No abstract

Despite the vital role of paper publication and citation in higher education institutions (HEIs), literature on publication exercises is relatively scarce. There are a number of factors which influence the rate of university publications and citations. Accordingly, with a focus on policy perspectives, this paper discusses publication exercises by addressing the factors that can increase or decrease the rate of publication and citation in HEIs. The investigated zones are divided into two macro and micro levels, in which macro level deals with global policy and micro level is related to local and university policies. The effective factors and their relevant criteria are traced in all the aforementioned policies.

Purpose-This paper aims to focus on plagiarism and the consequences of anti-plagiarism services such as Turnitin.com, iThenticate, and PlagiarismDetect.com in detecting the most recent cheatings in academic and other writings. Design/methodology/approach-The most important approach is plagiarism prevention and finding proper solutions for detecting more complex kinds of plagiarism through natural language processing and artificial intelligence self-learning techniques. Findings-The research shows that most of the anti-plagiarism services can be cracked through different methods and artificial intelligence techniques can help to improve the performance of the detection procedure. Research limitations/implications-Accessing entire data and plagiarism algorithms is not possible completely, so comparing is just based on the outputs from detection services. They may produce different results on the same inputs. Practical implications-Academic papers and web pages are increasing over time, and it is very difficult to capture and compare documents with all available data on the network in an up to date manner. Originality/value-As many students and researchers use the plagiarism techniques (e.g. PDF locking, ghost-writers, dot replacement, online translators, previous works, fake bibliography) to cheat in academic writing, this paper is intended to prevent plagiarism and find suitable solutions for detecting more complex kinds of plagiarism. This should also be of grave concern to teachers and librarians to provide up to date/standard anti-plagiarism services. The paper proposes some new solutions to overcome these problems and to create more resilient and intelligent future systems.

This paper discusses plagiarism origins, and the ethical solutions to prevent it. It also reviews some unethical approaches, which may be used to decrease the plagiarism rate in academic writings. We propose eight ethical techniques to avoid unconscious and accidental plagiarism in manuscripts without using online systems such as Turnitin and/or iThenticate for cross checking and plagiarism detection. The efficiency of the proposed techniques is evaluated on five different texts using students individually. After application of the techniques on the texts, they were checked by Turnitin to produce the plagiarism and similarity report. At the end, the "effective factor" of each method has been compared with each other; and the best result went to a hybrid combination of all techniques to avoid plagiarism. The hybrid of ethical methods decreased the plagiarism rate reported by Turnitin from nearly 100% to the average of 8.4% on 5 manuscripts.

Emotions play an important role in human interactions. They can be integrated into the computer system to make human-computer interaction (HCI) more effective. Affective computing is an innovative computational modeling and detecting user's emotions to optimize system responses in HCI. However, there is a trade-off between recognition accuracy and real-time performance in some of the methods such as processing the facial expressions, human voice and body gestures. Other methods lack efficiency and usability in real-world applications such as natural language processing and electroencephalography signals. To accomplish a reliable, usable and high-performance system, this paper proposes an intelligent hybrid approach to recognize users' emotions by using easily accessible and low computational cost input devices including keyboard, mouse (touch pad: single touch) and touch screen display (single touch). Using the proposed approach, the system is developed and trained in a supervised mode by artificial neural network and support vector machine (SVM) techniques. The result shows an increase in accuracy of 6 % (93.20 %) by SVM in comparison with the currently existing methods. It is a significant contribution to show new directions of future research in emotion recognition, user modeling and emotional intelligence.