Reviewerly/exHarmony huggingface dataset:
Distributed elephant herding optimization for grid-based privacy association rule mining

Purpose Association rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining. Design/methodology/approach The primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm. Findings The experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods. Originality/value Data mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.
Copyright in the Scientific Community. The Limitations and Exceptions in the European Union and Spanish Legal Frameworks

The increase of visibility and transfer of scholar knowledge through digital environments have been followed by the author’s rights abuses such as plagiarism and fraud. For this reason, copyright is increasingly a topic of major importance since it provides authors with a set of rights to enable them to utilize their work and to be recognized as the creators. The new research methods linked to technological advances (such as data mining) and the emergence of systems such as Open Access (OA) are currently under debate. These issues have generated legislative changes at the level of the European Union (EU) and its Member States. For this reason, it is relevant that the researchers know how to protect their work and the proper use of another’s work. Consequently, this research aims to identify the limitations of copyright in the EU and as a specific case in Spain, within the framework of scientific research. For this, the changes in the European and Spanish copyright regulations are analyzed. The results confirm new exceptions and limitations for researchers related to technological evolution, such as data mining. Additionally, the article incorporates several guidelines and implications for the scientific community.
Remote Technologies in the Field of Foreign Language Teaching as a Part of the Digital Education Ecosystem

The most important direction of the reform process in the field of education in all its constituent components in recent years has been the intensive use of a variety of distance learning platforms (technologies), including in the field of teaching foreign languages.The introduction of modern methods and technologies in higher education is connected to the fact that traditional education does not fully meet the requirements of the time both in the forms of its organization and its methods.As a result, students do not always have the opportunity to acquire the necessary knowledge and skills for complete assimilation in modern society.The purpose of this article is to identify significant, in the opinion of the authors, remote technologies in the digital education ecosystem in higher educational institutions, to describe the advantages and disadvantages of the most popular of them from the point of view of teachers and students.A survey of the control group (100 people) made it possible to identify several of the most successful remote educational platforms (technologies): Moodle, Flipped Classroom, CLIL, SRS; determine the degree of their usefulness from the point of view of the respondents.Their active and motivated use in the educational process will undoubtedly contribute to its qualitative filling, radical improvement of teaching methods, activating the formation of professional foreignlanguage communicative competencies in order to increase the level of knowledge of the studied foreign language, its successful application in the corresponding professional field.
WebIDE Cloud Server Resource Allocation with Task Pre-Scheduling in IOT Application Development

WebIDE is leveraged for IOT applications development, which could adapt to the rapid growth of IOT applications and meanwhile facilitate the rapid development. Resource allocation is of vital significance in the WebIDE cloud service system. Existing resource allocation approaches may encounter issues such as unbalanced resource assignments, which could lead to the reduced system resource utilization or extended system response time. The fundamental reason behind it lies in the fact that existing ones are typically on the basis of predetermined resource demands for each task, and not applicable to the case with dynamic and unknown resource demands. Thus, in this paper, we propose a novel approach based on task pre-scheduling, which schedules beforehand each task into a virtual machine local task queue by means of demand prediction. Firstly, all tasks are classified, based on the execution state, execution operations and cloud server resource requirements. Secondly, the grouped tasks are mapped to different system states, with the Markov state transition probability matrix leveraged to model the transition probability between tasks, followed by the prediction model constructed. Finally, integrating both task pre-scheduling and ant colony algorithm, cloud server resource allocation is carried out. Simulation results show that the task prediction model could significantly not only reduce the task response time, but also improve the cloud server resource utilization.
Holographic method for storage of digital information

The holographic coding method improves the reliability of storing digital information in memory systems that are subject to external influences that introduce a large number of errors. The method is based on writing to memory instead of the initial data of a digital hologram created in the virtual space by a wave from an input source. The property of the hologram divisibility is used, allowing to recover the recorded data block by its fragment. The achieved level of noise immunity is determined by the size of the hologram. For an 8-bit data block, a 256-bit hologram recording provides recovery of information when 75% of the recorded hologram is lost. The developed decoder corrects the packet of dependent (grouped) errors that distort all bits of the hologram. The number of random independent errors that the decoder corrects can be up to 40% of the recorded information.
[Heavy Metal Pollution Characteristics and Influencing Factors in Baoji Arban Soils].

