Lexical Borrowing as Code Alternation Strategy in Gender Discourse

Code alternation is one of the unavoidable consequences of communication between different language varieties that has lingered on, in discussion of various studies, both in theory and practice for several decades. These studies have neither examined code alternation strategies utilised in a language variety nor concentrated on communication of such (language variety) in gender discourse. Also, most of the studies are conducted by using face-to-face conversations or written materials occurring in the real world. This paper fills this gap by classifying the structure of lexical borrowing as code alternation strategy in gender discourse, together with their functions. Twelve extracts involving borrowed items were selected from four novels written by Nigerian writers to illustrate how lexical borrowing has being used in defining the actions of various genders in a social context. The data were analysed using insights from Markedness Model (Myers-Scotton & Bolonyai 2001) of code-switching, specifically the rationality notion, and Butler (1990)‟s social constructionist theory of gender. It observes that the codeswitched items in form of borrowing in gender discourse function as clarification, euphemism and humour. The structural form of the items ranges from intra-sentential to inter-sentential. The paper concludes that such alterations express deference or its opposite to the ideal/repulsive qualities expected from or exhibited by each gender in various occasions

Semantic enhanced Markov model for sequential E-commerce product recommendation

To model sequential relationships between items, Markov Models build a transition probability matrix P of size n× n, where n represents number of states (items) and each matrix entry p(i,j) represents transition probabilities from state i to state j. Existing systems such as factorized personalized Markov chains (FPMC) and fossil either combine sequential information with user preference information or add the high-order Markov chains concept. However, they suffer from (i) model complexity: an increase in Markov Model’s order (number of states) and separation of sequential pattern and user preference matrices, (ii) sparse transition probability matrix: few product purchases from thousands of available products, (iii) ambiguous prediction: multiple states (items) having same transition probability from current state and (iv) lack of semantic knowledge: transition to next state (item) depends on probabilities of items’ purchase frequency. To alleviate sparsity and ambiguous prediction problems, this paper proposes semantic-enabled Markov model recommendation (SEMMRec) system which inputs customers’ purchase history and products’ metadata (e.g., title, description and brand) and extract products’ sequential and semantic knowledge according to their (i) usage (e.g., products co-purchased or co-reviewed) and (ii) textual features by finding similarity between products based on their characteristics using distributional hypothesis methods (Doc2vec and TF-IDF) which consider the context of items’ usage. Next, this extracted knowledge is integrated into the transition probability matrix P to generate personalized sequential and semantically rich next item recommendations. Experimental results on various E-commerce data sets exhibit an improved performance by the proposed model

A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation

E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customer’s need is crucial for retailers to increase revenue and retain customers’ loyalty. As users’ interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand users’ long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product recommendation as an application and classifies SRecSys under three main categories as: (i) traditional approaches (sequence similarity, frequent pattern mining and sequential pattern mining), (ii) factorization and latent representation (matrix factorization and Markov models) and (iii) neural network-based approaches (deep neural networks, advanced models). This classification contributes towards enhancing the understanding of existing SRecSys in the literature with the application domain of e-commerce product recommendation and provides current status of the solutions available alongwith future research directions. Furthermore, a classification of surveyed systems according to eight important key features supported by the techniques along with their limitations is also presented. A comparative performance analysis of the presented SRecSys based on experiments performed on e-commerce data sets (Amazon and Online Retail) showed that integrating sequential purchase patterns into the recommendation process and modeling users’ sequential behavior improves the quality of recommendations

Improving e-commerce product recommendation using semantic context and sequential historical purchases

Collaborative Filtering (CF)-based recommendation methods suffer from (i) sparsity (have low user–item interactions) and (ii) cold start (an item cannot be recommended if no ratings exist). Systems using clustering and pattern mining (frequent and sequential) with similarity measures between clicks and purchases for next-item recommendation cannot perform well when the matrix is sparse, due to rapid increase in number of items. Additionally, they suffer from: (i) lack of personalization: patterns are not targeted for a specific customer and (ii) lack of semantics among recommended items: they can only recommend items that exist as a result of a matching rule generated from frequent sequential purchase pattern(s). To better understand users’ preferences and to infer the inherent meaning of items, this paper proposes a method to explore semantic associations between items obtained by utilizing item (products’) metadata such as title, description and brand based on their semantic context (co-purchased and co-reviewed products). The semantics of these interactions will be obtained through distributional hypothesis, which learns an item’s representation by analyzing the context (neighborhood) in which it is used. The idea is that items co-occurring in a context are likely to be semantically similar to each other (e.g., items in a user purchase sequence). The semantics are then integrated into different phases of recommendation process such as (i) preprocessing, to learn associations between items, (ii) candidate generation, while mining sequential patterns and in collaborative filtering to select top-N neighbors and (iii) output (recommendation). Experiments performed on publically available E-commerce data set show that the proposed model performed well and reflected user preferences by recommending semantically similar and sequential products

Mining Integrated Sequential Patterns From Multiple Databases

Existing work on multiple databases (MDBs) sequential pattern mining cannot mine frequent sequences to answer exact and historical queries from MDBs having different table structures. This article proposes the transaction id frequent sequence pattern (TidFSeq) algorithm to handle the difficult problem of mining frequent sequences from diverse MDBs. The TidFSeq algorithm transforms candidate 1-sequences to get transaction subsequences where candidate 1-sequences occurred as (1-sequence, itssubsequenceidlist) tuple or (1-sequence, position id list). Subsequent frequent i-sequences are computed using the counts of the sequence ids in each candidate i-sequence position id list tuples. An extended version of the general sequential pattern (GSP)-like candidate generates and a frequency count approach is used for computing supports of itemset (I-step) and separate (S-step) sequences without repeated database scans but with transaction ids. Generated patterns answer complex queries from MDBs. The TidFSeq algorithm has a faster processing time than existing algorithms

Sustainable Information and Communication Technology (ICT) for Sustainable Data Governance in Nigeria: A Narrative Review.

