Reviewerly/exHarmony huggingface dataset:
Simple Modification for an Apriori Algorithm With Combination Reduction and Iteration Limitation Technique

Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset. An a priori algorithm generates a combination by iteration methods that are using repeated database scanning process, pairing one product with another product and then recording the number of occurrences of the combination with the minimum limit of support and confidence values. The a priori algorithm will slow down to an expanding database in the process of finding frequent itemset to form association rules. Modification techniques are needed to optimize the performance of a priori algorithms so as to get frequent itemset and to form association rules in a short time. Modifications in this study are obtained by using techniques combination reduction and iteration limitation. Testing is done by comparing the time and quality of the rules formed from the database scanning using a priori algorithms with and without modification. The results of the test show that the modified a priori algorithm tested with data samples of up to 500 transactions is proven to form rules faster with quality rules that are maintained.Keywords: Data Mining; Association Rules; Apriori Algorithms; Frequent Itemset; Apriori Modified;
Enhanced CNN-LSTM approach for Human Activity Recognition

Research in Deep Learning (DL) based computer vision techniques attracted wider attention in the research area. Human activity recognition by digital devices still lacks to perform well in different activity environments. To address this challenge, we proposed the hybrid framework called Enhanced CNN-LSTM approach for the Human Activity Recognition model. In recent days, huge theft and violent activities are happening everywhere, unable to find the culprit even though many technologies exist. The proposed deep learning model monitors and detects human activity through the hybrid approach, which combines both Convolutional Neural Network for spatial and temporal feature extraction and finding the discriminative feature identification using the technique Long Short Term Memory algorithm. The technique LSTM aims to predict the sequences. The experiments are conducted on the UCF10 Human Activity Recognition datasets, which results in improved performance of activity recognition.
Musical Thinking and Composing Technique in Terms of Computer Technology Development

Determining the place of music computer technology in modern history requires a thorough consideration of musical processes and factors of the first half of the XX century, as well as the study of relevant discoveries, concepts and experiments in terms of new music technologies. The purpose of the article is to identify the patterns of development of musical art in relation to modern social life, appeal and thorough study of music of different styles and eras in their systemacity. The methodological basis of the study are dialectical and systemic methodologies used in the field of cultural studies. General scientific and logical methods of analysis, synthesis, induction and deduction, historical and comparative research of the problem are used. The historical evolution of musical instruments is deeply connected with the creative processes that take place in the fields of composition, acting skills, musicology and education. This led to the search and improvement of musical language, enrichment of compositional methods, techniques and means of texture of musical works and, as a consequence, the birth of electronic instruments.
Digitisation and Role of Academic Libraries

In the cutting edge year of academic library these days a large portion of the library are center in designing their whole library space in tackle the issues on the quantity of door count and circulation activities. Thought on the significance of different spaces which can give satisfaction towards the users need in a roundabout way open the eyes of academic library in doing this activity as the primary objective in accomplishing the objective. The incentive on entire arranging is truly needed as to help the two exercises, which is teaching and learning.
Technology of formation of future pedagogue’s commitment to designing the educational project content

The relevance of the research is accounted for by the need to reshape the system of professional training of future pedagogues towards realisation of the sought educational function. The training process should envisage the formation of young specialist’s professional qualities in adapting the content of educational work to learners’ individual characteristics and educational needs. Due commitment to selecting and structuring (designing) the content of educational projects is a significant component of such qualities. In the research 680 students and 836 learners of general education organisations took part. The following methods were applied: active learning, group expert assessment, observation, pedagogical experiment, authorial diagnostic tools, case studies. Pearson’s criterion χ2 was used to establish statistically credible differences between the control and experimental groups. It was revealed that the efficiency of future pedagogue’s training within the framework of the explored activities increases with the use of due technology for forming a commitment to constructing the educational project content. The essence of the professional training technology is teaching the future pedagogue to design (select and structure) the elements of the educational project thesaurus with regard for the learners’ individual characteristics and educational needs, and to apply it in practice in addressing the educational targets. Following the implementation of the said technology, the experimental group showed a significant increase in the number of students demonstrating high and medium levels of preparedness for the above work (χ2 = 5.99; p = 0.05). A significant difference was found between the groups in respect of three of the four analysed components: motivational (χ2 = 9.14; p &lt; 0.05), cognitive (χ2 = 12.8; p &lt; 0.05), evaluative-reflexive (χ2 = 7.8; p &lt; 0.05). At the same time, no difference was found between the groups in terms of the activity component: (χ2 = 2.45; p &gt; 0.05). This fact is accounted for by the same requirements for the educational process results in both groups and by the generalised mission of preparing students for project-specific and functional activities. The developed technology may be used in the process of practice-oriented training of the future and presently functioning pedagogues for the realisation of the educational mission.
Monoclonal Gammopathy of Undetermined Significance Clinic during the Coronavirus Disease-19 Pandemic: Caring for the Vulnerable in an Academic Medical Center

