Reviewerly/exHarmony huggingface dataset:
Secure Association Rule Mining on Vertically Partitioned Data Using Private-Set Intersection

Data mining entails the discovery of unexpected but reusable knowledge from large unorganized datasets.Among the many available data-mining algorithms, association rule mining (ARM) is very common.It was developed to aggregate all data into one site and subsequently mine them.In recent years, organizations in different fields have been required to collaborate to create new value.However, data mining among and within organizations has raised privacy and confidentiality concerns.In our scheme, parties cannot share anything other than the number of records, including the candidate itemset.This study focuses on the private-set intersection instead of the scalar product and shows that this intersection enables organizations to execute ARM on vertically partitioned data, allowing flexible information sharing while preserving privacy without increasing communication and computation costs.Furthermore, we focus on the fact that the number of protocol rounds among parties can be reduced and present three use cases in which the proposed scheme works more effectively than the existing schemes.
Metacognitive Sedenion-Valued Neural Network and its Learning Algorithm

In this article, a metacognitive sedenion-valued neural network (Mc-SVNN) and its learning algorithm are proposed.Its application to diverse time-series prediction problems is presented.The Mc-SVNN contains two components: a sedenion-valued neural network that represents the cognitive component, and a metacognitive component, which serves to self-regulate the learning algorithm.At each epoch, the metacognitive component decides what, how, and when learning occurs.The algorithm deletes unnecessary samples and stores only those that are used.This decision is determined by the sedenion magnitude and the 15 sedenion phases.The Mc-SVNN is applied to four real-world forecasting problems: USD-to-euro currency exchange rate forecasting, the sunspot number time series, power demand forecasting, and daily temperature prediction in Abu Dhabi.Compared to existing methods, the Mc-SVNN demonstrates superior performance in time-series forecasting while using a smaller number of parameters.
AFAM-PEC: Adaptive Failure Avoidance Tracking Mechanism Using Prediction-Estimation Collaboration

During recent years correlation tracking is considered fast and effective by the virtue of circulant structure of the sampling data for learning phase of filter and Fourier domain calculation of correlation.During the occurrence of occlusion, motion blur and out of view movement of target, most of the correlation filter based trackers start to learn using erroneous samples and tracker starts drifting.Currently, adaptive correlation filter based tracking algorithms are being combined with redetection modules.This hybridization helps in redetection of the target in long term tracking.The redetection modules are mostly classifier, which classify the true object after tracking failure occurrence.The methods perform favorable during short term occlusion or partial occlusion.To further increase the tracking efficiency specifically during long term occlusion, while maintaining real time processing speed, this study proposes tracking failure avoidance method.We first propose, a strategy to detect the occlusion using two cues from the response map i.e., peak correlation score and peak to side lobe ratio.After successful detection of tracking failure, second strategy is proposed to save the target being getting more erroneous.Kalman filter based predictor continuously predicts the location during occlusion.Kalman filter passes this result to Support Vector Machine (SVM).When the target reappears in frame, support vector machine based classifier classifies the correct object using the predicted location of Kalman filter.This decreases the chance of tracking failure as Kalman filter continuously updates itself during occlusion and predicts the next location using its own previous prediction.Once the true object is detected by classifier after the clearance of occlusion, this result is forwarded to correlation filter tracker to resume its operation of tracking and updating its parameters.Together these two proposed schemes show significant improvement in tracking efficiency.Furthermore, this collaboration in redetection phase shows significant improvement in the tracking accuracy over videos containing six challenging aspects of visual object tracking as mentioned in the literature.
Contourlet domain locally optimum image watermark decoder using Cauchy mixtures based vector HMT model

