dataesr/scientific-paragraphs-categorization huggingface dataset:
B. ASSOCIATION RULE MINING (ARM) To understand the relationship between different data items in transactional datasets and to find out interesting patterns and correlations, Agrawal et al. [6]  presented the central concept of ARM using binary representations of data items as shown in Table  5. However, ARM is also presented for numerical data items as quantitative ARM  [50] , numerical ARM  [51] ,  [52]. ARM is highly effective in discovering relations and interesting associations among data items using different measures of interestingness  [6] ,  [53]  and it is a prevalent technique that plays a crucial role in market basket data analysis, bioinformatics, ocean, land, and medical diagnosis. In the original settings, association rules are extracted from transactional datasets composed of a set I = {i 1 ,.
, i n } of n binary attributes called items and a set D = {t 1 ,. , t n }, t k ⊆ I , of transactions called database. An association rule is a pair of itemsets (X , Y ), often denoted by an implication of the form X ⇒ Y , where X is called the antecedent (or premise) and Y is called the consequent (or conclusion), X ∩ Y = ∅. To select interesting association rules, the following are the most popular measures of interestingness in ARM.
Lift(X ⇒ Y ) = Supp(X ∪ Y ) Supp(X ) × Supp(Y ) In ARM, when the number of association rules is too large to be presented to a data mining expert or even treated by a computer, measures of interestingness can filter the interesting association rules. After support, confidence, and lift, more than fifty different measures of interestingness are in the literature  [53] ,  [55] ,  [56]. These measures of interestingness are discussed in detail in the literature  [57] ,  [58]. Initially, ARM was limited to large transactional datasets. Still, later, Han et al. ,  Lu et al. , Imielinski et al. , and Nguyen et al. [40] ,  [43] ,  [59] ,  [60]  presented different views on multi-level and multi-dimensional ARM. Over the years, different ARM frameworks  [34]  and the use of ARM in varied application scenarios  [61] ,  [62]  have also been discussed in the state of the art  [63].
1) MULTIDIMENSIONAL VIEW OF ARM More recently, ARM has been adapted to the multidimensional case  [43]  and multitask-based ARM  [64]. In multidimensional setting of ARM, datasets are composed of a set D = {S 1 ,. , S n } of dimensions, and an n-ary relation between them, i. e. they are formally tuples (S 1 ,. , S n , R) with R ⊆ S 1 × • • • × S n. In ''Multitask-based'' ARM, highly frequent association rules for different ARM tasks are referred as ''single-task'' rules which are Later combined together to generate the global results, i. e, ''multitask rules''. Multidimensional association rules are rules between two so-called associations that generalize the notion of itemset. They are defined as the Cartesian products of subsets of dimensions. The set of dimensions used in an association X is called its domain and is noted dom(X ).
V. THE FRAMEWORK, EVALUATION AND EXPERIMENTS In this section, the framework for the unification of three DSTs is presented. As a data science process provided by Schutt and O'Neil  [66] , the proposed framework is modular in design and every module in the framework can be displaced. In Fig. 6 , we illustrate the high-level abstraction of the framework and based on the process of knowledge discovery in databases (KDD)  [36] , a detailed overview proposed framework is given in Fig. 7. The framework consists mainly of seven major components. The Graphical User Interface (GUI) allows decisionmakers to communicate with the framework to process the raw data. The data pre-processing includes various operations and checks, including discretization, cleaning, e. g. , checking for corrupt data, reviewing the types of data, transforming and integrating data in useful formats, etc.
A. IMPLEMENTATION OF THE PROPOSED FRAMEWORK To demonstrate the usability of the proposed framework, an instance of the framework is developed using ASP. NET, an open-source framework for developing web applications. The resulting tool is an example of a next-generation decision support tool implemented by adopting the proposed framework. A summary of technologies and framework used for the implementation of the tool is given in Table  11. The programming code and other instructions on how to use the proposed tool are available in the GitHub repository  [15]. The AJAX request methods are used throughout the tool's implementation to establish a connection between the client and server. JSON serialization and deserialization functions convert. NET objects (strings) to JSON format and JSON format to.
