Reviewerly/exHarmony huggingface dataset:
ProVe - Self-supervised pipeline for automated product replacement and cold-starting based on neural language models

In retail vertical industries, businesses are dealing with human limitation of quickly understanding and adapting to new purchasing behaviors. Moreover, retail businesses need to overcome the human limitation of properly managing a massive selection of products/brands/categories. These limitations lead to deficiencies from both commercial (e.g. loss of sales, decrease in customer satisfaction) and operational perspective (e.g. out-of-stock, over-stock). In this paper we propose a pipeline approach based on Natural Language Understanding, for recommending the most suitable replacements for products that are out-of-stock. Moreover, we will propose a solution for managing products that were newly introduced in a retailer's portfolio with almost no transactional history. This solution will help businesses: automatically assign the new products to the right category; recommend complementary products for cross sell from day 1; perform sales predictions even with almost no transactional history. Finally, the vector space model resulted by applying the pipeline presented in this paper is directly used as semantic information in deep learning-based demand forecasting solutions, leading to more accurate predictions. The whole research and experimentation process have been done using real life private transactional data, however the source code is available on https://github.com/Lummetry/ProVe.
Evolutionary tree-based quasi identifier and federated gradient privacy preservations over big healthcare data

&lt;span&gt;Big data has remodeled the way organizations supervise, examine and leverage data in any industry. To safeguard sensitive data from public contraventions, several countries investigated this issue and carried out privacy protection mechanism. With the aid of quasi-identifiers privacy is not said to be preserved to a greater extent. This paper proposes a method called evolutionary tree-based quasi-identifier and federated gradient (ETQI-FD) for privacy preservations over big healthcare data. The first step involved in the ETQI-FD is learning quasi-identifiers. Learning quasi-identifiers by employing information loss function separately for categorical and numerical attributes accomplishes both the largest dissimilarities and partition without a comprehensive exploration between tuples of features or attributes. Next with the learnt quasi-identifiers, privacy preservation of data item is made by applying federated gradient arbitrary privacy preservation learning model. This model attains optimal balance between privacy and accuracy. In the federated gradient privacy preservation learning model, we evaluate the determinant of each attribute to the outputs. Then injecting Adaptive Lorentz noise to data attributes our ETQI-FD significantly minimizes the influence of noise on the final results and therefore contributing to privacy and accuracy. An experimental evaluation of ETQI-FD method achieves better accuracy and privacy than the existing methods.&lt;/span&gt;
Training workshop technologies in the practice of training university lecturers

The article discusses a topical problem of implementation of training workshop technologies for training university lecturers. Based on the analysis of the scientific literature, the category “training workshop” is defined. It has established that “training workshop” is a multifaceted concept: it is considered by scholars from different perspectives – legal, economic, social, pedagogical and philosophical. We consider training workshop as a pedagogical learning technology in the process of professional training of university lecturers, as it has a clear algorithm of use and guarantees the achievement of the planned result. Its purpose is to form the program results of students’ learning, organize various activities, shape personal attitude to solving situational problems in professional activities. The types of training workshop, the structure and teaching methods are analyzed. A method of conducting intensive training workshop “Professional competence – the basis of becoming a modern teacher of higher education” for students majoring in 011 “Educational, pedagogical sciences” has been developed, which aims to form general orientations in the field of competence approach and practical skills to successfully solve learning problems and education; to be socially active in society; systematically improve professional culture and pedagogical skills; be ready for a quality evaluation of their work. In the process of intensive training, a system of active learning methods was used, namely: cases, game methods, group discussions, brainstorming, video analysis, etc. A conclusion is drawn that the implementation of training workshop in practical classes helps the teacher to develop students’ professional abilities and skills to be creative, adapted and flexible with new information, capable of professional growth. Therefore, training workshop can be considered a new technology in the educational process that adapts the future teachers to their profession, as it aims at the step-wise implementation of various types of pedagogical innovations which changes the traditional pedagogical process.
Towards an entity relation extraction framework in the cross-lingual context