In order to analyze the sources and factors influencing heavy metal pollution in industrial cities, 62 surface layer (0-20 cm) soil samples were collected in Baoji City for analysis using inductively coupled plasma mass spectrometry (ICP-MS). Cd, As, Cu, Pb, Zn, Cr, Mn, and Ni were detected in the samples. Based on geostatistical methods and geo-detector models, the soil heavy metal pollution load index (PLI) was selected as the best indicator of factors affecting heavy metal pollution. The Unmix 6.0 receptor model was also used to analyze the heavy metal source. The results showed that:① The average concentrations of Cd, Pb, Cu, Zn, As, Cr, Mn, and Ni in the surface soils of Baoji City were 0.77 mg·kg-1, 16.75 mg·kg-1, 40.52 mg·kg-1, 261 mg·kg-1, 17.03 mg·kg-1, 49.18 mg·kg-1, 331 mg·kg-1, and 30.52 mg·kg-1, respectively. These values exceeded the national secondary standard for Cd and Zn, which were 8.2 and 3.8 times more abundant, respectively, than in the background soils of Shaanxi Province, indicating that urban soils are seriously polluted with Cd and Zn. The average concentrations of Cu, As, and Ni also exceeded the background levels; ② The total pollution load index (PLIzone) for heavy metals in the surface soils of Baoji City was 1.36, which indicated mild levels of pollution, as analyzed using a geo-detector. Soil texture and distance to the city's railway had the strongest explanatory power for the distribution of pollutants (PD, H=0.040 and 0.026, respectively, with a combined explanatory power of 0.099); ③ Cd, Cu, and As mainly derive from "artificial sources" associated with industrial and agricultural activities, and Zn and Ni derive from traffic emissions. Cr, Pb, and Mn mainly derive from "mixed sources". Overall, the main source of heavy metals in the surface soils of the study area was classified as "mixed source" and soils were classified as having a mild level of pollution. These results provide scientific support for the prevention and control of heavy metal pollution in the soils of Baoji City.
TOPIC MODELING-BASED APPROACH FOR WORD PREDICTION USING AUTOMATA

Rapid development in digital data acquisition techniques have brought about massive extent of data. More than 80 percentage of these days records consists of unstructured or semi-structured data. The discovery of pattern and to analyze the textual content documents from massive volume of data is in trouble. Text mining is a prominent area of research as there is humongous amount of data already available. It is used to extract meaningful information from texts and has vast applications. Predicting the next word in a sentence, a text analysis application, is a challenging semantic modelling exercise. In this work, we have designed a model to automatically predict and suggest the next word to complete a sentence. To develop this model, we have used Latent Dirichlet Allocation, a topic modelling algorithm, for collecting predominant words under each topic and a Deterministic Finite Automata to maintain the syntactic meaning and structure of the sentence. The developed model is tested using a restaurant review dataset and validated in terms of coherence, perplexity, precision and recall which gives a competent prediction.
Digital Transformation of History? Data, Standards, Approaches

The article examines the methodological possibilities of digital transformation in actual historical research. In the context of trends in the datafication, the approach to historical sources as data is considered, with due regard to the distinction into human-readable and machine-readable. Standards for creating open linked data are presented as the main direction to integral interconnected data collections that can be reusable. Modern approaches to data analysis (CT, EDA, SNA) are described, which allow to take a new look at working with complex historical data (databases of structured and unstructured data). Thus, combining the technical principles of creating digital repositories and meaningful professional standards will guarantee stable results in the conditions of historical research datafication.
Modeling user interface design for panic button application for deaf people using user-centered design method

Abstract An emergency can occur at anytime, anywhere and at anyone. The local government offers an application called “Panic Button” as a solution to these problems. This application aims to help people who experience or see emergency conditions so that they can be followed up by the authorities. However, this application has not been targeted for disability users. The procedure for using this application still has weaknesses that make it difficult for users with hearing loss, especially when an emergency event is confirmed. Normally, emergency events confirmation uses telephone calls to the incident reporter (user). This confirmation method is a big problem for those users with disabilities to be able to confirm the details of emergency events. In this research, a user interface re-design was carried out at the application interface to be able to accommodate the users necessary. The user interface modeling is done using the User-Centered Design (UCD) method to produce a user interface design that suits the user’s characteristics and limitations. USE Questionnaire was used to test the resulting prototype and results in a usability value of 87% with an excellent category after the third iteration. These results indicate that the results of the re-design that have been done have met the needs of the deaf, with the use of appropriate communication for people with hearing loss so that they can confirm emergencies properly.
BERT of all trades, master of some

This paper describes our results for TRAC 2020 competition held together with the conference LREC 2020. Our team name was Ms8qQxMbnjJMgYcw. The competition consisted of 2 subtasks in 3 languages (Bengali, English and Hindi) where the participants’ task was to classify aggression in short texts from social media and decide whether it is gendered or not. We used a single BERT-based system with two outputs for all tasks simultaneously. Our model placed first in English and second in Bengali gendered text classification competition tasks with 0.87 and 0.93 in F1-score respectively.
Sparse-to-Dense Depth Completion in Precision Farming

Autonomous driving in agriculture can be eased and be more safe if guided by dense depth maps, since dense depth maps outlines scene geometry. RGB monocular image has only naive information about depth and although LiDAR has accurate depth information, it can only provide sparse depth maps. By interpolating sparse LiDAR with aligned color image, reliable dense depth maps can be created.
Detecting ambient/focal visual attention in professional airline pilots with a modified Coefficient K: a full flight simulator study