NET objects. We use Oracle database and Microsoft Excel as databases and for OLAP, we have used relational algebra in the tool. The tool first recognizes different kinds of data (discretized, numerical, categorical) and then develops generalized association rules for the various combinations of influencing factors and target columns. In the tool, if the selected target column is numerical, then the aggregate function is used, and the average value of the target column is calculated against the chosen influencing factors by the following SQL query; Select AVG (target column) from table group by influencing factors. If the specified target column is numerical, the aggregate function is employed in the tool, and the average value of the target column is determined against the chosen influencing factors using the SQL statement; Select AVG (target column) from table group by influencing factors.
1) ACIF GENERATOR In the tool, we have developed a function for ACIF generator and implemented it in the proposed framework. The ACIF generator is developed to select the target column and influencing factors to generate all possible combinations of the selected target column and influencing factors. First, the generator identifies the column combinations from the dataset and generates reports for the target column and influencing factors. The pseudo-code for the ACIF generator and ACIF report generator is given in Listing 1. In the pseudocode, the CREATE_COMBINATIONS function is defined to pass the information of influencing columns and the number of columns. This function calculates the possible combinations of the selected influencing factors. In line 15, the GENERATE_REPORT function is defined to generate the reports for various combinations of influencing factors against target columns.
2) MATHEMATICAL DESCRIPTION OF THE ACIF GENERATOR Let T be a database Table with multiple columns C = {X 1 : T 1 ,. , X n : T n }, where X 1. X n represent column names and T 1. T n represent column types. To calculate various operations of SR, OLAP and ARM for T , ∀1 ≤ ψ ≤ n ∀D = {X 1 : D 1 ,. , X ψ-1 : D ψ-1 } ⊆ C(D i = d 1 ,. , d ni ) ∀d 1 ∈ D 1 ,. , d ψ-1 ∈ D ψ-1 Here, D is the subset of C and the influencing factor. ∀Y : R ∈ C or Y = X ij : B, X i : d i ∈ C Y is the target column, R is the real-valued numbers then. Support: P(Y , X 1 = d 1 ,. , X ψ-1 = d ψ-1 ) (29) Average: E(Y | X 1 = d 1 ,. , X ψ-1 = d ψ-1 ) (30) Lift: E(Y | X 1 = d 1 ,. , X ψ-1 = d ψ-1 ) E(Y ) (31).
B. EXPERIMENTS ON THE PROPOSED FRAMEWORK The experiment section demonstrates the potential of the introduced framework. The tool is evaluated on two real datasets and one synthetic dataset. The tool is tested on a computer with an Intel(R) Core(TM) i5-8265U CPU @ 1. 60GHz, 1800 Mhz, 4 Core(s), 8 Logical Processor(s), 16 GB RAM and Windows 10 x 64 operating system. The programming code, datasets, and other necessary instructions about the tool are available in the GitHub repository  [15]. The datasets are summarized in Table  12 , in which we highlight the number of records, number of attributes, and number of numeric attributes. The first Dataset, New Jersey (NJ) School Teacher Salaries (2016)  [67]  contains 138, 715 records and 15 attributes, while another real dataset, DC public government employees  [68]  contains 33, 424 records, which are huge in numbers to check the performance of the tool.
In the table, we have described the dataset attributes with their types. Dataset NJ Teacher Salaries (2016) consists of salary, job, and experience data for the teachers and employees in New Jersey schools. The data are sourced from the (NJ) Department of Education. The second real dataset is a list of DC public government employees and their salaries in 2011. The second data set is sourced from the washington times via freedom of information act (FOIA) requests. We have also tested the proposed tool on the sample dataset UDS1  [69]. This dataset contains 1, 470 records with different combinations of numerical, categorical, and discretized attributes. A feature list obtained by parsing the UDS1 dataset is summarized in Table  13. In the datasets, the target column is the one for which we are computing ARM operations, i.