Digital image watermarking has become a necessity in many applications such as data authentication, broadcast monitoring on the Internet and ownership identification. Various watermarking schemes have been proposed to protect the copyright information. There are three indispensable, yet contrasting requirements for a watermarking scheme: imperceptibility, robustness and payload. Therefore, a watermarking scheme should provide a trade-off among these requirements from the information-theoretic perspective. Generally, in order to enhance the imperceptibility, robustness and payload simultaneously, the human visual system (HVS) and the statistical properties of the image signal should be fully taken into account. The statistical model-based transform domain multiplicative watermarking scheme embodies the above ideas, and therefore the detection and extraction of the multiplicative watermarks have received a great deal of attention. The performance of a statistical model-based watermark detector or decoder is highly influenced by the accuracy of the statistical model itself and the applicability of decision rule. In this paper, we firstly propose a new hidden Markov trees (HMT) statistical model in Contourlet domain, namely Cauchy mixtures-based vector HMT (vector CMM–HMT), by describing the marginal distribution with Cauchy mixture model (CMM) and grouping Contourlet coefficients into a vector, which can capture both the subband marginal distributions and the strong dependencies across scales and orientations of the Contourlet coefficients. Then, by modeling the Contourlet coefficients with vector CMM–HMT and employing locally most powerful (LMP) test, we develop a locally optimum image watermark decoder in Contourlet domain. We conduct extensive experiments to evaluate the performance of the proposed blind watermark decoder, in which encouraging results validate the effectiveness of the proposed technique, in comparison with the state-of-the-art approaches recently proposed in the literature.
Design and Realization of Computer Aided Music Teaching System Based on Interactive Mode

With the development and progress of society, consumers are more and more inclined to choose personalized and rich products.In order to meet the needs of people's increasingly rich material and cultural products, the designed products must not only have functional practicality and beautiful appearance, but also be able to meet the internal personalized needs of consumers.The introduction of experiential teaching in computer-aided graphic design courses is mainly for students to design products with rich connotation and personalization based on mastering the technology of computer-aided graphic design and combining with practice.In experiential teaching, the starting point and end point of our teaching reform is to closely combine classroom teaching of design performance techniques with professors of design theory knowledge, while focusing on cultivating and enhancing students' practical innovation ability and personalized design level.And then achieve the purpose of training high-quality composite design talents.
An algorithm for non-parametric estimation in state–space models

State–space models are ubiquitous in the statistical literature since they provide a flexible and interpretable framework for analyzing many time series. In most practical applications, the state–space model is specified through a parametric model. However, the specification of such a parametric model may require an important modeling effort or may lead to models which are not flexible enough to reproduce all the complexity of the phenomenon of interest. In such situations, an appealing alternative consists in inferring the state–space model directly from the data using a non-parametric framework. The recent developments of powerful simulation techniques have permitted to improve the statistical inference for parametric state–space models. It is proposed to combine two of these techniques, namely the Stochastic Expectation–Maximization (SEM) algorithm and Sequential Monte Carlo (SMC) approaches, for non-parametric estimation in state–space models. The performance of the proposed algorithm is assessed though simulations on toy models and an application to environmental data is discussed.
The Impact of Talent Management on Organizational Commitment of the Employees of Telecommunication Companies in Jordan: The Mediating Role of Employee Work Engagement

The aim of this study is twofold.First, to identify the impact of talent management on organizational commitment.Second, to examine the mediating role played by employee work engagement dimensions in the impact of talent management on organizational commitment.Data were gathered by a questionnaire developed based on the literature, validated using exploratory and confirmatory factor analyses, and distributed to representative sample consisted of 375 managers working in telecommunication companies in Jordan (Zain, Orange, Umniah).IMB SPSS and AMOS were used to analyze 302 questionnaires that returned valid with a response rate of 80.5%.The results revealed that talent management has a significant impact on organizational commitment.Notably, talent management has a significant impact on all dimensions of organizational commitment: affective, continuance and normative commitment.Furthermore, the results indicated that employee work engagement dimensions except dedication engagement significantly mediated the impact of talent management on organizational commitment.A crucial contribution of this study is that it provides evidence in relation to the mediating role of employee work engagement dimensions in this context.Hence, organizations should pay a great attention to vigor engagement, absorption engagement, and employee voicewhen decide to identify, develop and retain their talents and to ensure higher levels of organizational commitment.
Boomerang uniformity of popular S-box constructions

