarity of the subject at hand using sentiment analysis. Sentiment Analysis (SA) has revolutionized the way information is perceived today. Inspired by this, the work in this paper investigates the much-debated act- the Citizenship Amendment Act (CAA) by analyzing opinionated geo-tagged tweets, manually annotated and cross verified by six annotators. This is the first paper to the best of our knowledge to analyse CAA using SA and to provide a clear statistics of the mass opinion across the states of the nation. In this paper, machine learning approach is used for sentiment analysis of tweets. Support vector machine classifier is used to classify the tweets into three classes viz. positive, negative and neutral.
Analysis of ANN-based daily global horizontal irradiance prediction models with different meteorological parameters: a case study of mountainous region of India

Solar resource availability of a location depends on the local meteorological parameters. Therefore, the selection of suitable meteorological variable is highly desirable to develop the accurate solar irradiance prediction models. In present study, global horizontal irradiance (GHI) prediction models are developed using artificial neural network (ANN) with different combinations of meteorological parameters. A four-year station measured dataset consists of minimum temperature (Tmin), maximum temperature (Tmax), temperature difference (ΔT), GHI, extraterrestrial radiation (H0), and bright sunshine hours (S) have been employed to establish ANN models. Five types of ANN models (ANN-1 to ANN-5) are developed with 32 possible input combinations to determine the best input combinations to predict the daily GHI accurately. The achieved maximum correlation coefficient for ANN-1, ANN-2, ANN-3, ANN-4, and ANN-5 models are 0.9197, 0.9681, 0.9688, 0.9515, and 0.9457, respectively. The results revealed that ANN-2 and ANN-3 has shown best performance with the input combinations of [ΔT,S] and [Tmax,Tmin,ΔT] respectively. The proposed methodology is also used to assess the solar potential of the mountainous state of Uttarakhand, India, situated in foots of Himalayas, using the best ANN model. The obtained results suggest that Uttarakhand has good solar potential with annual GHI varies from 16.96 to 19.54 MJ/m2/day, which is sufficient to implement a broad range of solar applications in the region. The methodology proposed in this work can be utilized to develop solar irradiance prediction models for different locations where monitoring stations are not available.
Combining Planning and Learning of Behavior Trees for Robotic Assembly

Industrial robots can solve tasks in controlled environments, but modern applications require robots able to operate also in unpredictable surroundings. An increasingly popular reactive policy architecture in robotics is Behavior Trees (BTs) but as other architectures, programming time drives cost and limits flexibility. The two main branches of algorithms to generate policies automatically, automated planning and machine learning, both have their own drawbacks and have not previously been combined for generation of BTs. We propose a method for creating BTs by combining these branches, inserting the result of an automated planner into the population of a Genetic Programming algorithm. Experiments confirm that the proposed method performs well on a variety of robotic assembly problems and outperforms the base methods used separately. We also show that this high level learning of Behavior Trees can be transferred to a real system without further training.
Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.
Interval-valued Fuzzy Logic approach for overloaded hosts in consolidation of virtual machines in cloud computing

Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing. For example, determining when it is best to relocate VMs from an overloaded host is one aspect of dynamic VM consolidation that directly influences the resource utilization and Quality of Service (QoS) offered by the system. This paper presents a new proposal with an Interval-valued Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. The results obtained in a simulated cloud computing environment showed improvements for energy efficiency of 2.3% with Inter Quartile Range (IQR). With regard to Service Level Agreement (SLA), results of 10.1% were achieved with Threshold Policy (THR), and finally, IQR obtained 7.9% better results considering the reduction in the number of VM migrations.
Reflectance spectroscopy of ilmenites and related Ti and Ti Fe oxides (200 to 2500 nm): Spectral–compositional–structural relationships