We have analyzed the performance of the proposed tool with three datasets. The performance of the tool varies with the number of records. If the number of records in a dataset is high, the tool has higher running time and slow performance. In Fig. 9 , the performance variation induced by the number of records in a dataset is shown. The Dataset NJ Teacher has a huge number of records; therefore, its running time is 36, 650 milliseconds. Dataset DC Public Employees has 33, 424 records. Therefore, its running time is 22, 090 milliseconds, and the sample dataset UDS1 has a small number of records, i. e. , 1, 470; therefore, its running time is 2, 030 milliseconds. Running time and performance variation of the proposed tool induced by the number of records in the datasets is shown in Fig. 10. A summary of records in datasets and performance variation of the tool with the datasets is given in Fig.
Moreover, a decision-maker must create a new project for each dataset and repeatedly modify its columns and combinations. Therefore, in Fig. 13 , we provide a sample use case to generate all possible combinations of influencing factors against the target column in RapidMiner. Additionally, in RapidMiner, the influencing factors and their values are stored in a single column called the 'conclusion' column as ''influencing factors=value''. The target column and its values are stored in the 'premises' column as ''Target Column=value''. A sample list of premises and conclusions generated by RapidMiner for the influencing factors and target column is displayed in Table  14. The representation of the target factors and influencing factors is difficult to understand. It is hard for decision-makers to identify each factor and its instance from the multiple tables.
We are also working on a new measure to identify any instance of Simpson's paradox in Big Data. Implementing these measures in the proposed platform will enable decision-makers to determine the genuine and unbiased impact factors. The proposed tool has some performance issues with large datasets momentarily; therefore, we plan to scale up the performance of the tool by utilizing high-performance computing (HPC) infrastructure and making it available to the decision-makers. We intend to build it as a trustworthy platform and grow as a service provider in the near future.
The tool provides unified usage of DSTs in a classical decision support process and clarifies in how far the operations of SR, ARM, and OLAP can complement each other in understanding data, data visualization and decision making. The tool was developed on the basis of an open-source framework and tested with two real datasets and one synthetic dataset. The results and performance of the tool show valuable contributions towards developing the next-generation DSSs. DIRK DRAHEIM received the Ph. D. degree from Freie Universität Berlin and the Habilitation degree from Universität Mannheim, Germany. He is currently a Full Professor of information systems and the Head of the Information Systems Group, Tallinn University of Technology, Estonia. The Information Systems Group conducts research in large and ultra-large-scale IT systems.
Listing 1 1 Pseudo-Code to Find the ACIF and Generate ACIF Reports Therefore, we also denote the WHERE clause as an influencing factor for the target column in OLAP computations. The UDS1 dataset consists four columns with different data types; age is discretized, gender is categorical, education is categorical and DailyRate is numerical. The column Age has the age groups as 20 -30, 30 -40, etc. , 1: function CREATE_COMBINATIONS(influencing_Columns[], numberofColumns) 2: if numberofColumns == \text{0}~then 3: return [] 4: return_Values = [] 5: for i = \text{1}~to LENGTH(influencing_Columns) do 6: colName = influencingColumns[i] 7: partialLst = REMOVE_COLUMN(i,influencingColumns) 8: for each: j in CREATE_COMBINATIONS(partialLst, numberofColumns -1) do 9: APPEND_TO(return_Values,ADD_FIRST(colName,j)) 10: end for 11: end for 12: return return_Values 13: end function 14: 16: function GENERATE_REPORT(table_Name, target_Column, influencing_Columns[]) 16: for i = \text{1}~to LENGTH(influencing_Columns) do 17: column_Combination = call:CREATE_COMBINATIONS(influencingColumns,i) 18: for each: Combinations in column_Combination 19: "SELECT COUNT( * )/ (SELECT COUNT( * ) FROM "+table_Name+") AS SUPPORT, AVG("+target_Column+") AS LIFT, AVG("+target_Column+") AS AVG_targetColumn, "+ Combinations +" FROM "+table_Name +" GROUP BY "+ Combinations +" ORDER BY "+ Combinations;" 20: end for 21: end for 22: end function the target columns.
Methods: We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence. Results: Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV).