Background: Patients with monoclonal gammopathy of undetermined significance (MGUS) have clinical features including older age, presence of medical comorbidities, susceptibility to infections, and thrombotic tendencies which are relevant when assessing their risk during the coronavirus disease (COVID-19) pandemic.Objective: To study the vulnerability of patients with MGUS during the COVID-19 pandemic, we assessed the local management of MGUS patients and their clinical outcomes.Methods: Retrospective chart reviews were performed for all patients with MGUS seen at a university medical center clinic (2014-2020).Results: A total of 228 MGUS patients were included; 211 patients are alive, 7 patients died before the pandemic, and 10 patients died since the pandemic declaration.The mean age and the overall survival (OS) of the patients who died before versus during the pandemic were 83.0 versus 75.2 years, p = 0.4, and OS 40.6 versus 53.2 months, p = 0.3, respectively.One patient died of COVID-19.Nine patients had venous thromboembolisms (VTE), all of which occurred before the pandemic onset.Conclusions: There were no significant differences found in the mean age or OS of the MGUS patients who died before versus after the pandemic onset.An increase in VTE rates was not seen.Study results are limited by small patient numbers.(REV INVEST CLIN.
SMate: Synthetic Minority Adversarial Technique

In important prediction scenarios, data-sets are naturally imbalanced, for instance in cancer detection: a small minority of people may exhibit the disease. This poses a significant classification challenge to machine learning algorithms. Data imbalance can cause lower performance for the class of interest, e.g. classifying with high precision that the person has cancer. When training data is abundant, a possible approach is to down-sample the majority class, thus restoring balance. Another prevalent approach is weighting, accelerating learning for minority class training examples. Synthesis is a major alternative, producing examples of the minority class, adding them to the training set to overcome the class imbalance. The Synthetic Minority Over-sampling Technique, SMOTE is widely applied, but it was not developed for image data. Rather, this research applies Generative Adversarial Networks, which generate image examples drawn from the minority class distribution. The novel SMate approach leverages GAN minority-class image generators, which benefit from Transfer Learning from majority-class image generators. Consequently, SMate outperforms SMOTE for imbalanced image data-sets.
Utilization of Air Conditioning Heat Waste as Cloth Drying Energy Source

Air Conditioning (AC) as an air conditioner has been used on one side only as a room cooling. The cooling process with Air Conditioning produces heat as a result of the work of the condenser component in the AC. This heat can be used as an alternative energy source. This study aims to utilize hot air discharging Air Conditioning as an energy source for drying clothes. This development is based on the Thosiba AC specification. The specification data is used to get the coefficient of performance (COP) and heating effect. Development is also carried out for studies in the form of experiments. The results of the study indicate that the application of AC exhaust heat has the potential to be developed as an alternative energy in the process of drying clothes.
APPLICATION OF AUGMENTED REALITY TOOLS TO THE DESIGN PREPARATION OF PRODUCTION

The paper describes the application of augmented reality tools to create an auxiliary interactive tool in the field of design. To mediate it, an application with AR operation based on marker tracking has been designed. The created application works with the mobile devices platform. Two types of markers have been used in the application. To display basic information, buttons with functions are added to the scene, such as part information, rotation and change of position in the X, Y, Z direction. The application runs on a mobile phone, with a built-in camera. The marker is displayed in the drawing's lower left corner. The model is positioned so that it appears above the view of the part in the drawing. The task of the application is to support intelligent tools applicable in the design stage of production preparation, in the creation of drawing documentation.
Signals classification based on IA-optimal CNN