Flight instruments, from which a pilot monitors an aircraft, usually serve as areas-of-interest (AOI) that help to investigate the dynamics of the visual behavior of pilots. Consequently, several meta-metrics have been proposed to provide more information than common variables such as the number of fixations and saccades, the fixation durations, the saccade amplitude, and the standard dwell time. Researchers are however still searching for the best metrics for better insights into eye movements during scene exploration or inspection. In this work, we propose extending the formerly well established κ-coefficient metric defined by Krejtz et al. [2016] that allows discerning ambient and focal attention. Using AOI and transitions between them, we have derived a new measure that enables assessment of the distribution of visual attention (via eye-tracking data). Professional pilots’ eye movements were recorded while they were performing a flight scenario with full automation, including phases of flight (take-off, cruise, landing). Our analysis suggests that the take-off, cruise, and landing phases call for checking of specific areas, evidenced by the number of fixations and their durations. Furthermore, we compare our metric to the standard κ-coefficient and validate our approach using data collected during an experiment with 11 certified aircraft pilots. Here, we were able to show that the derived metric can be an interesting alternative for visual behavior investigation. The modified κ-coefficient can be used as a metric to investigate visual attention distribution, with application in cockpit monitoring assessment during training sessions or potentially during real flights.
Machine Learning Approach to Suffix Separation on a Sandhi Rule Annotated Malayalam Data Set

This article explores in depth various sandhi (joining) rules in Kerala’s Malayalam language, which play a vital role in framing of the inflected and agglutinated forms of words and their compounds. It discusses significant progress in a scientific method to generate a specific annotated data set of Malayalam words that would be useful in many Natural Language Processing tasks which involve Malayalam preprocessing. The article discusses the results and issues encountered in developing this word-splitting tool for Malayalam, mainly in the context of improving the alignments between parallel texts that form a core resource in the Machine Translation task.
MT Problems and its MTPE Suggestion with Regard to Structural Differences between English and Korean

This paper suggests how to effectively post-edit the English-Korean machine translation (MT) by examining the impact of the structural differences between the Korean and English languages on the quality of the machine-translated Korean text. First, this study collected English expressions whose equivalence in Korean language are hard to form because of the differences in their respective basic language structure. Based on the Lee’s (2000) model, four topics were chosen: English sentences using inanimate nouns for abstract concepts, English passive constructions using diverse transitive verbs, and English expressions of movement and cause-and-effects. After machine translating the English text into Korean using Google and Papago engines, the study examined whether the machine-translated output meets the lexical, syntactic and semantic requirements of Korean (Lee, 2000; Nunan, 2004). Based on this study, several tips are suggested for fixing problems in the MT of English-Korean text. It is noted that the more different English is from Korea in syntactic and cognitive levels, the less productive MTPE is likely to become. Consequently, the findings stress the importance of MTPE to be a more syntactically competent and cognitively mindful process for the productive MTPE of English-Korean translation.
Far Point Algorithm

In some data sets the number of categories (i.e. classes) that are represented is not known in advance. The process of discovering these categories can be difficult, particularly when a data set is skewed, such that the number of data points of some classes may greatly exceed those of other classes. Rare category detection algorithms address this problem by trying to present a user with at least one data point from each category, while minimizing the overall number of data points presented. We present an algorithm based on active and semi-supervised learning that finds category clusters using a query selection strategy that maximizes the distance from a set of already labeled data points to a query data point. We evaluate the algorithm's performance on artificially skewed versions of the MNIST data set as a rare category detection algorithm, investigating differences in performance due to both the effects of relative frequency and inherent class structure differences in feature space.
GPGPU Programming for CS Undergraduates

Over the past few decades, Central Processing Units (CPUs) have improved exponentially, often doubling performance on a year-by-year basis. Recently progress has slowed due to the limits imposed by energy and heat. At present, instead of increasing CPU performance, manufacturers have focused on improving system performance by increasing the number of CPUs (known as cores) on a single silicon chip. A single application can benefit from the additional cores by being threaded; multiple threads can execute on different cores in parallel. Another source of parallel execution is the Graphical Processing Unit. GPUs have been used for graphics for decades. In recent years, programming interfaces have been introduced that allow the GPU to solve a variety of data parallel problems, not just problems involving graphics. This use of the GPU is known as General Purpose computing for Graphics Processing Units (GPGPU).
Digital First: Not Just an Add-On

This manuscript will highlight MTLT's digital first philosophy, which is not just an add-on to the mathematics, but a partner, working hand in hand with the mathematics to enhance the experience.
The effects of seaweed powder to the properties of polymer modified concrete

Abstract Eucheuma Cottonii is a sea plant that commonly known as seaweed grows abundantly and part of the products in agricultural industries. The effects of alginate from seaweed benefited the properties of composites due to its advantage as a hydrophilic gelling material. This paper investigates the effects of Eucheuma Cottonii in polymer modified concrete. The investigations covers on the physical properties and mechanical properties of the polymer modified concrete based on different percentages of seaweed powder. The percentages chosen are 10%, 20%, 30%, 40% and 50% cement replacement inside polymer modified concrete. The effects on the seaweed powder percentage to the properties of polymer modified concrete are determined through microstructural, water absorption, pulse velocity, compressive strength and indirect tensile strength. Findings from the experimental works show that an optimum seaweed powder percentage into polymer modified concrete is 20% with improvement shows from the mechanical properties. It can be concluded that, seaweed powder potentially acted as a binder in polymer modified concrete and suitably chosen as a sustainable material inside polymer modified concrete.