]. This collection method is called "passive data gathering"; no intervention is necessary, reducing the weight of the observers and mitigating cognitive bias of the clinicians. This term is opposed to "active data gathering, " which requires the involvement of the patient in the collection of the data (e. g. , Ecological Momentary Assessment EMA) (Figure  1 ). Passive data are collected automatically in real time, without requiring any input from the user and relying on tools such as the accelerometer (number of steps, motor behavior), GPS, mobile phone-based software sensing (e. g. , sleep analysis), or wearables that measure HR, heart rate variability (HRV), galvanic skin conductance, temperature, blood pressure or others indicators that could be considered as potential biomarkers of certain psychiatric disorders.
Vocal recording Depressive disorders Shin et al. (37)  used machine learning on data extracted from semistructured interview recordings. Previous research showed that depressed patients had simpler, lifeless voices with lower volume. Their movements of the vocal tract were slow and they spoke in low voices. Thus, the features extracted are from four aspects: namely glottal, tempo-spectral, formant, and other physical features. Glottal features included information about sound articulation with the vocal cords, obtained by parameterizing each numeric after drawing a waveform. They used it for calculating three parameters: the opening phase, closing phase, and closed phase. They extracted tempo-spectral features with the help of "librosa, " an audio processing toolkit. Temporal features refer to the time or length of the interval when participants continue an utterance.
Several studies combined temperature with actigraphy, making it possible to categorize body temperature throughout the day's phases, and adjust the amplitude with activity. While promising, voice analysis still remains one of the poorest study domains. The data provided come from speech text provided by smartphone and use of acoustic data automatically recorded. Other metrics can also be calculated: total speech activity, the proportion of speech during a day, analysis of voice and linguistic patterns, GPS location provided by environmental sounds, and verbal fluency tasks (letter, semantic, free word generation, and associational fluency). Activity of humans in daily life is often represented through GPS location or actigraphy on wearables that can be used to study physical activity, recording GMA, and sleep quality with CR.
Böröczky core, Kahle core In the BigBoro software package (see Appendix A), we consider two cores. The Böröczky core  [11]  consists of 20 disks (see Fig. 1a ). 1a ). The Kahle core  [15] , with a total of 8 disks, is constructed from two disks at coordinates (1, 1), and (1 + √ 3, 0), using the same symmetries (see highlighted disks in Fig. 1b ). The Böröczky core is locally stable if repeated periodically in a central simulation box that fully encloses the core disks, with L/2 = 3 + √ 6/2 + 1/ √ 2. The Kahle core is collectively stable if the outer-disk centers are placed at the box boundaries, with L/2 = 1 + √ 3. It is locally stable if the outer disks are fully enclosed in the central simulation box, with L/2 = 2 + √ 3.
If the first-order term in eq. (  5 ) vanishes for all i and j, the separation between disks in contact cannot decrease. It then increases-to second order in the displacements ∆ i , ∆ j -if ∆ i = ∆ j , so that contact is lost. The first-order term writes as a product of twice an "escape matrix" M esc of dimensions N contacts × 2N with a 2N - dimensional vector ∆ = {∆ 1 ,. , ∆ N } = {∆ x 1 , ∆ y 1 , ∆ x 2 , ∆ y 2 ,. }. The row r of M esc corresponding to the contact between i and j has the following four non-zero entries M esc r,2i-1 = x i -x j , M esc r,2i = y i -y j , M esc r,2j-1 = -(x i -x j ), M esc r,2j = -(y i -y j ). (6) The BigBoro software package (see Appendix A) solves for M esc ∆ = 0 (7) using singular-value decomposition. For the k = 5 Böröczky packing with the Kahle core, we find 28 vanishing singular values, that comprise the two uniform translations.
Analysis of measured escape times ECMC is a continuous-time MCMC method, and its continuous time t MCMC takes the place of the usual count of discrete-time Monte-Carlo trials. In ECMC, each chain corresponds to a segment [t MCMC , t MCMC +τ chain ) of Monte-Carlo time. However, ECMC is event-driven. The time t, and especially the escape time t esc , are integers, and they count events. The computational effort in hard-disk ECMC is O (1) per event, using a cell-occupancy system that is also implemented in the BigBoro software package. In several of our algorithms, the times t and t MCMC are not proportional to each other, because the mean-free path (roughly equivalent to the time between events) evolves during each individual run.