Ilmenite is an important mineral for understanding lunar evolution. Ilmenite is also a primary ore of titanium on the Earth. Here we present a comprehensive examination of the spectral-compositional-structural relationships of ilmenites and related FeTi oxides. Ilmenite spectral features of interest include maxima near ~250 nm (due Ti4+-O and Fe2+-O charge transfers), ~335 nm (due to Fe2+-Ti4+ charge transfer), and 950 nm (interband maximum), and absorption features near ~540 nm (due to Fe2+-Ti4+ charge transfers), ~630 nm (due to Ti3+-Ti4+ charge transfers), and a ~ 1300/1600 nm absorption doublet (due to Fe2+ crystal field transitions). Absorption features transition from Fresnel peaks to valley around 400 nm, as absorption coefficients decrease toward longer wavelengths. Ilmenite powders are generally darkest and show the greatest spectral contrast and reflectance rise beyond ~1300 nm for the smallest grain sizes. Other Ti and FeTi oxides share some spectral properties with ilmenites but also exhibit many differences. The most common feature they share is a rise in reflectance toward shorter wavelengths below ~500 nm (i.e., blue spectral slope). Ti ± Fe oxide spectra can also exhibit absorption features attributable to Ti, Fe, and Ti ± Fe, and these features vary in intensity, shape and wavelength position due to factors such as Ti and Fe oxidation states, coordination environment, and nearest neighbor cation types. Ilmenite differs most from silicates in the region below ~500 nm: it shows a reflectance increase versus decrease toward shorter wavelengths for silicates, as well as diagnostic Ti/Ti-Fe maxima or minima. Thus, detection of ilmenite in mixtures is best accomplished by including the UV region in spectral analysis. With increasing ilmenite in mixtures, its diagnostic spectral features become increasingly apparent.
Attention Transfer Network for Nature Image Matting

Natural image matting is an important problem that widely applied in computer vision and graphics. Recent deep learning matting approaches have made an impressive process in both accuracy and efficiency. However, there are still two fundamental problems remain largely unsolved: 1) accurately separating an object from the image with similar foreground and background color or lots of details; 2) exactly extracting an object with fine structures from complex background. In this paper, we propose an attention transfer network (ATNet) to overcome these challenges. Specifically, we firstly design a feature attention block to effectively distinguish the foreground object from the color-similar regions by activating foreground-related features as well as suppressing others. Then, we introduce a scale transfer block to magnify the feature maps without adding extra information. By integrating the above blocks into an attention transfer module, we effectively reduce the artificial content in results and decrease the computational complexity. Besides, we use a perceptual loss to measure the difference between the feature representations of the predictions and the ground-truths. It can further capture the high-frequency details of the image, and consequently, optimize the fine structures of the object. Extensive experiments on two publicly common datasets (i.e., Composition-1k matting dataset, and www.alphamatting.com dataset) show that the proposed ATNet obtains significant improvements over the previous methods. The source code and compiled models have been made publicly available at https://github.com/ailsaim/ATNet.
Understanding AI Advertising From the Consumer Perspective

In recent years, artificial intelligence (AI) technology has been used to create advertising messages. This study examined the factors that influence consumers’ overall appreciation of AI-created advertisements. The findings indicate that, in addition to its direct effect on consumer reactions to AI-created advertisements, consumers’ perceived objectivity of the general advertisement creation process positively influences machine heuristic—a rule of thumb that machines are more secure and trustworthy than humans. This effect boosted consumer appreciation of AI-created advertisements. Consumers’ perceived objectivity of advertisement creation negatively influenced perceived eeriness of AI advertising, which jeopardized consumer appreciation of AI-created advertisements. Consumers’ feelings of uneasiness with robots were found to have a positive influence on both machine heuristic and perceived eeriness of AI advertising.
Formation of communication and teamwork skills of future IT-specialists using project technology

Abstract The subject of the study is the formation of communication and teamwork skills of future IT-specialists, using project technology in teaching the administration of computer systems and networks. Content analysis of research has shown that communication and teamwork skills are the most requested and necessary soft skills for future IT professionals. It is offered to use project technology of training for their formation. This technology consists of the approaches and tools application used in real-world software development. To implement this technology, we propose to use mind maps for design. The organization of the working process on the project is implemented in Jira Software, Asana or Trello. Communication among project participants should be implemented with the help of several means: video chats (Zoom, Skype, Google Meet) and written communication in corporate messengers (Slack, Zulip). Experimental verification has shown the efficiency of the proposed design technology implementation.
Arabic Gloss WSD Using BERT

Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous; 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.
Rethinking digital ethnography: A qualitative approach to understanding interfaces