Purpose Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource languages. From the perspective of entity relation extraction, this paper aims to extend the knowledge acquisition task from a single language context to a cross-lingual context, and to improve the relation extraction performance for low resource languages. Design/methodology/approach This paper proposes a cross-lingual adversarial relation extraction (CLARE) framework, which decomposes cross-lingual relation extraction into parallel corpus acquisition and adversarial adaptation relation extraction. Based on the proposed framework, this paper conducts extensive experiments in two tasks, i.e. the English-to-Chinese and the English-to-Arabic cross-lingual entity relation extraction. Findings The Macro-F1 values of the optimal models in the two tasks are 0.880 1 and 0.789 9, respectively, indicating that the proposed CLARE framework for CLARE can significantly improve the effect of low resource language entity relation extraction. The experimental results suggest that the proposed framework can effectively transfer the corpus as well as the annotated tags from English to Chinese and Arabic. This study reveals that the proposed approach is less human labour intensive and more effective in the cross-lingual entity relation extraction than the manual method. It shows that this approach has high generalizability among different languages. Originality/value The research results are of great significance for improving the performance of the cross-lingual knowledge acquisition. The cross-lingual transfer may greatly reduce the time and cost of the manual construction of the multi-lingual corpus. It sheds light on the knowledge acquisition and organization from the unstructured text in the era of big data.
A Unified Representation Learning Strategy for Open Relation Extraction with Ranked List Loss

Open Relation Extraction (OpenRE), aiming to extract relational facts from open-domain corpora, is a sub-task of Relation Extraction and a crucial upstream process for many other NLP tasks. However, various previous clustering-based OpenRE strategies either confine themselves to unsupervised paradigms or can not directly build a unified relational semantic space, hence impacting down-stream clustering. In this paper, we propose a novel supervised learning framework named MORE-RLL (Metric learning-based Open Relation Extraction with Ranked List Loss) to construct a semantic metric space by utilizing Ranked List Loss to discover new relational facts. Experiments on real-world datasets show that MORE-RLL can achieve excellent performance compared with previous state-of-the-art methods, demonstrating the capability of MORE-RLL in unified semantic representation learning and novel relational fact detection.
SSProve: A Foundational Framework for Modular Cryptographic Proofs in Coq

State-separating proofs (SSP) is a recent methodology for structuring game-based cryptographic proofs in a modular way, by using algebraic laws to exploit the modular structure of composed protocols. While promising, this methodology was previously not fully formalized and came with little tool support. We address this by introducing SSProve, the first general verification framework for machine-checked state-separating proofs. SSProve combines high-level modular proofs about composed protocols, as proposed in SSP, with a probabilistic relational program logic for formalizing the lower-level details, which together enable constructing machine-checked cryptographic proofs in the Coq proof assistant. Moreover, SSProve is itself fully formalized in Coq, including the algebraic laws of SSP, the soundness of the program logic, and the connection between these two verification styles. To illustrate SSProve, we use it to mechanize the simple security proofs of ElGamal and pseudo-random-function–based encryption. We also validate the SSProve approach by conducting two more substantial case studies: First, we mechanize an SSP security proof of the key encapsulation mechanism–data encryption mechanism (KEM-DEM) public key encryption scheme, which led to the discovery of an error in the original paper proof that has since been fixed. Second, we use SSProve to formally prove security of the sigma-protocol zero-knowledge construction, and we moreover construct a commitment scheme from a sigma-protocol to compare with a similar development in CryptHOL. We instantiate the security proof for sigma-protocols to give concrete security bounds for Schnorr’s sigma-protocol.
Information Bottleneck Approach to Spatial Attention Learning

The selective visual attention mechanism in the human visual system (HVS) restricts the amount of information to reach visual awareness for perceiving natural scenes, allowing near real-time information processing with limited computational capacity. This kind of selectivity acts as an ‘Information Bottleneck (IB)’, which seeks a trade-off between information compression and predictive accuracy. However, such information constraints are rarely explored in the attention mechanism for deep neural networks (DNNs). In this paper, we propose an IB-inspired spatial attention module for DNN structures built for visual recognition. The module takes as input an intermediate representation of the input image, and outputs a variational 2D attention map that minimizes the mutual information (MI) between the attention-modulated representation and the input, while maximizing the MI between the attention-modulated representation and the task label. To further restrict the information bypassed by the attention map, we quantize the continuous attention scores to a set of learnable anchor values during training. Extensive experiments show that the proposed IB-inspired spatial attention mechanism can yield attention maps that neatly highlight the regions of interest while suppressing backgrounds, and bootstrap standard DNN structures for visual recognition tasks (e.g., image classification, fine-grained recognition, cross-domain classification). The attention maps are interpretable for the decision making of the DNNs as verified in the experiments. Our code is available at this https URL.
Analisis Algoritma Apriori Dalam Menentukan Pola Pemesanan Konsumen Pada Ucokopi