Escape times may also play an important role in the study of glasses and in granular matter, where the high or even infinite pressures favor local configurations that resemble the mutually blocked disks in the ε-relaxed Böröczky configurations. We finally point out that the very concept of locally blocked packings naturally extends to higher dimensions. layers (see Section 2. 1. 3). The geometric convex polygonal chain A geo with different attenuation parameters φ, and the circular A circ are implemented (see Section 2. 2. 1). The core, the number of layers, and the convex polygonal chain are specified using command-line arguments that are described in the README. md file of the package, as well as in the output of the script's --help command-line option. The docstrings of the script contain further information.
The Python script construct packing. py uses arbitrary-precision decimal floating-point arithmetic (using the decimal module of Python's standard library). Two additional command-line options specify the number of decimal digits, and the precision b of the bisection search for the value g < 2 that renders the Böröczky packing compatible with periodic boundary conditions (see Section 2. 1. 3). In general, b should be smaller than the number of places of the decimals. The script modifies g < 2 until 1 ≤ x B k -x A k ≤ 1 + 10 -b. If the bisection search succeeds, the script first tests that no pair of disks has a distance smaller than 2 -10 -b+2. Second, it checks that every disk has at least three contacts with distances in the interval [2 -10 -b+2 , 2 + 10 -b+2 ], and, finally, that the total number of contacts agrees with eq.
A. 2 Python script collective escape modes. py The double-precision Python script collective escape modes. py identifies the orthonormal basis vectors ∆ a of the escape matrix M esc from a packing x (see eq. (  6 )) that have zero singular values. This is the solution space for 2Ndimensional displacements ∆ = {∆ x 1 , ∆ y 1 , ∆ x 2 , ∆ y 2 ,. } that have a vanishing first-order term in eq. (  5 ) and thus for collective infinitesimal displacements ∆ of all disks that escape from the packing. The script asserts that the configurations x + 10 -8 ∆ a are without overlaps and that all contacts persist at a precision 10 -8 (contacts are lost at second order only). Furthermore, the script ensures that the uniform translations of all disks along the x-and yaxis are part of the solution space. Finally, the basis vectors ∆ a are stored in a human-readable output file, and optionally represented as in Fig.
2. The input filename of the packing, and the output filename for the collective escape modes are specified in command-line arguments. Further optional arguments specify the filename for the plots of the escape modes, and the system length of the central simulation box (that is unnecessary for packings generated by the Python script construct packing. py in which case the system length is parsed from the packing file). The package's README. md file, as well as the --help command-line option and the docstrings of the script contain more detailed information.
A. 3 Go application go-hard-disks The Go application go-hard-disks relies on a cell-occupancy system for the efficient simulations of large-N hard-disk systems using several variants of the ECMC algorithm. Straight, reflective, forward, and Newtonian ECMC are implemented. In its current form, it samples the maximum nearest-neighbor distance d(t) (see eq. (  8 )) after a given sampling time. All computations use a fixed number of mantissa bits (in base 2) that may exceed the usual 24 or 53 bits for single-or double-precision floating-point values. (We use the math/big package of the Go standard library for the arbitrary-precision arithmetic. ) The ECMC variant, its parameters (as for example the sampling time or chain time), and further specifications (the number of mantissa bits, the cell specifications, the filename for the initial configuration, etc.
A. 4 License, dependencies, software versions The BigBoro software package is published as an open-source project under the GNU GPLv3 license. It is available on GitHub as part of the JeLLyFysh organization. 1 Users can clone or fork the repository to study the code, and to run the Python3 scripts construct packing. py and collective escape modes. py, and the Go application go-hard-disks. The Python3 scripts rely on NumPy as their only external dependency  [39]. Optional plotting also requires the Matplotlib library  [40]. They are expected to work with any Python3 version and any NumPy version ≥ 1. 20. Python scripts were tested with Python 3. 9 and NumPy 1. 21. The Go application only requires the Go standard library. It is expected to work with any Go version ≥ 1. 13 (tested with 1. 16).