Recent developments in big data have heightened the need for Sustainable Data Governance (SDG). SDG is significant in realizing sustainable economic development in Nigeria. Information and Communication Technologies (ICTs) have made landmark innovational trends in empowering data governance globally. Despite these global impacts of ICT on data governance, numerous investigations have shown that poor sustainability of ICT in Nigeria poses barriers that impede progress related to data governance. SDG which is the pivot for economic growth has remained relatively nonexistence or unattended to due to corrupt policies and practices, ignorance, and illiteracy that plagued sustainable ICT innovations in Nigeria. For this study, the Unified Theory of Acceptance and Use of Technology (UTAUT) was adopted as the conceptual framework. UTAUT model claims that the benefits of using technology and the factors that drive users’ decision to use it, is what determines users’ acceptance behavior. In this study, the authors explored a narrative review, analysis, and synthesis of prior research that focused on the theoretical underpinnings of vast works of literature that revealed significant information on the impact of sustainable ICT on Sustainable Data Governance in Nigeria. The authors also extracted peer-reviewed articles within the last five years from electronic databases, using some keywords such as “ICT and SDG”, “ICT and national economic development”, “Trends for ICT”, etc. The result of this study revealed that strict adherence to policies, laws, and guidelines on the adoption of ICT coupled with good formulation and communication of same, are the major impact of sustainable ICT that can leverage SDG in Nigeria. The result from this study may increase understanding, minimize corrupt practices and encourage trust in ICT innovations, ICT adoption, its acceptance and sustainability that can positively impact SDG and national economic development in Nigerian. Keywords: ICT, Sustainable Data Governance, adoption, sustainability, Trends, Trust, corruption. DOI: 10.7176/JIEA/9-5-02 Publication date: August 31st 201

Monitoring IT and Internet Usage of Employees for Sustainable Economic Development in Nigeria: Legal and Ethical Issues.

Globally, organization system resources: hardware, software, data, and communication lines and networks are now handled with better interconnected and interdependent facilities because internet connectivity is widely integrated into ambient or ubiquitous environments through intuitive interfaces or “smart” interactions. Organization enterprises are increasingly becoming competitive, with widespread cyberloafing and lawsuits. Through IT and Internet usage, employees may compromise an organization’s confidential information, deliberately or inadvertently. Such concerns prompt companies to introduce employee monitoring to preserve the integrity, availability, and confidentiality of system resources, track employee performance, avoid legal liability, protect trade secrets, and address security concerns. Despite these laudable benefits, employees feel that monitoring is an invasion of their privacy rights. For this study, organizational ethics and major ethical principles of respect for persons, beneficence, and justice representing the key ethical concerns for human subject protection in research were fully adopted as identified in The Belmont Report of 1979. In this study, the authors explored a narrative review, analysis, and synthesis of prior researches that focused on monitoring of employee IT and Internet usage. The authors also extracted peer-reviewed articles within the last five years from electronic databases, using some search keys such as “employee monitoring”, “legal and ethical issues”, “impact of employee monitoring on economic sustainability”, etc. The result of this study revealed that developing an acceptable monitoring policy will keep both employer and employee on the same page as to what is acceptable in the workplace along with what isn’t. This result may further explain the need for employee monitoring, address the legal and ethical issues involved when monitoring employees in a work environment, and provide strategies and practices for acceptable monitoring policy for improved organizational performance and sustainable economic development. Keywords: Employee Monitoring, Legal and Ethical Issues, IT and Internet Usage. Economic Sustainability. DOI: 10.7176/JIEA/9-5-03 Publication date: August 31st 201

Quality of life and sex-differences in a South-Eastern Nigerian stroke sample

BackgroundQuality of Life (QOL) studies in stroke among Africans are rather few and mainly from South-Western Nigeria. Hardly is there any from the other regions of this vast nation. Reports on gender influences on stroke survivors’ QOL have also been contradictory.ObjectivesThis study set out to provide preliminary data on the QOL of stroke survivors in South-Eastern Nigeria and also investigate sex-differences in the QOL.MethodsOne hundred and three volunteering stroke survivors (53 males, 50 females) were recruited from various settings. The Stroke-Specific Quality of Life (SS-QOL) scale was used to assess participants’ QOL. Participants mean QOL score in the overall and individual domains were presented as percentages of Maximum Possible Scores (MPS) while sex-differences across domains were investigated with Mann-Whitney U test statistics at 0.05 alpha level.ResultsParticipants mean scores in the vision (12.44 ± 3.56), thinking (11.50 ± 3.71), mood (18.55 ± 4.81) and language (19.04 ± 6.81) domains were above 70 percent of MPS while mean score in the social role (11.82 ± 4.75) was below 50% of MPS. Overall QOL score was slightly below 70% of the MPS. No significant sex-difference was found in all the SS-QOL domains (p<0.05).ConclusionsQOL seems to be affected, albeit not too severely, among stroke survivors from South-Eastern Nigeria. The effect is however similar for survivors of both gender. Social and family roles and physical functioning seem to be areas requiring keener clinicians’ attention.Key words: Stroke, Quality of life, Sex-differences, South-Eastern Nigeria