Ucokopi is a business engaged in the sale of food and beverages for the general public. The menus are served in the form of snacks such as french fries, fried rice, sausages and so on, as well as various drinks processed at economical prices, especially for students and students. The large number of consumer orders everyday will increase the amount of transaction data. Existing data can be processed to determine the combination of food and beverage menu itemset that consumers often buy so as to produce important information. The problem that often occurs is the unavailability of consumer order menus which are classified as in demand. This research was conducted in Pematangsiantar city. The purpose of this study was to analyze a sales strategy through the association search process. Based on these results, it can be obtained that if you buy Ginger Milk, it is 100% most likely to buy Sausages, if you buy Milk Coffee, 88% are most likely to buy Penyet Chicken Rice, if you buy Sausages then 75% are most likely to buy Potatoes. It is hoped that the research results can provide information to Ucok Cafe business owners in providing food and beverage stocks that are most in demand and can adjust the menu placement based on a combination of menu itemset that is formed to increase sales.
Budget-Constrained Coalition Strategies with Discounting

Discounting future costs and rewards is a common practice in accounting, game theory, and machine learning. In spite of this, existing logics for reasoning about strategies with cost and resource constraints do not account for discounting. The paper proposes a sound and complete logical system for reasoning about budget-constrained strategic abilities that incorporates discounting into its semantics.
Differentially Private Correlation Alignment for Domain Adaptation

Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. As a simple and efficient method for domain adaptation, correlation alignment transforms the distribution of the source domain by utilizing the covariance matrix of the target domain, such that a model trained on the transformed source data can be applied to the target data. However, when source and target domains come from different institutes, exchanging information between the two domains might pose a potential privacy risk. In this paper, for the first time, we propose a differentially private correlation alignment approach for domain adaptation called PRIMA, which can provide privacy guarantees for both the source and target data. In PRIMA, to relieve the performance degradation caused by perturbing the covariance matrix in high dimensional setting, we present a random subspace ensemble based covariance estimation method which splits the feature spaces of source and target data into several low dimensional subspaces. Moreover, since perturbing the covariance matrix may destroy its positive semi-definiteness, we develop a shrinking based method for the recovery of positive semi-definiteness of the covariance matrix. Experimental results on standard benchmark datasets confirm the effectiveness of our approach.
Constructing Complexity-efficient Features in XCS with Tree-based Rule Conditions

A major goal of machine learning is to create techniques that abstract away irrelevant information. The generalisation property of standard Learning Classifier Systems (LCSs) removes such information at the feature level but not at the feature interaction level. Code Fragments (CFs), a form of tree-based programs, introduced feature manipulation to discover important interactions, but they often contain irrelevant information, which causes structural inefficiency. XOF is a recently introduced LCS that uses CFs to encode building blocks of knowledge about feature interaction. This paper aims to optimise the structural efficiency of CFs in XOF. We propose two measures to improve constructing CFs to achieve this goal. Firstly, a new CF-fitness update estimates the applicability of CFs to the problem while also considering the structural complexity. The second measure is a niche-based method for generating CFs. These approaches were tested on Even-parity and Hierarchical problems, which require highly complex combinations of input features to capture the data patterns. The results show that the proposed methods significantly increase the structural efficiency of CFs, which is estimated by the rule "generality rate". This results in faster learning performance in the Hierarchical Majority-on problem. Furthermore, a user-set depth limit for CF generation is not needed as the learning agent will not adopt higher-level CFs once optimal CFs are constructed.
Reduce Redundancy in Server Storage using Deduplication and Compression Technique

With the documentation process atomization document/file sharing increased this leads to increase in quantity of duplicate documents/files in the Internet and occupying HDD space in servers which is directly related with other resource i.e. hardware and increases maintenance cost making burden to sever owner and service providers. The proposed technique has been solved the problem of saving duplicate files in less space in real-time more efficiently and with high reliability and accuracy. Virtual addressing plays a vital role in to make a virtual file system for authentic users in highly secure manner where chances of real file direct access is negligible by unauthentic users. The proposed system will be useful in cloud services (like email) where hundreds to lakhs of duplicated files are shared.
EMPLOYEE ENGAGEMENT AND COMMITMENT IN SERVICE SECTOR

This paper aims to explore the employee’s engagement and their commitment towards the organization. An employee who is the engaged and positive attitude they are inclined toward their organization. Employee Engagement is a more challenging role in the organization. Organizational success depends upon how employee perception and commitment to their work. This research paper can be used to understand employee engagement in the service sector in North India. The overall effect can be analyzed through a survey and questionnaire with help of primary data and secondary data. This study was investigated employee engagement and commitment in different organizations. An expansive review of the literature was done which created the framework of the research. A total of 206 respondents within North India were on an online survey. The results were analyzed and used to understand employee engagement and commitment in different organizations.
MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data