In order to study the resistance of a block cipher against boomerang attacks, a tool called the Boomerang Connectivity Table (BCT) for S-boxes was recently introduced. Very little is known today about the properties of this table especially for bijective S-boxes defined for n variables with $$n\equiv 0 \mod 4$$ . In this work we study the boomerang uniformity of some popular constructions used for building large S-boxes, e.g. for eight variables, from smaller ones. We show that the BCTs of all the studied constructions have abnormally high values in some positions. This remark permits us in some cases to link the boomerang properties of an S-box with other well-known cryptanalytic techniques on such constructions while in other cases it leads to the discovery of new ones. A surprising outcome concerns notably the Feistel and MISTY networks. While these two structures are very similar, their boomerang uniformity can be very different. Indeed, we show that the boomerang uniformity of a Feistel network is always the worst one possible, while for a MISTY construction this quantity depends on its inner building blocks. In the second part, we investigate the boomerang uniformity under EA-equivalence for Gold and the inverse function (as used respectively in MPC-friendly ciphers and the AES) and we prove that the boomerang uniformity is EA-invariant in these cases. Finally, we present an algorithm for inverting a given BCT and provide experimental results on the size of the BCT-equivalence classes for some 4 and 8-bit S-boxes.
WhatsApp and Google Drive Influence on Pre-service Students’ Learning

The use of technology and all that it entails, such as applications and social networks, has led to a transformation of education in all aspects. Communication and the monitoring of training processes are being transformed through these new elements gradually incorporated into the classrooms. The aim of the study is to analyse the impact of WhatsApp and Google Drive on the monitoring of training practices among pre-service students using a traditional, face-to-face methodology. A quasi-experimental design was carried out on a sample of 123 Spanish pre-service students. The data were collected by means of a questionnaire. The results show that the follow-up of external practices of future teachers using WhatsApp and Google Drive is effective in comparison with the face-to-face follow-up, although no significant differences are shown in the students' grades. Conclusions show that applying a monitoring method with these technological tools leads to an increase in students' motivation mainly as it allows a more direct interaction between the teacher and the student. It is important to select the monitoring method and the target student in the didactic development. Hence, these educational applications enable active learning where the learner is the main protagonist and can be supported and monitored.
Sentence Embedding Based Semantic Clustering Approach for Discussion Thread Summarization

Huge data on the web come from discussion forums, which contain millions of threads. Discussion threads are a valuable source of knowledge for Internet users, as they have information about numerous topics. The discussion thread related to single topic comprises a huge number of reply posts, which makes it hard for the forum users to scan all the replies and determine the most relevant replies in the thread. At the same time, it is also hard for the forum users to manually summarize the bulk of reply posts in order to get the gist of discussion thread. Thus, automatically extracting the most relevant replies from discussion thread and combining them to form a summary are a challenging task. With this motivation behind, this study has proposed a sentence embedding based clustering approach for discussion thread summarization. The proposed approach works in the following fashion: At first, word2vec model is employed to represent reply sentences in the discussion thread through sentence embeddings/sentence vectors. Next, K-medoid clustering algorithm is applied to group semantically similar reply sentences in order to reduce the overlapping reply sentences. Finally, different quality text features are utilized to rank the reply sentences in different clusters, and then the high-ranked reply sentences are picked out from all clusters to form the thread summary. Two standard forum datasets are used to assess the effectiveness of the suggested approach. Empirical results confirm that the proposed sentence based clustering approach performed superior in comparison to other summarization methods in the context of mean precision, recall, and F -measure.
Arbitration quantum signature protocol based on XOR encryption

Quantum signature is a branch of quantum cryptography that draws on the design ideas of classic digital signatures, and uses the basic principles of quantum mechanics to achieve the integrity, authenticity and nonrepudiation of quantum information. Among them, arbitration quantum signature (AQS) plays a very important role. In this paper, we proposed an AQS protocol based on XOR encryption. Unlike other protocols, a quantum one-time pad encryption method or chain-type CNOT encryption method is abandoned. The proposed protocol is designed based on the three-particle GHZ state combined with the idea of classical XOR encryption, with CNOT operations used to encrypt quantum messages and quantum signatures. A security analysis of the proposed protocol reveals that the proposed protocol can satisfy the AQS requirements of unforgeability and nonrepudiation.
A Complete Methodology for Kuzushiji Historical Character Recognition using Multiple Features Approach and Deep Learning Model

As per the studies during many decades, substantial research efforts have been devoting towards character recognition.This task is not so easy as it it appears; in fact humans' have error rate about more than 6%, while reading the handwritten characters and recognizing.To solve this problem an effort has been made by applying the multiple features for recognizing kuzushiji character, without any knowledge of the font family presented.At the outset a pre-processing step that includes image binarization, noise removal and enhancement was applied.Second step was segmenting the page-sample by applying contour technique along with convex hull method to detect individual character.Third step was feature extraction which included zonal features (ZF), structural features (SF) and invariant moments (IM).These feature vectors were passed for training and testing to the various machine learning and deep learning models to classify and recognize the given character image sample.The accuracy achieved was about 85-90% on the data-set which consisted of huge data samples round about 3929 classes followed by 392990 samples.
Evolving Deep Forest with Automatic Feature Extraction for Image Classification Using Genetic Programming

