Building trust in agribusiness supply chains: A conceptual model of buyer-seller relationships in the seed potato industry in Asia

In the absence of a certified seed system, potato farmers in Asia must purchase replacement seed tubers from an informal seed system. With no third party assurance that the seed tubers purchased are of good quality, the farmer's decision to purchase seeds may be influenced by the long-standing relationships that have been established between buyers and sellers. Trust is the critical determinant of a good buyer-seller relationship. Through maintaining communication and the making of various relationship specific investments, a conceptual model is proposed which suggests that seed suppliers may engage in trust building behavior which should result in the preferred seed supplier enjoying a greater share of the farmer's patronage

Energy Efficiency Optimization by Spectral Efficiency Maximization in 5G Networks

Energy and spectral efficiency are the main challenges in 5th generation of mobile cellular networks. In this paper, we propose an optimization algorithm to optimize the energy efficiency by maximizing the spectral efficiency. Our simulation results show a significant increase in terms of spectral efficiency as well as energy efficiency whenever the mobile user is connected to a low power indoor base station. By applying the proposed algorithm, we show the network performance improvements up to 9 bit/s/Hz in spectral efficiency and 20 Gbit/Joule increase in energy efficiency for the mobile user served by the indoor base station rather than by the outdoor base station

The Challenges of Distance Learning in Countries Undergoing Transition During the COVID-19 Pandemic - Case Study

Virtual learning, also known as online learning or distance learning has transformed the face of the education system for quite some time. Now, it is rapidly becoming an integral aspect and a common tool, in the broader aspect of higher education, as a result of the COVID-19 pandemic. In addition to providing an alternative method of learning in the digital age, online learning offers students the opportunity to learn new skills or improve existing ones. On March 11, 2020, the World Health Organization declared COVID-19 a global pandemic. Following the speed with which COVID-19 spread to all parts of the world and to curb the spread of the disease, most governments around the world, including the Republic of Kosovo, authorized unprecedented social control measures to stop this disease unknown. These measures, among others, required social distancing and temporary physical closure of educational institutions. The first case of COVID 19 in Kosovo was identified on March 13, 2020, social distancing - full closure came into force on March 15, while UBT Higher Education Institution started online learning on March 16, 2020, the first in Kosovo and possibly in the Western Balkans. This teaching-learning process was a novelty for Kosovo and was applied for the first time. Objective: The main objective of the current survey was to study the impact of E-learning on students' academic performance and their evaluations of this form of teaching in general. The purpose of this paper is to reflect as professionally as possible the organization of distance learning, the effects on the teaching and learning process as well as the form and level of communication and teacher-student relations in this process which was a novelty for Kosovo and UBT as one of the largest Private Colleges in the region

From Conditional Random Field (CRF) to Rhetorical Structure Theory (RST): incorporating context information in sentiment analysis

This paper investigates a method based on Conditional Random Fields (CRFs) to incorporate sentence structure (syntax and semantics) and context information to identify sentiments of sentences. It also demonstrates the usefulness of the Rhetorical Structure Theory (RST) taking into consideration the discourse role of text segments. Thus, this paper’s aim is to reconsider the effectiveness of CRF and RST methods in incorporating the contextual information into Sentiment Analysis systems. Both methods are evaluated on two, different in size and genre of information sources, the Movie Review Dataset and the Finegrained Sentiment Dataset (FSD). Finally, we discuss the lessons learned from these experimental settings w.r.t. addressing the following key research questions such as whether there is an appropriate type of social media repository to incorporate contextual information, whether extending the pool of the selected features could improve context incorporation into SA systems and which is the best performing feature combination to achieve such improved performance

The NeuroSent System at ESWC-2018 Challenge on Semantic Sentiment Analysis

Multi-domain sentiment analysis consists in estimating the polarity of a given text by exploiting domain-specific information. One of the main issues common to the approaches discussed in the literature is their poor capabilities of being applied on domains which are different from those used for building the opinion model. In this paper, we will present an approach exploiting the linguistic overlap between domains to build sentiment models supporting polarity inference for documents belonging to every domain. Word embeddings together with a deep learning architecture have been implemented for enabling the building of multi-domain sentiment model. The proposed technique is validated by following the Dranziera protocol in order to ease the repeatability of the experiments and the comparison of the results. The outcomes demonstrate the effectiveness of the proposed approach and also set a plausible starting point for future work

The CLAUSY System at ESWC-2018 Challenge on Semantic Sentiment Analysis

With different social media and commercial platforms, users express their opinion about products in a textual form. Automatically extracting the polarity(i.e. whether the opinion is positive or negative) of a user can be useful for both actors: the online platform incorporating the feedback to improve their product as well as the client who might get recommendations according to his or her preferences. Different approaches for tackling the problem, have been suggested mainly using syntactic features. The “Challenge on Semantic Sentiment Analysis” aims to go beyond the word-level analysis by using semantic information. In this paper we propose a novel approach by employing the semantic information of grammatical unit called preposition. We try to derive the target of the review from the summary information, which serves as an input to identify the proposition in it. Our implementation relies on the hypothesis that the proposition expressing the target of the summary, usually containing the main polarity information

Opinion Mining with a Clause-Based Approach

With different social media and commercial platforms, users express their opinion about products in a textual form. Automatically extracting the polarity (i.e. whether the opinion is positive or negative) of a user can be useful for both actors: the online platform incorporating the feedback to improve their product as well as the client who might get recommendations according to his or her preferences. Different approaches for tackling the problem, have been suggested mainly using syntactic features. The “Challenge on Semantic Sentiment Analysis” aims to go beyond the word-level analysis by using semantic information. In this paper we propose a novel approach by employing the semantic information of grammatical unit called preposition. We try to derive the target of the review from the summary information, which serves as an input to identify the proposition in it. Our implementation relies on the hypothesis that the proposition expressing the target of the summary, usually containing the main polarity information

The FeatureSent System at ESWC-2018 Challenge on Semantic Sentiment Analysis

The approach described in this paper explores the use of semantic structured representation of sentences extracted from texts for multi-domain sentiment analysis purposes. The presented algorithm is built upon a domain-based supervised approach using index-like structured for representing information extracted from text. The algorithm extracts dependency parse relationships from the sentences containing in a training set. Then, such relationships are aggregated in a semantic structured together with either polarity and domain information. Such information is exploited in order to have a more fine-grained representation of the learned sentiment information. When the polarity of a new text has to be computed, such a text is converted in the same semantic representation that is used (i) for detecting the domain to which the text belongs to, and then (ii), once the domain is assigned to the text, the polarity is extracted from the index-like structure. First experiments performed by using the Blitzer dataset for training the system demonstrated the feasibility of the proposed approach

Aspect-Based Opinion Mining Using Knowledge Bases

In the last decade, the focus of the Opinion Mining field moved to detection of the pairs “aspect-polarity” instead of limiting approaches in the computation of the general polarity of a text. In this work, we propose an aspect-based opinion mining system based on the use of semantic resources for the extraction of the aspects from a text and for the computation of their polarities. The proposed system participated at the third edition of the Semantic Sentiment Analysis (SSA) challenge took place during ESWC 2017 achieving the runner-up place in the Task #2 concerning the aspect-based sentiment analysis. Moreover, a further evaluation performed on the SemEval 2015 benchmarks demonstrated the feasibility of the proposed approach