The versatility of the existing A-optimal-based CNN for solving multiple types of signals classification problems has not been verified by different signals datasets. Moreover, the existing A-optimal-based CNN uses a simplified approximate function as the optimization objective function instead of precise analytical function, which affects the signals classification accuracy to a certain extent. In this paper, a classification method called IA-optimal CNN is proposed. To improve the stability of the classifier, the trace of the covariance matrix of the weights of the fully connected layer is used as the optimization objective function, and the parameter optimization model is established without any simplification of the optimization objective function. In addition, to avoid the difficulty of not being able to obtain the analytical expression formula of the partial derivative of the inverse matrix with regard to the networks parameters, a novel dual function is introduced to transform the optimization problem into an equivalent binary function optimization problem. Furthermore, based on the above analytical solution results, the parameters are updated using the alternate iterative optimization method and the accurate weight update formula is deduced in detail. Five signals datasets are used to test the universality of the IA-optimal CNN in signals classification fields. The performance of IA-optimal CNN is showed, and the experimental results are compared with the existing A-optimal-based classification algorithm. Lastly, the following conclusion is proved theoretically: For the A-optimal-based CNN, the trace of the covariance matrix will continue to decrease and approach a convergence value in the iterative process, but it is impossible for the networks to strictly reach the A-optimal state.
On the Number of Affine Equivalence Classes of Boolean Functions and q-Ary Functions

Let R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (r,n) be the rth order q-ary Reed-Muller code of length q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , which is the set of functions from \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> to \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> represented by polynomials of degree ≤ r in \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> [X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , ... ,X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ]. The affine linear group AGL(n,\mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> ) acts naturally on R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (r,n). We derive two formulas concerning the number of orbits of this action: (i) an explicit formula for the number of AGL orbits of R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (n(q-1),n), and (ii) an asymptotic formula for the number of AGL orbits of R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (n,n)/R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (1,n). The number of AGL orbits of R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (n,n) has been numerically computed by several authors for n ≤ 31; the binary case of result (i) is a theoretic solution to the question. Result (ii) answers a question by MacWilliams and Sloane.
Guided Visual Exploration of Relations in Data Sets

Efficient explorative data analysis systems must take into account both what a user knows and wants to know. This paper proposes a principled framework for interactive visual exploration of relations in data, through views most informative given the user's current knowledge and objectives. The user can input pre-existing knowledge of relations in the data and also formulate specific exploration interests, which are then taken into account in the exploration. The idea is to steer the exploration process towards the interests of the user, instead of showing uninteresting or already known relations. The user's knowledge is modelled by a distribution over data sets parametrised by subsets of rows and columns of data, called tile constraints. We provide a computationally efficient implementation of this concept based on constrained randomisation. Furthermore, we describe a novel dimensionality reduction method for finding the views most informative to the user, which at the limit of no background knowledge and with generic objectives reduces to PCA. We show that the method is suitable for interactive use and is robust to noise, outperforms standard projection pursuit visualisation methods, and gives understandable and useful results in analysis of real-world data. We provide an open-source implementation of the framework.
ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables

Estimating the gradients for binary variables is a task that arises frequently in various domains, such as training discrete latent variable models. What has been commonly used is a REINFORCE based Monte Carlo estimation method that uses either independent samples or pairs of negatively correlated samples. To better utilize more than two samples, we propose ARMS, an Antithetic REINFORCE-based Multi-Sample gradient estimator. ARMS uses a copula to generate any number of mutually antithetic samples. It is unbiased, has low variance, and generalizes both DisARM, which we show to be ARMS with two samples, and the leave-one-out REINFORCE (LOORF) estimator, which is ARMS with uncorrelated samples. We evaluate ARMS on several datasets for training generative models, and our experimental results show that it outperforms competing methods. We also develop a version of ARMS for optimizing the multi-sample variational bound, and show that it outperforms both VIMCO and DisARM. The code is publicly available.
A Hybrid Swarm Intelligence Based Feature Selection Algorithm for High Dimensional Datasets