Cluster analysis is the prominent data mining technique in knowledge discovery and it discovers the hidden patterns from the data. The K-Means, K-Modes and K-Prototypes are partition based clustering algorithms and these algorithms select the initial centroids randomly. Because of its random selection of initial centroids, these algorithms provide the local optima in solutions. To solve these issues, the strategy of Crow Search algorithm is employed with these algorithms to obtain the global optimum solution. With the advances in information technology, the size of data increased in a drastic manner from terabytes to petabytes. To make proposed algorithms suitable to handle these voluminous data, the phenomena of parallel implementation of these clustering algorithms with Hadoop Mapreduce framework. The proposed algorithms are experimented with large scale data and the results are compared in terms of cluster evaluation measures and computation time with the number of nodes.
Coping strategies of a rape victim as reflected in Chanel Miller’s Know My Name memoir

Memoir is a literary work genre that belongs to the subgenre of autobiography. Most of the memoir showcases the writer’s particular moment of life, rather than the reels of their whole life. Chanel Miller, for example, showcases her experience during the aftermath of rape in her memoir, Know My Name. Various definition of rape has one thing in common, which rape is something that happens despite the lack of consensus and that creates stress after it happens. Stressful encounters require a coping strategy to conquer them. This study tries to analyze coping strategies on Know My Name memoir because it is due time for us to recognize how rape has influenced the victim’s life to provide better care both physical and psychological wise for rape victims and, even better, to formulate better law to protect the victim. Using descriptive study, the important features were found as Channel Miller’s emotional reactions to choose a way out of the problem.
Gesture Recognition-Malaysian Sign Language Recognition with Convolutional Neural Network

Sign language is a communication medium for the deaf and vocally impaired. However, this language is not practised in public due to the deaf community being a minority and it takes time to learn and skilled manpower to assist the deaf in public interaction. Thus, this study aims to produce a Malaysian Sign Language Recognition (MSLR) application to recognise MSL alphabets to help normal people communicate with the deaf. The proposed work involves a few stages that consist of background subtraction to detect the moving hand, skin segmentation based on skin tones using YCbCr (Luminance, Chrominance) colour space for robustness in illumination and a 2D Convolutional Neural Network (CNN) model for feature extraction and classification of 24 alphabets. As MSL alphabets are similar to American Sign Language (ASL) alphabets, the ASL FingerSpelling Dataset from the University of Surrey’s Center for Vision, Speech and Signal Processing is used for model training and testing. Evaluation criteria include micro averages in Precision, Recall and F1-Score. The test accuracy is 79.54% with misclassifications on letters such as ‘E’ and ‘Q’ due to the signing orientation and similarity in finger articulation. Keywords: 2D CNN, Computer Vision, Fingerspelling, Machine Learning, Malaysian Sign Language Recognition
Bachelor of education model: integration of approaches

Introduction. The relevance of the research is due to the contradiction between the high demand of the society to the teacher professional competence level and unclear conceptualization of teacher training institutes concerning the summarized results of the professional training of Bachelor’s Degree students. Modern labor market gives a chance to a teacher not only to teach a subject or to work as a form master but to become a specialist in business administration, a researcher, or an edupreneur. A future teacher, when being a university student, should realize his/her chances, abilities, personal and professional prospects and choose the main direction of the professional training program. The research is aimed to create a generalized Model of a university grad (Bachelor of Education) in accordance with integrated methodological approaches to learning outcomes problem analyses. Materials and methods. The research is based on the method of theoretical analysis, synthesis, generalization, analytical modelling, review and literal analysis, method of expert assessment, brainstorm, Belgorod State University professors’ and students’ needs assessment survey. The authors made an attempt to establish theoretical Bachelor of Education Model through an integrated methodology, in particular, through the ideas and conceptions of the following approaches: competency-based, polysubjective, and prosocial. Research results. The authors present the Bachelor of Education Model as a scientific-based image of a future teacher, expressed in terms of integrated systemic competency combining cross-functional, general professional and special competencies as well as appropriate educational programs promoting a set of dominant competences. Conclusion. The Bachelor of Education Model, mentioned in the article, can stimulate higher education establishments to rethink the main strategies of future teacher professional training in accordance with individual educational program track and asynchronous educational process.