Contemporary everyday cultures are increasingly pervaded by digital platforms, making a rethinking of observational research into sociocultural phenomena necessary. Observational practices that remain confined to physical localities face various impasses in the era of digital platforms. Combining participant observation in workplaces where digital platforms are accessed with technical walkthroughs of their interfaces, I make a case for renewing interface ethnography. On one hand, technical walkthroughs reveal the sociocultural assumptions embedded in interfaces and the ways in which their affordances generate symbolic meanings. However, on the other hand, participant observation foregrounds emic perspectives on digital platforms within communities of practice. Based on an ethnographic study of a Norwegian software firm, I argue that the integration of the walkthrough method with participant observation enables researchers to trace the twofold meaning construction inherent in communities of practice whose skilled labor is orientated toward digital platforms. This fusion of methods expands the scope of ethnographic knowledge across the digital-physical continuum.
Methods for developing motivational and value-orientated readiness of math students at teacher training universities for implementing educational innovations

Abstract The present article considers the issue of developing motivational and value-orientated readiness of Math students at teacher training universities for implementing educational innovations. The method, chosen for the present research is the analysis of the scientists’ viewpoints, on-line resources and reports regarding the issue of designing and using Rich Tasks when teaching Mathematics, methods and means for developing students’ motivation for implementing educational innovations. This paper describes the methods, aimed at developing in students motivation and ability to systematize, devise, use Rich Tasks in their professional activity and to combine them with other innovational learning methods and means. The analysis of the teacher training internship results and the surveys in the experimental group give ground to make a conclusion about efficiency of the methods offered.
The Modern Swedish National Seismic Network: Two Decades of Intraplate Microseismic Observation

Abstract The Swedish National Seismic Network (SNSN) was modernized and rapidly expanded during the period 1998–2012. The network currently operates 68 permanent seismic stations, all with broadband instruments supplying real-time continuous data at 100 samples per second. Continuous data from 10 stations are shared with the international community via Orfeus, and approximately 10 stations of their individual choice are shared with institutes in neighboring countries (Denmark, Finland, Norway, and Germany). The SNSN uses the South Iceland Lowland (SIL) system as the primary system for automatic detection and event definition. In addition, an in-house system based on migration and stacking is used for automatic detection of small events, and implementations of SeisComP (SC) and Earthworm are used primarily for rapid detection of larger regional events. Global monitoring is performed with SC, using approximately 250 global stations, and we operate a continuous rapid risk assessment system serving Swedish crisis management authorities. Since the start of automatic processing in August 2000, the SNSN has recorded and interactively analyzed more than 171,000 seismic events, of which 10,700 were earthquakes with local magnitudes ranging from around −1 to 4.3. The microearthquake activity detected in the last 20 yr has significantly improved the identification and understanding of seismically active structures in Sweden.
Integrating the Health-care Enterprise Pathology and Laboratory Medicine Guideline for Digital Pathology Interoperability

Integrating the health-care enterprise (IHE) is an international initiative to promote the use of standards to achieve interoperability among health information technology systems. The Pathology and Laboratory Medicine domain within IHE has brought together subject matter experts, electronic health record vendors, and digital imaging vendors, to initiate development of a series of digital pathology interoperability guidelines, called "integration profiles" within IHE. This effort begins with documentation of common use cases, followed by identification of available data and technology standards best utilized to achieve those use cases. An integration profile that describes the information flow and technology interactions is then published for trial use. Real world testing occurs in "connectathon" events, in which multiple vendors attempt to connect their products following the interoperability guidance parameters set forth in the profile. This paper describes the overarching set of integration profiles, one of which has been published, to support key digital pathology use cases.
Deformable Self-Attention for Text Classification

Text classification is an important task in natural language processing. Contextual information is essential for text classification, and different words usually need different sizes of contextual information. However, most existing methods learn contextual features with predefined fixed sizes, which cannot extract the different sizes of contextual features for different words. To this end, we propose a new model named Deformable Self-Attention (DSA) to flexibly learn word-specific contextual features, rather than extracting features of fixed context sizes. Our model is mainly composed of a Deformable Local Attention Weight Generation (DLAWG) module and a Multi-Range Feature Integration (MRFI) module. The DLAWG module can adaptively determine different context sizes for different words within a particular range and then learn word-specific contextual features for each word. DLAWG then employs multiple ranges to capture context dependencies of different ranges. After that, the MRFI module integrates features from different ranges by considering the interactions with features of different ranges, which can delete irrelevant features while enhancing discriminative ones. Experiments on extensive benchmark datasets and visualizations illustrate the effectiveness of our model.