High dimensional datasets expose a critical obstacle in machine learning. Feature selection overcomes this obstacle by eliminating duplicated and unimportant features from the dataset to increase the robustness of learning algorithms. This paper introduces a binary version of a hybrid swarm intelligence approach as a wrapper method for feature selection that gathers between the strengths of both the grey wolf and particle swarm optimizers. This approach is named Improved Binary Grey Wolf Optimization (IBGWO). The original version of this hybrid approach was proposed in the literature with a continuous search space as a high-level hybrid form, which runs the optimizers one after the other. Two different types of transfer functions, named S-Shaped and V-Shaped, are applied in this work to turn continuous data into binary. Nine of high-dimensional small-instance medical datasets are employed to assess the proposed approach. The experimental results demonstrate that IBGWO based on S-Shaped (IBGWO-S) outperforms the binary particle swarm and the binary grey wolf optimizers on six out of nine datasets according to the classification accuracy and fitness values. IBGWO-S selects the fewest features on 100% of the datasets. The results show IBGWO based on V-Shaped (IBGWO-V) outperforms the binary particle swarm and binary grey wolf optimizers on five datasets based on the classification accuracy and fitness values. The results indicate that IBGWO-V outperforms IBGWO-S in terms of all studied evaluation metrics. The results also show that IBGWO-S and IBGWO-V outperform eight meta-heuristics known in the literature in selecting the relevant features with acceptable classification accuracy.
Continual 3D Convolutional Neural Networks for Real-time Processing of Videos

We introduce Continual 3D Convolutional Neural Networks (Co3D CNNs), a new computational formulation of spatio-temporal 3D CNNs, in which videos are processed frame-by-frame rather than by clip. In online tasks demanding frame-wise predictions, Co3D CNNs dispense with the computational redundancies of regular 3D CNNs, namely the repeated convolutions over frames, which appear in overlapping clips. We show that Continual 3D CNNs can reuse preexisting 3D-CNN weights to reduce the per-prediction floating point operations (FLOPs) in proportion to the temporal receptive field while retaining similar memory requirements and accuracy. This is validated with multiple models on Kinetics-400 and Charades with remarkable results: CoX3D models attain state-of-the-art complexity/accuracy trade-offs on Kinetics-400 with 12.1−15.3 $$\times $$ reductions of FLOPs and 2.3−3.8% improvements in accuracy compared to regular X3D models while reducing peak memory consumption by up to 48%. Moreover, we investigate the transient response of Co3D CNNs at start-up and perform extensive benchmarks of on-hardware processing characteristics for publicly available 3D CNNs.
Revocable hierarchical identity-based encryption with adaptive security

Hierarchical identity-based encryption (HIBE) can be extended to revocable HIBE (RHIBE) if a private key of a user can be revoked when the private key is revealed or expired. Previously, many selectively secure RHIBE schemes were proposed, but it is still unsolved problem to construct an adaptively secure RHIBE scheme. In this work, we propose two RHIBE schemes in composite-order bilinear groups and prove their adaptive security under simple static assumptions. To prove the adaptive security, we use the dual system encryption framework, but it is not simple to use the dual system encryption framework in RHIBE since the security model of RHIBE is quite different with that of HIBE. We show that it is possible to solve the problem of the RHIBE security proof by carefully designing hybrid games.
Green Human Resource Management and Organisational Sustainability of Deposit Money Banks in Nigeria

This study investigates the relationship between green human resource management and organisational sustainability of deposit money banks in Nigeria using cross-sectional survey. Ten deposit money banks were surveyed using simple random sampling. Five managers from each bank were surveyed. Fifty copies of questionnaire were administered but forty one copies were returned and filled correctly. Kendall coefficient of concordance (tau_b) was used to analyse the hypotheses with SPSS (20.0). The study found that green recruitment, green training and green employee relations were positively associated with organisational sustainability. The study concluded that green human resource management consciousness measured in terms of green recruitment, green training and green human relations enhances organisational sustainability of deposit money banks in Nigeria. The study recommends that managers of deposit money banks should inculcate green human resource management culture into their strategic intent in order to enhance organisational sustainability.
Touch Effect of Mental Simulation in Online Fashion Shopping

This study investigates the effects of the interplay of device types and the need for touch (NFT) on product attitudes and determines how the mental simulation of touch mediates such relationships. Specifically, we test the roles of instrumental and autotelic NFT in the moderated mediation effect of mental simulation of touch. We instructed the potential participants to shop for a leather jacket on a webpage. With a total of 152 data points from the responses of participants who used a laptop and a touch device that uses a direct-touch interface (e.g., tablets), we conducted descriptive statistics, analysis of variance, and PROCESS procedures using SPSS 20.0. The results show a greater mental simulation for touch when using a touch device than a laptop. When individuals' instrumental NFT is low, using a touch device while shopping online heightens mental simulation of touch, which impacts product attitudes. In particular, such a moderated mediation effect strengthens as the value of individuals' autotelic NFT increases. However, when individuals' instrumental NFT is high, a touch device cannot drive mental simulation for touch, increasing favorable attitudes toward the product.