Deep forest is an alternative to deep neural networks to use multiple layers of random forests without back-propagation for solving various problems. In this study, we propose a genetic programming-based approach to automatically and simultaneously evolving effective structures of deep forest connections and extracting informative features for image classification. First, in the new approach we define two types of modules: forest modules and feature extraction modules. Second, an encoding strategy is developed to integrate forest modules and feature extraction modules into a tree and the search strategy is introduced to search for the best solution. With these designs, the proposed approach can automatically extract image features and find forests with effective structures simultaneously for image classification. The parameters in the forest can be dynamically determined during the learning process of the new approach. The results show that the new approach can achieve better performance on the datasets having a small number of training instances and competitive performance on the datasets having a large number of training instances. The analysis of evolved solutions shows that the proposed approach uses a smaller number of random forests over the deep forest method.
Simulation of Daily Rainfall Data using Articulated Weather Generator Model for Seasonal Prediction of ENSO-Affected Zones in Indonesia

Synthetic rainfall simulation using weather generator models is commonly used as a substitute at locations with incomplete or short rainfall data. It incorporates a method that can be developed into forecasts of future rainfall. This study was designed to modify a rainfall prediction system based on the principles of weather generator models and to test the validity of the modelling results. It processed the data collected from eight rain stations in zones affected by El-Nino Southern Oscillation (ENSO). A large-scale predictor, that is, SST prediction data in the Nino 3.4 region over the Pacific Ocean was used as the influencing variable in projecting rainfall for the following six months after the predefined dates. Rainfall data from weather stations and SST in 1960-2000 were analyzed to identify the effects of ENSO and build a statistical model based on the regression function. Meanwhile, the model was validated using the data from 2001 to 2007 by backtesting six months in a row. The analysis results showed that the model could simulate both low rainfall in the dry season and high one in the rainy season. Validation by the student's t-test confirmed that the six-month synthetic rain data at nearly all observed stations was homogenous. For this reason, the developed model can be potentially used as one of the season prediction systems.
Decomposition-Fusion for Label Distribution Learning

Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life machine learning applications. However, LDL is a generalization of the classification task and as such it is exposed to the same problems as standard classification algorithms, including class-imbalanced, noise, overlapping or irregularities. The purpose of this paper is to mitigate these effects by using decomposition strategies. The technique devised, called Decomposition-Fusion for LDL (DF-LDL), is based on one of the most renowned strategy in decomposition: the One-vs-One scheme, which we adapt to be able to deal with LDL datasets. In addition, we propose a competent fusion method that allows us to discard non-competent classifiers when their output is probably not of interest. The effectiveness of the proposed DF-LDL method is verified on several real-world LDL datasets on which we have carried out two types of experiments. First, comparing our proposal with the base learners and, second, comparing our proposal with the state-of-the-art LDL algorithms. DF-LDL shows significant improvements in both experiments.
Information-Educational Environment as a Condition of Formation of Gifted Children's Informational-Digital Competence

The article deals with the influence of information-educational environment on gifted child’s development. The study was carried out on the basis of the Laboratory of informational technologies of education in Kharkiv Regional Specialized Boarding School of the 2nd – 3rd degrees “Giftedness”. The directions of the activities of the Laboratory of informational technologies of education have been revealed. They included: introduction and integration of distance learning into educational process, organization of distance learning through cooperation with institutions of higher education (dual education); creation and replenishment of methodical-informational resources of school; ensuring sustainable functioning and development of computer network, use of new means of informational technologies; software and technical, methodic and organizational support of educational computer classrooms and computer equipment; providing school representation in world informational space; support of projects that include use of new informational technologies and Internet; funding educational software; researching the effectiveness of using software tools etc. The requirements to information-educational environment and organization of gifted children’s work have been determined. They are development of informational culture and competence of the participants of the environment, its orientation on monitoring of development of child’s giftedness, harmonization of relationships between participants of educational process, strengthening school ties with the external environment.